A Survey of Personality Computing
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paper_title: Does computer-synthesized speech manifest personality? experimental tests of recognition, similarity-attraction, and consistency-attraction

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Would people exhibit similarity-attraction and consistency-attraction toward unambiguously computergenerated speech even when personality is clearly not relevant? In Experiment 1, participants (extrovert or introvert) heard a synthesized voice (extrovert or introvert) on a book-buying Web site. Participants accurately recognized personality cues in text to speech and showed similarity-attraction in their evaluation of the computer voice, the book reviews, and the reviewer. Experiment 2, in a Web auction context, added personality of the text to the previous design. The results replicated Experiment 1 and demonstrated consistency (voice and text personality)-attraction. To maximize liking and trust, designers should set parameters, for example, words per minute or frequency range, that create a personality that is consistent with the user and the content being presented. The vast majority of content on computers and on the World Wide Web is textual. E-mail, documents, spreadsheets, presentation outlines, e-commerce sites, news and information sites, bulletin boards and chat rooms, advertisements, and search engines, although enhanced by graphical content, are all dominated by text. The prevalence of textual material is both a problem and an opportunity. The problem is that there are significant barriers to accessing textual content. Only half of U.S. homes have access to the Internet (The White House, 2000), and less than two-thirds have computers; these are the highest rates in the world (Samuelson, 1999, p. 52).
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paper_title: Users of the world, unite! The challenges and opportunities of Social Media

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The concept of Social Media is top of the agenda for many business executives today. Decision makers, as well as consultants, try to identify ways in which firms can make profitable use of applications such as Wikipedia, YouTube, Facebook, Second Life, and Twitter. Yet despite this interest, there seems to be very limited understanding of what the term ''Social Media'' exactly means; this article intends to provide some clarification. We begin by describing the concept of Social Media, and discuss how it differs from related concepts such as Web 2.0 and User Generated Content. Based on this definition, we then provide a classification of Social Media which groups applications currently subsumed under the generalized term into more specific categories by characteristic: collaborative projects, blogs, content communities, social networking sites, virtual game worlds, and virtual social worlds. Finally, we present 10 pieces of advice for companies which decide to utilize Social Media.
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paper_title: PERSONALITY AND THE PREDICTION OF CONSEQUENTIAL OUTCOMES

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Personality has consequences. Measures of personality have contemporaneous and predictive relations to a variety of important outcomes. Using the Big Five factors as heuristics for organizing the research literature, numerous consequential relations are identified. Personality dispositions are associated with happiness, physical and psychological health, spirituality, and identity at an individual level; associated with the quality of relationships with peers, family, and romantic others at an interpersonal level; and associated with occupational choice, satisfaction, and performance, as well as community involvement, criminal activity, and political ideology at a social institutional level.
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paper_title: Networked: The New Social Operating System

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Reviewed by Jonathan Bishop, Centre for Research into Online Communities and E-Learning Systems, European Parliament, Brussels, BelgiumNetworked: The New Social Operating SystemLee Raine and Barry Wellman©2012 by The MIT Press358 pp.$29.95ISBN 978-026-2017-19-0This book by Lee Rainie and Barry Wellman aims to show how the large, loosely knit social circles of networked individuals expand oppor-tunities for learning, problem solving, decision making, and personal interaction. It does this quite successfully, through providing a strong introduction to the authors’ research specialism of networked individualism. The purpose of the book appears to be to consolidate over 30 years’ of research on networked individualism into one volume, which makes it easier for people getting to grips with online social networking to understand this important yet abstract area. Even so, one might still ask whether it is worth buying a whole book dedicated to a concept with a very narrow research base beyond that of the authors. This review might provide the answer.The book is made up of 11 chapters split into three sections. Organisationally it is very well assembled. Each section has a chapter linking it to the next, which although helpful, raises the question whether this should have been an inbuilt feature. The authors discuss in the introduction whether they should have named the book ‘Networked Individualism’, saying that this would confuse readers who might not know what it means. But to anyone who has followed the authors’ extensive works on the subject previously, it would probably have made more sense to have done so, if only to differentiate it from the more technological books on the market implied in the title. The book follows an interesting and well docu-mented biographical account of the lives of online community moderators, Peter and Trudy Johnson-Lenz, who first appeared in Wellman’s work in the 1990s (Wellman & Gulia, 1999). This provides consistency in terms of making the examples given easy to relate to, yet one can’t help but feel that it is not in keeping with the era of “YOU” conceptualised by Time Magazine. With the very nature of networked individualism being about the joining of differ-ent people across different frontiers, first spoken about by David Kemp in his three-decades long study of electoral behaviour and social mobility in Australia (Kemp, 1978), most would have
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paper_title: Bridging the Gap between Social Animal and Unsocial Machine: A Survey of Social Signal Processing

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Social Signal Processing is the research domain aimed at bridging the social intelligence gap between humans and machines. This paper is the first survey of the domain that jointly considers its three major aspects, namely, modeling, analysis, and synthesis of social behavior. Modeling investigates laws and principles underlying social interaction, analysis explores approaches for automatic understanding of social exchanges recorded with different sensors, and synthesis studies techniques for the generation of social behavior via various forms of embodiment. For each of the above aspects, the paper includes an extensive survey of the literature, points to the most important publicly available resources, and outlines the most fundamental challenges ahead.
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paper_title: Smartphones An Emerging Tool for Social Scientists

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Recent developments in mobile technologies have produced a new kind of device: a programmable mobile phone, the smartphone. In this article, the authors argue that the technological and social char...
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paper_title: Spontaneous Inferences, Implicit Impressions, and Implicit Theories

paper_content:

People make social inferences without intentions, awareness, or effort, i.e., spontaneously. We review recent findings on spontaneous social inferences (especially traits, goals, and causes) and closely related phenomena. We then describe current thinking on some of the most relevant processes, implicit knowledge, and theories. These include automatic and controlled processes and their interplay; embodied cognition, including mimicry; and associative versus rule-based processes. Implicit knowledge includes adult folk theories, conditions of personhood, self-knowledge to simulate others, and cultural and social class differences. Implicit theories concern Bayesian networks, recent attribution research, and questions about the utility of the disposition-situation dichotomy. Developmental research provides new insights. Spontaneous social inferences include a growing array of phenomena, but they have been insufficiently linked to other phenomena and theories. We hope the links suggested in this review begin to remedy this.
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paper_title: Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German

paper_content:

Abstract To provide a measure of the Big Five for contexts in which participant time is severely limited, we abbreviated the Big Five Inventory (BFI-44) to a 10-item version, the BFI-10. To permit its use in cross-cultural research, the BFI-10 was developed simultaneously in several samples in both English and German. Results focus on the psychometric characteristics of the 2-item scales on the BFI-10, including their part-whole correlations with the BFI-44 scales, retest reliability, structural validity, convergent validity with the NEO-PI-R and its facets, and external validity using peer ratings. Overall, results indicate that the BFI-10 scales retain significant levels of reliability and validity. Thus, reducing the items of the BFI-44 to less than a fourth yielded effect sizes that were lower than those for the full BFI-44 but still sufficient for research settings with truly limited time constraints.
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paper_title: A contemplated revision of the NEO Five-Factor Inventory

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Abstract Previous item factor analyses and readability analyses suggested that 14 of the 60 items in the NEO Five-Factor Inventory might usefully be replaced. New analyses in high school ( N =1959) and adult ( N =1492) samples led to the selection of new items from the remaining pool of Revised NEO Personality Inventory items. The resulting scales showed modest improvements in reliability and factor structure, and equivalent validity. These new scales should be appropriate for most respondents age 14 and up. However, continued use of the current instrument is also reasonable for most applications.
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paper_title: A very brief measure of the Big-Five personality domains

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When time is limited, researchers may be faced with the choice of using an extremely brief measure of the Big-Five personality dimensions or using no measure at all. To meet the need for a very brief measure, 5 and 10-item inventories were developed and evaluated. Although somewhat inferior to standard multi-item instruments, the instruments reached adequate levels in terms of: (a) convergence with widely used Big-Five measures in self, observer, and peer reports, (b) test–retest reliability, (c) patterns of predicted external correlates, and (d) convergence between self and observer ratings. On the basis of these tests, a 10-item measure of the Big-Five dimensions is offered for situations where very short measures are needed, personality is not the primary topic of interest, or researchers can tolerate the somewhat diminished psychometric properties associated with very brief measures.
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paper_title: Domains and Facets: Hierarchical Personality Assessment Using the Revised NEO Personality Inventory

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Personality traits are organized hierarchically, with narrow, specific traits combining to define broad, global factors. The Revised NEO Personality Inventory (NEO-PI-R; Costa & McCrae, 1992c) assesses personality at both levels, with six specific facet scales in each of five broad domains. This article describes conceptual issues in specifying facets of a domain and reports evidence on the validity of NEO-PI-R facet scales. Facet analysis—the interpretation of a scale in terms of the specific facets with which it correlates—is illustrated using alternative measures of the five-factor model and occupational scales. Finally, the hierarchical interpretation of personality profiles is discussed. Interpretation on the domain level yields a rapid understanding of the individual interpretation of specific facet scales gives a more detailed assessment.
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paper_title: Dimensions of personality : 16, 5 or 3? ― Criteria for a taxonomic paradigm

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Abstract It is suggested that what is needed in personality research is a paradigm which would give rise to coordinated research work (“normal science”) directed in part at the elimination or clarification of anomalies. Several such paradigms have in fact been suggested, so that clearly what is needed is a set of criteria to decide between them. Several such criteria are put forward, and an attempt is made to apply them to three major systems, namely Cattell's 16 PF, the Norman “big 5”, and the Eysenck PEN system. It is suggested that if agreement could be reached on these criteria, we might be able to approach agreement on the substantive issue of what are the major dimensions of personality.
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paper_title: Methods of personality assessment

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[Extract] This chapter cannot provide an exhaustive review of the many approaches to personality assessment that are in common use because of the size of the area. With entire books devoted to individual tests, a chapter such as this is necessarily limited in its scope. In particular, the chapter will not address methods of projective personality assessment. Those interested in an introduction to such methods can consult the relevant chapters in Groth-Marnat (2003), and by Weiner (1997) and the following commentaries in the Journal of Personality Assessment for the use of the Rorschach. For a more critical perspective, readers can consult Hunsley, Lee and Wood (2003). The approach being taken is in part a focus on contrasting the multidimensional personality assessment instruments constructed using factor analysis by Raymond B. Cattell and his colleagues and those multidimensional scales developed using other approaches to assessing people, notably the construct-oriented methods advocated by Douglas Jackson. Despite its known limitations, the self-report questionnaire has become the dominant method for assessing personality. Even though other approaches to assessment remain in use, self-report instruments, whether administered on a computer screen and scored online (e.g., Drasgow and Olson-Buchanan 1999) or through a traditional answer sheet and question booklet or combined question and answer sheet, remain the dominant form of assessment. Their economy, apparent ease of use and interpretation, and freedom of the need for trained interviewers (or even third parties in some cases) provide advantages that often outweigh benefits of other approaches to assessment. Such other methods will be mentioned briefly, but the bulk of this chapter will be on self-report techniques.
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paper_title: Spontaneous Inferences, Implicit Impressions, and Implicit Theories

paper_content:

People make social inferences without intentions, awareness, or effort, i.e., spontaneously. We review recent findings on spontaneous social inferences (especially traits, goals, and causes) and closely related phenomena. We then describe current thinking on some of the most relevant processes, implicit knowledge, and theories. These include automatic and controlled processes and their interplay; embodied cognition, including mimicry; and associative versus rule-based processes. Implicit knowledge includes adult folk theories, conditions of personhood, self-knowledge to simulate others, and cultural and social class differences. Implicit theories concern Bayesian networks, recent attribution research, and questions about the utility of the disposition-situation dichotomy. Developmental research provides new insights. Spontaneous social inferences include a growing array of phenomena, but they have been insufficiently linked to other phenomena and theories. We hope the links suggested in this review begin to remedy this.
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paper_title: Predictors of Internet Use

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We examined audience uses o f the Internet from a uses-and-gratifications perspective. We expected contextual age, unwillingness to communicate, social presence, and Internet motives to predict outcomes of Internet exposure, affinity and satisfaction. The analyses identified five motives for using the Internet and multivariate links among the antecedents and motives. The results suggested distinctions between instrumental and ritualized Internet use, as well as Internet use serving as a functional alternative to face-to-face interaction.
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paper_title: Personality and self reported mobile phone use

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As the mobile phone supports interpersonal interaction, mobile phone use might be a function of personality. This study sought to predict amounts and types of mobile phone use from extraversion, agreeableness, conscientiousness, neuroticism and self-esteem. One hundred and twelve mobile phone owners reported on their use of their mobile phones, and completed the NEO-FFI and the Coopersmith self-esteem inventory. Extraverts reported spending more time calling, and changing ring tone and wallpaper, implying the use of the mobile phone as a means of stimulation. Extraverts and perhaps disagreeable individuals were less likely to value incoming calls. Disagreeable extraverts also reported using the mobile phone more, and spent more time adjusting ringtone/wallpaper. The neurotic, disagreeable, unconscientious and extroverted spent more time messaging using SMS. This study concludes that psychological theory can explain patterns of mobile phone use.
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paper_title: Facebook and Privacy: The Balancing Act of Personality, Gender, and Relationship Currency

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Social media profiles are telling examples of the everyday need for disclosure and concealment. The balance between concealment and disclosure varies across individuals, and personality traits might partly explain this variability. Experimental findings on the relationship between information disclosure and personality have been so far inconsistent. We thus study this relationship anew with 1,313 Facebook users in the United States using two personality tests: the big five personality test and the self-monitoring test. We model the process of information disclosure in a principled way using Item Response Theory and correlate the resulting user disclosure scores with personality traits. We find a correlation with the trait of Openness and observe gender effects, in that, men and women share equal amount of private information, but men tend to make it more publicly available, well beyond their social circles. Interestingly, geographic (e.g., residence, hometown) and work-related information is used as relationship currency, in that, it is selectively shared with social contacts and is rarely shared with the Facebook community at large.
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paper_title: Who blogs? Personality predictors of blogging

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The Big Five personality inventory measures personality based on five key traits: neuroticism, extraversion, agreeableness, openness to experience, and conscientiousness Costa, P. T., Jr., & McCrae, R. R. (1992). Normal personality assessment in clinical practice: The NEO Personality Inventory. Psychological Assessment 4, 5-13]. There is a growing body of evidence indicating that individual differences on the Big Five factors are associated with different types of Internet usage Amichai-Hamburger, Y., & Ben-Artzi, E. (2003). Loneliness and Internet use. Computers in Human Behavior 19, 71-80; Hamburger, Y. A., & Ben-Artzi, E. (2000). Relationship between extraversion and neuroticism and the different uses of the Internet. Computers in Human Behavior 16, 441-449]. Two studies sought to extend this research to a relatively new online format for expression: blogging. Specifically, we examined whether the different Big Five traits predicted blogging. The results of two studies indicate that people who are high in openness to new experience and high in neuroticism are likely to be bloggers. Additionally, the neuroticism relationship was moderated by gender indicating that women who are high in neuroticism are more likely to be bloggers as compared to those low in neuroticism whereas there was no difference for men. These results indicate that personality factors impact the likelihood of being a blogger and have implications for understanding who blogs.
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paper_title: You are what you tweet: Personality expression and perception on Twitter

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Microblogging services such as Twitter have become increasingly popular in recent years. However, little is known about how personality is manifested and perceived in microblogs. In this study, we measured the Big Five personality traits of 142 participants and collected their tweets over a 1-month period. Extraversion, agreeableness, openness, and neuroticism were associated with specific linguistic markers, suggesting that personality manifests in microblogs. Meanwhile, eight observers rated the participants’ personality on the basis of their tweets. Results showed that observers relied on specific linguistic cues when making judgments, and could only judge agreeableness and neuroticism accurately. This study provides new empirical evidence of personality expression in naturalistic settings, and points to the potential of utilizing social media for personality research.
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paper_title: Personality, motivation and video games

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This study explored relationships between personality, video game preference and gaming experiences. Two hundred and thirty-five participants completed an online survey in which they recalled a recent gaming experience, and provided measures of personality and their gaming experience via the Player Experience of Need Satisfaction (PENS) measure. Relationships between game genre, personality and gaming experience were found. Results are interpreted with reference to the validity of the PENS, current models of video gaming motivations and enjoyment, and sub-groups of people that may be more vulnerable to possible negative effects of games.
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paper_title: Personality inference from voice quality: The loud voice of extroversion.

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Investigated the process of personality inference from voice quality using 24 male American stimulus persons who served as subjects in simulated jury discussions. Applying a Brunswikian lens model of the inference process, criteria, distal cues, proximal cues and attributions were measured by independent groups of judges: personality criteria by three peers of each stimulus person and, on the basis of content-masked voice samples, distal voice quality indicator cues by six phoneticians, proximal voice percept by ten naive judges, personality attributions by nine naive judges. Only extroversion attributions correlate significantly with the criterion, replicating earlier findings. For the inference of extroversion, contrary to other traits which apparently cannot be inferred accurately from voice quality, the following conditions are met: (a) the criterion is associated with ecologically valid voice energy cues (vocal effort and dynamic range), (b) these indicator cues are adequately represented as proximal voice percepts (particularly loudness and sharpness), and(c) percept utilization in the judges' inferential strategy corresponds to the association between criterion and distal indicator cues. Path-analytic procedures are used to test empirically the adequacy of the inference model to (a) account for the variance in the attributions, and (b) explain significant correlations between criteria and attributions in terms of mediating variables.
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paper_title: Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German

paper_content:

Abstract To provide a measure of the Big Five for contexts in which participant time is severely limited, we abbreviated the Big Five Inventory (BFI-44) to a 10-item version, the BFI-10. To permit its use in cross-cultural research, the BFI-10 was developed simultaneously in several samples in both English and German. Results focus on the psychometric characteristics of the 2-item scales on the BFI-10, including their part-whole correlations with the BFI-44 scales, retest reliability, structural validity, convergent validity with the NEO-PI-R and its facets, and external validity using peer ratings. Overall, results indicate that the BFI-10 scales retain significant levels of reliability and validity. Thus, reducing the items of the BFI-44 to less than a fourth yielded effect sizes that were lower than those for the full BFI-44 but still sufficient for research settings with truly limited time constraints.
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paper_title: The media equation: how people treat computers, television, and new media like real people and places

paper_content:

Part I. Introduction: 1. The media equation Part II. Media and Manners: 2. Politeness 3. Interpersonal distance 4. Flattery 5. Judging others and ourselves Part III. Media and Personality: 6. Personality of characters 7. Personality of interfaces 8. Imitating a personality Part IV. Media and emotion: 9. Good versus bad 10. Negativity 11. Arousal Part V. Media and Social Roles: 12. Specialists 13. Teammates 14. Gender 15. Voices 16. Source orientation Part VI. Media and Form: 17. Image size 18. Fidelity 19. Synchrony 20. Motion 21. Scene changes 22. Subliminal images Part VII. Final Words: 23. Conclusions about the media equation References.
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paper_title: Spontaneous Inferences, Implicit Impressions, and Implicit Theories

paper_content:

People make social inferences without intentions, awareness, or effort, i.e., spontaneously. We review recent findings on spontaneous social inferences (especially traits, goals, and causes) and closely related phenomena. We then describe current thinking on some of the most relevant processes, implicit knowledge, and theories. These include automatic and controlled processes and their interplay; embodied cognition, including mimicry; and associative versus rule-based processes. Implicit knowledge includes adult folk theories, conditions of personhood, self-knowledge to simulate others, and cultural and social class differences. Implicit theories concern Bayesian networks, recent attribution research, and questions about the utility of the disposition-situation dichotomy. Developmental research provides new insights. Spontaneous social inferences include a growing array of phenomena, but they have been insufficiently linked to other phenomena and theories. We hope the links suggested in this review begin to remedy this.
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paper_title: The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods

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We are in the midst of a technological revolution whereby, for the first time, researchers can link daily word use to a broad array of real-world behaviors. This article reviews several computerized text analysis methods and describes how Linguistic Inquiry and Word Count (LIWC) was created and validated. LIWC is a transparent text analysis program that counts words in psychologically meaningful categories. Empirical results using LIWC demonstrate its ability to detect meaning in a wide variety of experimental settings, including to show attentional focus, emotionality, social relationships, thinking styles, and individual differences.
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paper_title: What Are They Blogging About? Personality, Topic and Motivation in Blogs.

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Personal weblogs (blogs), provide individuals with the opportunity to write freely and express themselves online in the presence of others. In such situations, what do bloggers write about, and what are their motivations for blogging? Using a large blog corpus annotated with the LIWC text analysis program, we examine the content of blogs to provide insight into the role of personality in motivation for blogging. As predicted, we find that highly Neurotic authors use blogs to serve a cathartic or auto-therapeutic function, and reflect mainly upon themselves and negative emotions. Highly Extraverted blog authors, as expected, use blogs to document their lives at a high level, and uniquely interact directly with the reader. Additionally Extraverts use blogs to vent both positive and negative emotions. Bloggers who are high scorers on the Openness trait are more concerned with leisure activities, although they are more evaluative than intellectual, whereas high Conscientiousness bloggers tend to report daily life – and work – around them. As in other contexts, expressing positive rather than negative emotions is associated with high Agreeableness, but that trait is associated with self reference to a greater degree in blogs than elsewhere. In general, findings are consistent with other contexts indicating that bloggers tend to adapt to the possibilities of the medium, rather than try to present themselves differently.
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paper_title: Personality in its natural habitat: manifestations and implicit folk theories of personality in daily life.

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To examine the expression of personality in its natural habitat, the authors tracked 96 participants over 2 days using the Electronically Activated Recorder (EAR), which samples snippets of ambient sounds in participants' immediate environments. Participants' Big Five scores were correlated with EAR-derived information on their daily social interactions, locations, activities, moods, and language use; these quotidian manifestations were generally consistent with the trait definitions and (except for Openness) often gender specific. To identify implicit folk theories about daily manifestations of personality, the authors correlated the EAR-derived information with impressions of participants based on their EAR sounds; judges' implicit folk theories were generally accurate (especially for Extraversion) and also partially gender specific. The findings point to the importance of naturalistic observation studies on how personality is expressed and perceived in the natural stream of everyday behavior.
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paper_title: Blog tells what kind of personality you have: egogram estimation from Japanese weblog

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In this paper, we investigate personality estimation from Japanese weblog text. Among various personality types, we focus on Egogram, which has been used in Transactional Analysis and is strongly related to the communicative behavior of individuals. Estimation is performed using the Multinomial Naive Bayes classifier with some feature words that are selected based on the information gain. The validity of this approach was evaluated with real weblog text of 551 subjects. The results show that our approach achieved 12-25% improvement from baseline. The feature words selected for the estimation are strongly correlated with the characteristics of Egogram.
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paper_title: Personality estimation based on weblog text classification

paper_content:

This paper describes the investigation of personality estimation from Japanese weblog text. Among various personality types, we focus on Egogram, which has been used in Transactional Analysis and is strongly related to the communicative behavior of individuals. Estimation is performed using the Multinomial Naive Bayes classifier with some feature words that are selected based on the information gain. The validity of this approach was evaluated with real weblog text of 551 subjects. The results showed that our approach achieved 12- 25% improvement from baseline. The feature words selected for the estimation are strongly correlated with the characteristics of Egogram.
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paper_title: Using linguistic cues for the automatic recognition of personality in conversation and text

paper_content:

It is well known that utterances convey a great deal of information about the speaker in addition to their semantic content. One such type of information consists of cues to the speaker's personality traits, the most fundamental dimension of variation between humans. Recent work explores the automatic detection of other types of pragmatic variation in text and conversation, such as emotion, deception, speaker charisma, dominance, point of view, subjectivity, opinion and sentiment. Personality affects these other aspects of linguistic production, and thus personality recognition may be useful for these tasks, in addition to many other potential applications. However, to date, there is little work on the automatic recognition of personality traits. This article reports experimental results for recognition of all Big Five personality traits, in both conversation and text, utilising both self and observer ratings of personality. While other work reports classification results, we experiment with classification, regression and ranking models. For each model, we analyse the effect of different feature sets on accuracy. Results show that for some traits, any type of statistical model performs significantly better than the baseline, but ranking models perform best overall. We also present an experiment suggesting that ranking models are more accurate than multi-class classifiers for modelling personality. In addition, recognition models trained on observed personality perform better than models trained using self-reports, and the optimal feature set depends on the personality trait. A qualitative analysis of the learned models confirms previous findings linking language and personality, while revealing many new linguistic markers.
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paper_title: Predicting personality with social media

paper_content:

Social media is a place where users present themselves to the world, revealing personal details and insights into their lives. We are beginning to understand how some of this information can be utilized to improve the users' experiences with interfaces and with one another. In this paper, we are interested in the personality of users. Personality has been shown to be relevant to many types of interactions; it has been shown to be useful in predicting job satisfaction, professional and romantic relationship success, and even preference for different interfaces. Until now, to accurately gauge users' personalities, they needed to take a personality test. This made it impractical to use personality analysis in many social media domains. In this paper, we present a method by which a user's personality can be accurately predicted through the publicly available information on their Facebook profile. We will describe the type of data collected, our methods of analysis, and the results of predicting personality traits through machine learning. We then discuss the implications this has for social media design, interface design, and broader domains.
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paper_title: Whose Thumb Is It Anyway? Classifying Author Personality From Weblog Text

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We report initial results on the relatively novel task of automatic classification of author personality. Using a corpus of personal weblogs, or 'blogs', we investigate the accuracy that can be achieved when classifying authors on four important personality traits. We explore both binary and multiple classification, using differing sets of n-gram features. Results are promising for all four traits examined.
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paper_title: Multimodal recognition of personality traits in human-computer collaborative tasks

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The user's personality in Human-Computer Interaction (HCI) plays an important role for the overall success of the interaction. The present study focuses on automatically recognizing the Big Five personality traits from 2-5 min long videos, in which the computer interacts using different levels of collaboration, in order to elicit the manifestation of these personality traits. Emotional Stability and Extraversion are the easiest traits to automatically detect under the different collaborative settings: all the settings for Emotional Stability and intermediate and fully-non collaborative settings for Extraversion. Interestingly, Agreeableness and Conscientiousness can be detected only under a moderately non-collaborative setting. Finally, our task does not seem to activate the full range of dispositions for Creativity.
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paper_title: Recognition of Personality Traits from Human Spoken Conversations

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We are interested in understanding human personality and its manifestations in human interactions. The automatic analysis of such personality traits in natural conversation is quite complex due to the user-profiled corpora acquisition, annotation task and multidimensional modeling. While in the experimental psychology research this topic has been addressed extensively, speech and language scientists have recently engaged in limited experiments. In this paper we describe an automated system for speaker-independent personality prediction in the context of human-human spoken conversations. The evaluation of such system is carried out on the PersIA human-human spoken dialog corpus annotated with user self-assessments of the Big-Five personality traits. The personality predictor has been trained on paralinguistic features and its evaluation on five personality traits shows encouraging results for the conscientiousness and extroversion labels. Index Terms: Automated personality prediction from speech, human–human dialog analysis.
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paper_title: Using linguistic cues for the automatic recognition of personality in conversation and text

paper_content:

It is well known that utterances convey a great deal of information about the speaker in addition to their semantic content. One such type of information consists of cues to the speaker's personality traits, the most fundamental dimension of variation between humans. Recent work explores the automatic detection of other types of pragmatic variation in text and conversation, such as emotion, deception, speaker charisma, dominance, point of view, subjectivity, opinion and sentiment. Personality affects these other aspects of linguistic production, and thus personality recognition may be useful for these tasks, in addition to many other potential applications. However, to date, there is little work on the automatic recognition of personality traits. This article reports experimental results for recognition of all Big Five personality traits, in both conversation and text, utilising both self and observer ratings of personality. While other work reports classification results, we experiment with classification, regression and ranking models. For each model, we analyse the effect of different feature sets on accuracy. Results show that for some traits, any type of statistical model performs significantly better than the baseline, but ranking models perform best overall. We also present an experiment suggesting that ranking models are more accurate than multi-class classifiers for modelling personality. In addition, recognition models trained on observed personality perform better than models trained using self-reports, and the optimal feature set depends on the personality trait. A qualitative analysis of the learned models confirms previous findings linking language and personality, while revealing many new linguistic markers.
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paper_title: Multimodal recognition of personality traits in social interactions

paper_content:

This paper targets the automatic detection of personality traits in a meeting environment by means of audio and visual features; information about the relational context is captured by means of acoustic features designed to that purpose. Two personality traits are considered: Extraversion (from the Big Five) and the Locus of Control. The classification task is applied to thin slices of behaviour, in the form of 1-minute sequences. SVM were used to test the performances of several training and testing instance setups, including a restricted set of audio features obtained through feature selection. The outcomes improve considerably over existing results, provide evidence about the feasibility of the multimodal analysis of personality, the role of social context, and pave the way to further studies addressing different features setups and/or targeting different personality traits.
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paper_title: Please, tell me about yourself: automatic personality assessment using short self-presentations

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Personality plays an important role in the way people manage the images they convey in self-presentations and employment interviews, trying to affect the other"s first impressions and increase effectiveness. This paper addresses the automatically detection of the Big Five personality traits from short (30-120 seconds) self-presentations, by investigating the effectiveness of 29 simple acoustic and visual non-verbal features. Our results show that Conscientiousness and Emotional Stability/Neuroticism are the best recognizable traits. The lower accuracy levels for Extraversion and Agreeableness are explained through the interaction between situational characteristics and the differential activation of the behavioral dispositions underlying those traits.
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paper_title: Relative importance of face, body, and speech in judgments of personality and affect.

paper_content:

Three experiments correlated judgments made from observing single channels (face, body, or speech) with multiple channel judgments (face, body, and speech together; or face and speech together). Judges observed the spontaneous behavior of videotaped student nurses in 2 types of interview situations, "deceptive" and "honest," and rated the nurses on 14 bipolar adjective scales (e.g., awkward–natural). The single channel judgments that correlated most highly with the multiple channel judgments depended on the type of attribute being judged and the situation in which the behavior occurred (e.g., in the deception condition, judgments made from speech had the highest correlation with whole-person judgments). (18 ref) (PsycINFO Database Record (c) 2016 APA, all rights reserved)
---
paper_title: A multimodal annotated corpus of consensus decision making meetings

paper_content:

In this paper we present an annotated audio–video corpus of multi-party meetings. The multimodal corpus provides for each subject involved in the experimental sessions six annotation dimensions referring to group dynamics; speech activity and body activity. The corpus is based on 11 audio and video recorded sessions which took place in a lab setting appropriately equipped with cameras and microphones. Our main concern in collecting this multimodal corpus was to explore the possibility of providing feedback services to facilitate group processes and to enhance self awareness among small groups engaged in meetings. We therefore introduce a coding scheme for annotating relevant functional roles that appear in a small group interaction. We also discuss the reliability of the coding scheme and we present the first results for automatic classification.
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paper_title: Opensmile: the munich versatile and fast open-source audio feature extractor

paper_content:

We introduce the openSMILE feature extraction toolkit, which unites feature extraction algorithms from the speech processing and the Music Information Retrieval communities. Audio low-level descriptors such as CHROMA and CENS features, loudness, Mel-frequency cepstral coefficients, perceptual linear predictive cepstral coefficients, linear predictive coefficients, line spectral frequencies, fundamental frequency, and formant frequencies are supported. Delta regression and various statistical functionals can be applied to the low-level descriptors. openSMILE is implemented in C++ with no third-party dependencies for the core functionality. It is fast, runs on Unix and Windows platforms, and has a modular, component based architecture which makes extensions via plug-ins easy. It supports on-line incremental processing for all implemented features as well as off-line and batch processing. Numeric compatibility with future versions is ensured by means of unit tests. openSMILE can be downloaded from http://opensmile.sourceforge.net/.
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paper_title: Spontaneous Inferences, Implicit Impressions, and Implicit Theories

paper_content:

People make social inferences without intentions, awareness, or effort, i.e., spontaneously. We review recent findings on spontaneous social inferences (especially traits, goals, and causes) and closely related phenomena. We then describe current thinking on some of the most relevant processes, implicit knowledge, and theories. These include automatic and controlled processes and their interplay; embodied cognition, including mimicry; and associative versus rule-based processes. Implicit knowledge includes adult folk theories, conditions of personhood, self-knowledge to simulate others, and cultural and social class differences. Implicit theories concern Bayesian networks, recent attribution research, and questions about the utility of the disposition-situation dichotomy. Developmental research provides new insights. Spontaneous social inferences include a growing array of phenomena, but they have been insufficiently linked to other phenomena and theories. We hope the links suggested in this review begin to remedy this.
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paper_title: Space speaks: towards socially and personality aware visual surveillance

paper_content:

There is a complex unwritten code which regulates human interactions. In this paper we present a camera based monitoring system that explores the relationship between proxemics, visual attention and personality traits during interaction. People's relative positions an head poses are extracted with a multi-target tracking algorithm and used to (i) estimate, under the 'thin slices' hypothesis, the level of extroversion and neuroticism of a person, to (ii) learn a model of people interactive behavior that improves, once integrated in the algorithm, the accuracy of the tracking estimates, and to (iii) steer a set of active cameras to the subject found to exhibit the most peculiar interaction pattern. We report on experimental results in a natural scenario where people engage in a party
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paper_title: Big-Five Personality Prediction Based on User Behaviors at Social Network Sites

paper_content:

Many customer services are already available at Social Network Sites (SNSs), including user recommendation and media interaction, to name a few. There are strong desires to provide online users more dedicated and personalized services that fit into individual's need, usually strongly depending on the inner personalities of the user. However, little has been done to conduct proper psychological analysis, crucial for explaining the user's outer behaviors from their inner personality. In this paper, we propose an approach that intends to facilitate this line of research by directly predicting the so called Big-Five Personality from user's SNS behaviors. Comparing to the conventional inventory-based psychological analysis, we demonstrate via experimental studies that users' personalities can be predicted with reasonable precision based on their online behaviors. Except for proving some former behavior-personality correlation results, our experiments show that extraversion is positively related to one's status republishing proportion and neuroticism is positively related to the proportion of one's angry blogs (blogs making people angry).
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paper_title: Towards Discovery of Influence and Personality Traits Through Social Link Prediction

paper_content:

Estimation of a person’s influence and personality traits from social media data has many applications. We use social linkage criteria, such as number of followers and friends, as proxies to form corpora, from popular blogging site Livejournal, for examining two two-class classification problems: influential vs. non-influential, and extraversion vs. introversion. Classification is performed using automatically-derived psycholinguistic and mood-based features of a user’s textual messages. We experiment with three sub-corpora of 10000 users each, and present the most effective predictors for each category. The best classification result, at 80%, is achieved using psycholinguistic features; e.g., influentials are found to use more complex language, than non-influentials, and use more leisure-related terms.
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paper_title: Workshop on Computational Personality Recognition: Shared Task

paper_content:

In the Workshop on Computational Personality Recognition (Shared Task), we released two datasets, varying in size and genre, annotated with gold standard personality labels. This allowed participants to evaluate features and learning techniques, and even to compare the performances of their systems for personality recognition on a common benchmark. We had 8 participants to the task. In this paper we discuss the results and compare them to previous literature.
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paper_title: Predicting Personality from Twitter

paper_content:

Social media is a place where users present themselves to the world, revealing personal details and insights into their lives. We are beginning to understand how some of this information can be utilized to improve the users' experiences with interfaces and with one another. In this paper, we are interested in the personality of users. Personality has been shown to be relevant to many types of interactions, it has been shown to be useful in predicting job satisfaction, professional and romantic relationship success, and even preference for different interfaces. Until now, to accurately gauge users' personalities, they needed to take a personality test. This made it impractical to use personality analysis in many social media domains. In this paper, we present a method by which a user's personality can be accurately predicted through the publicly available information on their Twitter profile. We will describe the type of data collected, our methods of analysis, and the machine learning techniques that allow us to successfully predict personality. We then discuss the implications this has for social media design, interface design, and broader domains.
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paper_title: Unsupervised Personality Recognition for Social Network Sites

paper_content:

In this paper, we present a system for personality recognition that exploits linguistic cues and does not require supervision for evaluation. We run the system on a dataset sampled from a popular Social Network: FriendFeed. We adopted the five classes from the standard model known in psychology as the "Big Five": extraversion, emotional stability, agreeableness, conscientiousness and openness to experience. Making use of the linguistic features associated with those classes the system generates one personality model for each user. The system then evaluates the models by comparing all the posts of one single user (users that have only one post are discarded). As evaluation measures the system provides accuracy (measure of the reliability of the personality model) and validity (measure of the variability of writing style of a user). The analysis of a sample of 748 Italian users of FriendFeed showed that that the most frequent personality type is represented by the model of an extravert, insecure, agreeable, organized and unimaginative person.
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paper_title: Unveiling the multimedia unconscious: implicit cognitive processes and multimedia content analysis

paper_content:

One of the main findings of cognitive sciences is that automatic processes of which we are unaware shape, to a significant extent, our perception of the environment. The phenomenon applies not only to the real world, but also to multimedia data we consume every day. Whenever we look at pictures, watch a video or listen to audio recordings, our conscious attention efforts focus on the observable content, but our cognition spontaneously perceives intentions, beliefs, values, attitudes and other constructs that, while being outside of our conscious awareness, still shape our reactions and behavior. So far, multimedia technologies have neglected such a phenomenon to a large extent. This paper argues that taking into account cognitive effects is possible and it can also improve multimedia approaches. As a supporting proof-of-concept, the paper shows not only that there are visual patterns correlated with the personality traits of 300 Flickr users to a statistically significant extent, but also that the personality traits (both self-assessed and attributed by others) of those users can be inferred from the images these latter post as "favourite".
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paper_title: Predicting personality with social media

paper_content:

Social media is a place where users present themselves to the world, revealing personal details and insights into their lives. We are beginning to understand how some of this information can be utilized to improve the users' experiences with interfaces and with one another. In this paper, we are interested in the personality of users. Personality has been shown to be relevant to many types of interactions; it has been shown to be useful in predicting job satisfaction, professional and romantic relationship success, and even preference for different interfaces. Until now, to accurately gauge users' personalities, they needed to take a personality test. This made it impractical to use personality analysis in many social media domains. In this paper, we present a method by which a user's personality can be accurately predicted through the publicly available information on their Facebook profile. We will describe the type of data collected, our methods of analysis, and the results of predicting personality traits through machine learning. We then discuss the implications this has for social media design, interface design, and broader domains.
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paper_title: Our Twitter Profiles, Our Selves: Predicting Personality with Twitter

paper_content:

Psychological personality has been shown to affect a variety of aspects: preferences for interaction styles in the digital world and for music genres, for example. Consequently, the design of personalized user interfaces and music recommender systems might benefit from understanding the relationship between personality and use of social media. Since there has not been a study between personality and use of Twitter at large, we set out to analyze the relationship between personality and different types of Twitter users, including popular users and influentials. For 335 users, we gather personality data, analyze it, and find that both popular users and influentials are extroverts and emotionally stable (low in the trait of Neuroticism). Interestingly, we also find that popular users are `imaginative' (high in Openness), while influentials tend to be `organized' (high in Conscientiousness). We then show a way of accurately predicting a user's personality simply based on three counts publicly available on profiles: following, followers, and listed counts. Knowing these three quantities about an active user, one can predict the user's five personality traits with a root-mean-squared error below 0.88 on a $[1,5]$ scale. Based on these promising results, we argue that being able to predict user personality goes well beyond our initial goal of informing the design of new personalized applications as it, for example, expands current studies on privacy in social media.
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paper_title: Who's Who with Big-Five: Analyzing and Classifying Personality Traits with Smartphones

paper_content:

In this paper, we investigate the relationship between behavioral characteristics derived from rich smart phone data and self-reported personality traits. Our data stems from smart phones of a set of 83 individuals collected over a continuous period of 8 months. From the analysis, we show that aggregated features obtained from smart phone usage data can be indicators of the Big-Five personality traits. Additionally, we develop an automatic method to infer the personality type of a user based on cell phone usage using supervised learning. We show that our method performs significantly above chance and up to 75.9% accuracy. To our knowledge, this constitutes the first study on the analysis and classification of personality traits using smartphone data.
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paper_title: Capturing Individual and Group Behavior with Wearable Sensors

paper_content:

We show how to obtain high level descriptions of human behavior in terms of physical activity, speech activity, face-to-face interaction (f2f), physical proximity, and social network attributes from sensor data. We present experimental results that showthat it is possible to identify individual personality traits as well as subjective and objective group performance metrics from low level data collected using wearable sensors.
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paper_title: Smartphones An Emerging Tool for Social Scientists

paper_content:

Recent developments in mobile technologies have produced a new kind of device: a programmable mobile phone, the smartphone. In this article, the authors argue that the technological and social char...
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paper_title: Friends don't lie: inferring personality traits from social network structure

paper_content:

In this work, we investigate the relationships between social network structure and personality; we assess the performances of different subsets of structural network features, and in particular those concerned with ego-networks, in predicting the Big-5 personality traits. In addition to traditional survey-based data, this work focuses on social networks derived from real-life data gathered through smartphones. Besides showing that the latter are superior to the former for the task at hand, our results provide a fine-grained analysis of the contribution the various feature sets are able to provide to personality classification, along with an assessment of the relative merits of the various networks exploited.
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paper_title: A study on personality identification using game based theory

paper_content:

Game Based Personality Profiling Application had been develop as an application to solve the problems faced by a counselor in capturing and determine personality. There are many types of personality model and theory such as Jung's Sixteen personality, Myers Briggs Types Indicators (MBTI), 5 Big factors, and Kathrine Benziger's Personality. This application uses MBTI based concept as a guideline to capture sixteen types of personality. The game genre is puzzle game, and it was developed using Netbeans tools and object-oriented technique. The client or the player will be tested by four mini games in this application so that the game Artifact Intelligent (AI) will capture the personality of the player. The system was tested using 50 samples which were chosen randomly and 77.5% of the results are based on engineering student. The result obtain was compared with the personality psychometric test paper based.
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paper_title: Introverted elves & conscientious gnomes: the expression of personality in world of warcraft

paper_content:

Personality inference can be used for dynamic personalization of content or system customization. In this study, we examined whether and how personality is expressed in Virtual Worlds (VWs). Survey data from 1,040 World of Warcraft players containing demographic and personality variables was paired with their VW behavioral metrics over a four-month period. Many behavioral cues in VWs were found to be related to personality. For example, Extraverts prefer group activities over solo activities. We also found that these behavioral indicators can be used to infer a player's personality.
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paper_title: Personality Traits and Life Satisfaction among Online Game Players

paper_content:

ABSTRACT The DFC Intelligence predicts worldwide online game revenues will reach $9.8 billion by 2009, making online gaming a mainstream recreational activity. Understanding online game player personality traits is therefore important. This study researches the relationship between personality traits and life satisfaction in online game players. Taipei, Taiwan, is the study location, with questionnaire surveys conducted in cyber cafe shops. Multiple regression analysis studies the causal relationship between personality traits and life satisfaction in online game players. The result shows that neuroticism has significant negative influence on life satisfaction. Both openness and conscientiousness have significant positive influence on life satisfaction. Finally, implications for leisure practice and further research are discussed.
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paper_title: From speech to personality: mapping voice quality and intonation into personality differences

paper_content:

From a cognitive point of view, personality perception corresponds to capturing individual differences and can be thought of as positioning the people around us in an ideal personality space. The more similar the personality of two individuals, the closer their position in the space. This work shows that the mutual position of two individuals in the personality space can be inferred from prosodic features. The experiments, based on ordinal regression techniques, have been performed over a corpus of 640 speech samples comprising 322 individuals assessed in terms of personality traits by 11 human judges, which is the largest database of this type in the literature. The results show that the mutual position of two individuals can be predicted with up to 80% accuracy.
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paper_title: Annotation and Recognition of Personality Traits in Spoken Conversations from the AMI Meetings Corpus

paper_content:

Recognition of personality traits is a well studied problem in psychology while only recently it has been addressed by speech and language technology research. This paper describes annotation and experiments towards automatically inferring speakers personality traits in spontaneous conversations. In the first part, the work describes the annotation framework based on the Big-Five personality traits model (Extraversion, Agreeableness, Conscientiousness, Neuroticism and Openness) applied to 128 speakers from the AMI corpus. As the corpus contains rich annotations, those data can generalize previous studies based on enacted speech or dialogues. In the second part, the paper describes experiments based on various features including prosody, words n-gram, dialog acts and speech activity. Results reveal that high/low extraversion, consciousness and neuroticism traits can be automatically recognized with accuracy rate of 74.5%, 67.6% and 68.7%, respectively, while agreeableness and openness classification error rates are not statistically better than chance. Non-linguistic features (prosody, speech activity, overlaps and interruptions) outperform linguistic features (words n-gram and dialog acts) in this setup.
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paper_title: On Speaker-Independent Personality Perception and Prediction from Speech

paper_content:

Keywords: extra-linguistic speech properties ; personality modeling from speech ; speaker characteristics Reference EPFL-CONF-178309 Record created on 2012-06-19, modified on 2017-05-10
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paper_title: Automatic Personality Perception: Prediction of Trait Attribution Based on Prosodic Features

paper_content:

Whenever we listen to a voice for the first time, we attribute personality traits to the speaker. The process takes place in a few seconds and it is spontaneous and unaware. While the process is not necessarily accurate (attributed traits do not necessarily correspond to the actual traits of the speaker), still it significantly influences our behavior toward others, especially when it comes to social interaction. This paper proposes an approach for the automatic prediction of the traits the listeners attribute to a speaker they never heard before. The experiments are performed over a corpus of 640 speech clips (322 identities in total) annotated in terms of personality traits by 11 assessors. The results show that it is possible to predict with high accuracy (more than 70 percent depending on the particular trait) whether a person is perceived to be in the upper or lower part of the scales corresponding to each of the Big -Five, the personality dimensions known to capture most of the individual differences.
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paper_title: Words Mark the Nerds: Computational Models of Personality Recognition through Language

paper_content:

Words Mark the Nerds: Computational Models of Personality Recognition through Language Franc¸ois Mairesse (F.Mairesse@sheffield.ac.uk) Department of Computer Science, University of Sheffield 211 Portobello Street, Sheffield S1 4DP, United Kingdom Marilyn Walker (M.A.Walker@sheffield.ac.uk) Department of Computer Science, University of Sheffield 211 Portobello Street, Sheffield S1 4DP, United Kingdom Abstract ent parties involved. Personality traits influence many aspects of individual behaviour, such as the attitude toward machines (Sigurdsson, 1991), overall job performance (Furnham, Jack- son & Miller, 1999), as well as academic motivation (Komar- raju & Karau, 2005); this last finding suggests that training systems would be more efficient if they could adapt to the learner’s personality. Our goal is to develop models to recognize the user’s per- sonality, and use them to modify the output generation of a dialogue system. This paper presents non-linear statistical models of the five most essential personality traits, learned automatically via machine learning on different sources of data. Results show that extraversion, emotional stability and conscientiousness are easier to model, and recognition mod- els based on observed personality perform significantly bet- ter than a baseline returning the average personality score, as well as better than models using self-reports. An analysis of decision trees confirms previous findings linking language and personality, while revealing many new linguistic markers. User models in human-computer interfaces have focused on various user characteristics, but there is little user modeling of the most fundamental dimension of variation between hu- mans: personality traits. We explore here the possibility of automatically acquiring such models by simply observing the user’s language. We automatically learn models for personality recognition from different corpora and sources of personality evaluation. The models are completely transparent, i.e. they can run in the background evaluating the user at every con- versational turn, and provide input for the system to adapt to the user. Results show that recognition models based on ob- served personality perform significantly better than a baseline of the average personality score, as well as better than mod- els using self-reports. An analysis of decision trees confirms previous findings linking language and personality, while re- vealing many new linguistic markers. Keywords: Personality; Statistical model; Machine learn- ing; Language; Conversation; EAR; Big Five; Pragmatics; Five factor model; Dialogue systems Introduction Personality traits and language Many scientists have focused on how to optimize our inter- actions with machines, by finding the best way to select and communicate information. However, because users behave in different ways, computer systems need to adapt to their con- versational partner, as humans do (Funder & Sneed, 1993; McLarney-Vesotski et al., in press); this is the purpose of user models in dialogue systems (Zukerman & Litman, 2001). One approach to user modeling is to elicit user preferences by asking the user (Linden, Hanks & Lesh, 1997), but other work develops such models through observation, i.e. by ana- lyzing the user’s language. Rich (1979) successfully maps user’s keywords to content selection and language genera- tion parameters, while Maloor & Chai (2000) estimate the user’s expertise using dialogue features. Researchers have also learned user models from data, e.g. for sentence plan- ning (Mairesse & Walker, 2005) and dialogue management (Litman et al., 2000). Rather than modeling specific user preferences, we pro- pose modeling personality traits. By definition, personal- ity is the highest level variable characterizing individuals. Computer users can detect a machine’s personality and pre- fer working with a computer exhibiting the same personality as theirs (Reeves & Nass, 1996). Personalities of real estate agents were used as a basis for the design of an e-commerce website (Fuchs, 2001). In addition, many studies have shown that the success of interpersonal tasks depends on the personalities of the differ- Trait identification Personality can be described as a set of attributes characteriz- ing an individual. The number of those attributes seems to be extremely large. Indeed, when talking about a close friend, one can usually come up with many adjectives describing his or her behaviour. To be able to reason about personality, psy- chologists have tried to identify the most essential traits, re- ferred to as the Big Five (Norman, 1963): • Extraversion (sociability, activity, assertiveness) • Emotional stability (as opposed to neuroticism) • Agreeableness to other people • Conscientiousness (discipline) • Openness to experience (intellect) Such traits were obtained by doing a factor analysis over the adjectives used in personality description questionnaires. They are therefore based on the assumption that the most rel- evant individual differences are encoded into the language, and the more important the difference, the more likely it is to be expressed as a single word. This is referred to as the Lexical Hypothesis. While there are known limits to the Big Five model (Eysenck, 1991; Paunonen & Jackson, 2000), we shall use it in our models, as it provides a very general framework for reasoning about individual differences, and it has become a standard in psychology as it was replicated many times (Nor- man, 1963; Peabody & Goldberg, 1989).
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paper_title: Multi-System Fusion of Extended Context Prosodic and Cepstral Features for Paralinguistic Speaker Trait Classification

paper_content:

As automatic speech processing has matured, research attention has expanded to paralinguistic speech problems that aim to detect beyond-the-words information. This paper focuses on the identification of seven speaker trait categories from the Interspeech Speaker Trait Challenge: likeability, intelligibility, openness, conscientiousness, extraversion, agreeableness, and neuroticism. Our approach combines multiple features including prosodic, cepstral, shifted-delta cepstral, and a reduced set of the OpenSMILE features. Our classification approaches included GMM-UBM, eigenchannel, support vector machines, and distance based classifiers. Optimized feature reduction and logistic regression-based score calibration and fusion led to results that perform competitively against the challenge baseline in all categories.
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paper_title: Automatically Assessing Personality from Speech

paper_content:

In this paper, we present first results on applying a personality assessment paradigm to speech input, and comparing human and automatic performance on this task. We cue a professional speaker to produce speech using different personality profiles and encode the resulting vocal personality impressions in terms of the "Big Five" NEO-FFI personality traits. We then have human raters, who do not know the speaker, estimate the five factors. We analyze the recordings using signal-based acoustic and prosodic methods and observe high consistency between the acted personalities, the raters' assessments, and initial automatic classification results. This presents a first step towards being able to handle personality traits in speech, which we envision will be used in future voice-based communication between humans and machines.
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paper_title: Personality in its natural habitat: manifestations and implicit folk theories of personality in daily life.

paper_content:

To examine the expression of personality in its natural habitat, the authors tracked 96 participants over 2 days using the Electronically Activated Recorder (EAR), which samples snippets of ambient sounds in participants' immediate environments. Participants' Big Five scores were correlated with EAR-derived information on their daily social interactions, locations, activities, moods, and language use; these quotidian manifestations were generally consistent with the trait definitions and (except for Openness) often gender specific. To identify implicit folk theories about daily manifestations of personality, the authors correlated the EAR-derived information with impressions of participants based on their EAR sounds; judges' implicit folk theories were generally accurate (especially for Extraversion) and also partially gender specific. The findings point to the importance of naturalistic observation studies on how personality is expressed and perceived in the natural stream of everyday behavior.
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paper_title: A Frame Pruning Approach for Paralinguistic Recognition Tasks

paper_content:

In conventional paralinguistic classification approaches, information gained by low level features is described over broad segments (like whole turns) via statistical functionals. This procedure presumes meaningful information to be embodied within the whole segment. This assumption may be misleading if distinctive cues within a sample are surrounded by non-meaningful information or noise. In this case it would surely be beneficial to keep only parts of the sample that are most relevant for the recognition task. In this paper we propose a novel cluster-based approach, which aims at identifying frames likely to carry distinctive information. Evaluation is done within the INTERSPEECH 2012 Speaker Trait Challenge. Results show that under certain configurations frame pruning in fact leads to an improvement in recognition accuracy. On the observed corpus most stable improvements were achieved at a frame drop of 4-8%. Index Terms: paralinguistic recognition, frame pruning, personality traits
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paper_title: Using linguistic cues for the automatic recognition of personality in conversation and text

paper_content:

It is well known that utterances convey a great deal of information about the speaker in addition to their semantic content. One such type of information consists of cues to the speaker's personality traits, the most fundamental dimension of variation between humans. Recent work explores the automatic detection of other types of pragmatic variation in text and conversation, such as emotion, deception, speaker charisma, dominance, point of view, subjectivity, opinion and sentiment. Personality affects these other aspects of linguistic production, and thus personality recognition may be useful for these tasks, in addition to many other potential applications. However, to date, there is little work on the automatic recognition of personality traits. This article reports experimental results for recognition of all Big Five personality traits, in both conversation and text, utilising both self and observer ratings of personality. While other work reports classification results, we experiment with classification, regression and ranking models. For each model, we analyse the effect of different feature sets on accuracy. Results show that for some traits, any type of statistical model performs significantly better than the baseline, but ranking models perform best overall. We also present an experiment suggesting that ranking models are more accurate than multi-class classifiers for modelling personality. In addition, recognition models trained on observed personality perform better than models trained using self-reports, and the optimal feature set depends on the personality trait. A qualitative analysis of the learned models confirms previous findings linking language and personality, while revealing many new linguistic markers.
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paper_title: Feature Selection for Speaker Traits

paper_content:

This study focuses on handling high-dimensional classification problems by means of feature selection. The data sets used are provided by the organizers of the Interspeech 2012 Speaker Trait Challenge. A combination of two feature selection approaches gives results that approach or exceed the challenge baselines using a knearest-neighbor classifier. One of the feature selection methods is based on covering the data set with correct unsupervised or supervised classifications according to individual features. The other selection method applies a measure of statistical dependence between discretized features and class labels. Index Terms: pattern recognition, feature selection, high-dimensional data, speaker characteristics
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paper_title: Relative importance of face, body, and speech in judgments of personality and affect.

paper_content:

Three experiments correlated judgments made from observing single channels (face, body, or speech) with multiple channel judgments (face, body, and speech together; or face and speech together). Judges observed the spontaneous behavior of videotaped student nurses in 2 types of interview situations, "deceptive" and "honest," and rated the nurses on 14 bipolar adjective scales (e.g., awkward–natural). The single channel judgments that correlated most highly with the multiple channel judgments depended on the type of attribute being judged and the situation in which the behavior occurred (e.g., in the deception condition, judgments made from speech had the highest correlation with whole-person judgments). (18 ref) (PsycINFO Database Record (c) 2016 APA, all rights reserved)
---
paper_title: The interspeech 2012 speaker trait challenge

paper_content:

Keywords: Computational Paralinguistics ; Speaker Traits ; Personality ; Likability ; Pathology Reference EPFL-CONF-174360 Record created on 2012-01-23, modified on 2017-05-10
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paper_title: Multi-System Fusion of Extended Context Prosodic and Cepstral Features for Paralinguistic Speaker Trait Classification

paper_content:

As automatic speech processing has matured, research attention has expanded to paralinguistic speech problems that aim to detect beyond-the-words information. This paper focuses on the identification of seven speaker trait categories from the Interspeech Speaker Trait Challenge: likeability, intelligibility, openness, conscientiousness, extraversion, agreeableness, and neuroticism. Our approach combines multiple features including prosodic, cepstral, shifted-delta cepstral, and a reduced set of the OpenSMILE features. Our classification approaches included GMM-UBM, eigenchannel, support vector machines, and distance based classifiers. Optimized feature reduction and logistic regression-based score calibration and fusion led to results that perform competitively against the challenge baseline in all categories.
---
paper_title: A Frame Pruning Approach for Paralinguistic Recognition Tasks

paper_content:

In conventional paralinguistic classification approaches, information gained by low level features is described over broad segments (like whole turns) via statistical functionals. This procedure presumes meaningful information to be embodied within the whole segment. This assumption may be misleading if distinctive cues within a sample are surrounded by non-meaningful information or noise. In this case it would surely be beneficial to keep only parts of the sample that are most relevant for the recognition task. In this paper we propose a novel cluster-based approach, which aims at identifying frames likely to carry distinctive information. Evaluation is done within the INTERSPEECH 2012 Speaker Trait Challenge. Results show that under certain configurations frame pruning in fact leads to an improvement in recognition accuracy. On the observed corpus most stable improvements were achieved at a frame drop of 4-8%. Index Terms: paralinguistic recognition, frame pruning, personality traits
---
paper_title: Feature Selection for Speaker Traits

paper_content:

This study focuses on handling high-dimensional classification problems by means of feature selection. The data sets used are provided by the organizers of the Interspeech 2012 Speaker Trait Challenge. A combination of two feature selection approaches gives results that approach or exceed the challenge baselines using a knearest-neighbor classifier. One of the feature selection methods is based on covering the data set with correct unsupervised or supervised classifications according to individual features. The other selection method applies a measure of statistical dependence between discretized features and class labels. Index Terms: pattern recognition, feature selection, high-dimensional data, speaker characteristics
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paper_title: Opensmile: the munich versatile and fast open-source audio feature extractor

paper_content:

We introduce the openSMILE feature extraction toolkit, which unites feature extraction algorithms from the speech processing and the Music Information Retrieval communities. Audio low-level descriptors such as CHROMA and CENS features, loudness, Mel-frequency cepstral coefficients, perceptual linear predictive cepstral coefficients, linear predictive coefficients, line spectral frequencies, fundamental frequency, and formant frequencies are supported. Delta regression and various statistical functionals can be applied to the low-level descriptors. openSMILE is implemented in C++ with no third-party dependencies for the core functionality. It is fast, runs on Unix and Windows platforms, and has a modular, component based architecture which makes extensions via plug-ins easy. It supports on-line incremental processing for all implemented features as well as off-line and batch processing. Numeric compatibility with future versions is ensured by means of unit tests. openSMILE can be downloaded from http://opensmile.sourceforge.net/.
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paper_title: The interspeech 2012 speaker trait challenge

paper_content:

Keywords: Computational Paralinguistics ; Speaker Traits ; Personality ; Likability ; Pathology Reference EPFL-CONF-174360 Record created on 2012-01-23, modified on 2017-05-10
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paper_title: Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models

paper_content:

Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions.
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paper_title: Automatic modeling of personality states in small group interactions

paper_content:

In this paper, we target the automatic recognition of personality states in a meeting scenario employing visual and acoustic features. The social psychology literature has coined the name personality state to refer to a specific behavioral episode wherein a person behaves as more or less introvert/extrovert, neurotic or open to experience, etc. Personality traits can then be reconstructed as density distributions over personality states. Different machine learning approaches were used to test the effectiveness of the selected features in modeling the dynamics of personality states.
---
paper_title: Bridging the Gap between Social Animal and Unsocial Machine: A Survey of Social Signal Processing

paper_content:

Social Signal Processing is the research domain aimed at bridging the social intelligence gap between humans and machines. This paper is the first survey of the domain that jointly considers its three major aspects, namely, modeling, analysis, and synthesis of social behavior. Modeling investigates laws and principles underlying social interaction, analysis explores approaches for automatic understanding of social exchanges recorded with different sensors, and synthesis studies techniques for the generation of social behavior via various forms of embodiment. For each of the above aspects, the paper includes an extensive survey of the literature, points to the most important publicly available resources, and outlines the most fundamental challenges ahead.
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paper_title: Social Signal Processing: Survey of an Emerging Domain

paper_content:

The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence - the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement - in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for social signal processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially aware computing.
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paper_title: Don't ask me what i'm like, just watch and listen

paper_content:

Traditional (based on psychology) approaches for personality assessment of an individual require him/her to fill up a questionnaire. This paper presents a novel way of utilizing multimodal cues to automatically fill up the questionnaire. The contributions of this work are three-fold. (1) Novel psychology-based audio/visual/lexical features are proposed and shown to be effective in predicting answers to a personality questionnaire, Big-Five Inventory-10 (BFI- 10). (2) Extracted features are used to learn linear and kernel versions of a novel regression model, 'SLoT', to automatically predict BFI-10 answers. The model is based on Sparse and Low-rank Transformation (SLoT). (3) Predicted answers are used to compute personality scores using standard BFI-10 scoring scheme. We evaluated our approach on a dataset of 3907 clips (for 50 characters from movies of diverse genres) manually labeled with BFI-10 answers and personality scores as ground-truth. Experiments indicate that the proposed 'SLoT' model effectively automates the answering process by emulating human understanding. We also conclude that predicting personality scores through predicting answers first is better than directly predicting scores based on audio/visual features (as studied in state-of-the art methods).
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paper_title: A multimodal annotated corpus of consensus decision making meetings

paper_content:

In this paper we present an annotated audio–video corpus of multi-party meetings. The multimodal corpus provides for each subject involved in the experimental sessions six annotation dimensions referring to group dynamics; speech activity and body activity. The corpus is based on 11 audio and video recorded sessions which took place in a lab setting appropriately equipped with cameras and microphones. Our main concern in collecting this multimodal corpus was to explore the possibility of providing feedback services to facilitate group processes and to enhance self awareness among small groups engaged in meetings. We therefore introduce a coding scheme for annotating relevant functional roles that appear in a small group interaction. We also discuss the reliability of the coding scheme and we present the first results for automatic classification.
---
paper_title: The YouTube Lens: Crowdsourced Personality Impressions and Audiovisual Analysis of Vlogs

paper_content:

Despite an increasing interest in understanding human perception in social media through the automatic analysis of users' personality, existing attempts have explored user profiles and text blog data only. We approach the study of personality impressions in social media from the novel perspective of crowdsourced impressions, social attention, and audiovisual behavioral analysis on slices of conversational vlogs extracted from YouTube. Conversational vlogs are a unique case study to understand users in social media, as vloggers implicitly or explicitly share information about themselves that words, either written or spoken cannot convey. In addition, research in vlogs may become a fertile ground for the study of video interactions, as conversational video expands to innovative applications. In this work, we first investigate the feasibility of crowdsourcing personality impressions from vlogging as a way to obtain judgements from a variate audience that consumes social media video. Then, we explore how these personality impressions mediate the online video watching experience and relate to measures of attention in YouTube. Finally, we investigate on the use of automatic nonverbal cues as a suitable lens through which impressions are made, and we address the task of automatic prediction of vloggers' personality impressions using nonverbal cues and machine learning techniques. Our study, conducted on a dataset of 442 YouTube vlogs and 2210 annotations collected in Amazon's Mechanical Turk, provides new findings regarding the suitability of collecting personality impressions from crowdsourcing, the types of personality impressions that emerge through vlogging, their association with social attention, and the level of utilization of nonverbal cues in this particular setting. In addition, it constitutes a first attempt to address the task of automatic vlogger personality impression prediction using nonverbal cues, with promising results.
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paper_title: What Elements of an Online Social Networking Profile Predict Target- Rater Agreement in Personality Impressions?

paper_content:

Social-Networking Websites (SNWs) like Facebook and MySpace are playing an increasingly prevalent role in everyday social interactions. But very little is known about the effectiveness of the various profile elements in conveying information about the personality of the profile owner. Here we examine 5,303 impressions made on the basis of a specially designed social networking website (http://www.YouJustGetMe.com) and on the basis of Facebook profiles. Our findings suggest that profile owners are generally seen by others as they see themselves; that when raters are judging unknown targets, rater-target agreement is stronger for female (vs. male) targets and for female (vs. male) raters; and several specific elements of profiles are associated with increased or diminished levels of rater-target impression agreement. The findings are important because they are the first to show how impression agreement may be affected by specific elements in SNW profiles.
---
paper_title: Is Your Profile Picture Worth 1000 Words? Photo Characteristics Associated with Personality Impression Agreement

paper_content:

Social-Networking Websites (SNWs) are rapidly becoming a central media for social exchange. A basic question is how well are people able to get to know each other through these websites? In this study, we explore characteristics of the profile photographs and their association with impression agreement. Using a specially designed social networking website, we examined 1,316 first-impressions of profile owners who had posted photographs as part of a complete profile. The results suggest that photographs in which the profile owners were smiling, outdoors, and shown with others were associated with higher impression agreement. Several gender interactions suggested that other aspects of the photographs, including head covering and apparent weight, also affected impression agreement depending on the gender of the profile owner and visitors. These results were interpreted in light of the literature on interpersonal perception.
---
paper_title: Unveiling the multimedia unconscious: implicit cognitive processes and multimedia content analysis

paper_content:

One of the main findings of cognitive sciences is that automatic processes of which we are unaware shape, to a significant extent, our perception of the environment. The phenomenon applies not only to the real world, but also to multimedia data we consume every day. Whenever we look at pictures, watch a video or listen to audio recordings, our conscious attention efforts focus on the observable content, but our cognition spontaneously perceives intentions, beliefs, values, attitudes and other constructs that, while being outside of our conscious awareness, still shape our reactions and behavior. So far, multimedia technologies have neglected such a phenomenon to a large extent. This paper argues that taking into account cognitive effects is possible and it can also improve multimedia approaches. As a supporting proof-of-concept, the paper shows not only that there are visual patterns correlated with the personality traits of 300 Flickr users to a statistically significant extent, but also that the personality traits (both self-assessed and attributed by others) of those users can be inferred from the images these latter post as "favourite".
---
paper_title: Personality and facial expressions in human-robot interaction

paper_content:

This paper presents the propensities of preference for Human Robot Interaction (HRI) according to different personalities and facial expressions of humans and robots. This study is based on two types of personalities: extroverted and introverted. According to the personality, the facial expressions are distinguished to express extroversion and introversion. The design of experiment is a 3 (participant's group of personality: extrovert, intermediate, introvert) × 2 (robot's groups of personality) between-subjects experiment (N=40) in which participants interact with KMC-EXPR robots expressing either extroversion or introversion. The results showed an unprecedented hypothesis, unexpected implications and certain propensities of preferences.
---
paper_title: Does computer-synthesized speech manifest personality? experimental tests of recognition, similarity-attraction, and consistency-attraction

paper_content:

Would people exhibit similarity-attraction and consistency-attraction toward unambiguously computergenerated speech even when personality is clearly not relevant? In Experiment 1, participants (extrovert or introvert) heard a synthesized voice (extrovert or introvert) on a book-buying Web site. Participants accurately recognized personality cues in text to speech and showed similarity-attraction in their evaluation of the computer voice, the book reviews, and the reviewer. Experiment 2, in a Web auction context, added personality of the text to the previous design. The results replicated Experiment 1 and demonstrated consistency (voice and text personality)-attraction. To maximize liking and trust, designers should set parameters, for example, words per minute or frequency range, that create a personality that is consistent with the user and the content being presented. The vast majority of content on computers and on the World Wide Web is textual. E-mail, documents, spreadsheets, presentation outlines, e-commerce sites, news and information sites, bulletin boards and chat rooms, advertisements, and search engines, although enhanced by graphical content, are all dominated by text. The prevalence of textual material is both a problem and an opportunity. The problem is that there are significant barriers to accessing textual content. Only half of U.S. homes have access to the Internet (The White House, 2000), and less than two-thirds have computers; these are the highest rates in the world (Samuelson, 1999, p. 52).
---
paper_title: Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship

paper_content:

Interfaces that talk and listen are populating computers, cars, call centers, and even home appliances and toys, but voice interfaces invariably frustrate rather than help. In Wired for Speech, Clifford Nass and Scott Brave reveal how interactive voice technologies can readily and effectively tap into the automatic responses all speech -- whether from human or machine -- evokes. Wired for Speech demonstrates that people are "voice-activated": we respond to voice technologies as we respond to actual people and behave as we would in any social situation. By leveraging this powerful finding, voice interfaces can truly emerge as the next frontier for efficient, user-friendly technology.Wired for Speech presents new theories and experiments and applies them to critical issues concerning how people interact with technology-based voices. It considers how people respond to a female voice in e-commerce (does stereotyping matter?), how a car's voice can promote safer driving (are "happy" cars better cars?), whether synthetic voices have personality and emotion (is sounding like a person always good?), whether an automated call center should apologize when it cannot understand a spoken request ("To Err is Interface; To Blame, Complex"), and much more. Nass and Brave's deep understanding of both social science and design, drawn from ten years of research at Nass's Stanford laboratory, produces results that often challenge conventional wisdom and common design practices. These insights will help designers and marketers build better interfaces, scientists construct better theories, and everyone gain better understandings of the future of the machines that speak with us.
---
paper_title: Experimental study on robot personality for humanoid head robot

paper_content:

The authors have been developing human-like head robots in order to develop new head mechanisms and functions for a humanoid robot that has the ability to communicate naturally with a human by expressing human-like emotions. We think that the personality of the robot is extremely important in attaining smooth and effective communication. We introduced a robot personality that consists of the sensing personality and the expression personality. The sensing personality determines how a stimulus works for a robot's mental state. A 3D mental space was defined in the robot. Seven emotions were mapped out in the 3D mental space. After the robot determines its emotion, it expresses its emotion by the expression personality. We expressed the expression personality using a matrix. Moreover, we assigned and loaded six robot personalities into the robot. The robot can express various personalities.
---
paper_title: Knowing me knowing you: exploring effects of culture and context on perception of robot personality

paper_content:

We carry out a set of experiments to assess collaboration between human users and robots in a cross-cultural setting. This paper describes the study design and deployment of a video-based study to investigate task-dependence and cultural-background dependence of the personality trait attribution on a socially interactive robot. In Human-Robot Interaction, as well as in Human-Agent Interaction research, the attribution of personality traits towards intelligent agents has already been researched intensively in terms of the social similarity or complementary rule. We assume that searching the explanation for personality trait attribution in the similarity and complementary rule does not take into account important contextual factors. Just like people equate certain personality types to certain professions, we expect that people may have certain personality expectations depending on the context of the task the robot carries out. Because professions have different social meaning in different national culture, we also expect that these task-dependent personality preferences differ across cultures. Therefore, we suggest an experiment that considers the task-context and the cultural-background of users.
---
paper_title: Evaluation of Four Designed Virtual Agent Personalities

paper_content:

Convincing conversational agents require a coherent set of behavioral responses that can be interpreted by a human observer as indicative of a personality. This paper discusses the continued development and subsequent evaluation of virtual agents based on sound psychological principles. We use Eysenck's theoretical basis to explain aspects of the characterization of our agents, and we describe an architecture where personality affects the agent's global behavior quality as well as their back-channel productions. Drawing on psychological research, we evaluate perception of our agents' personalities and credibility by human viewers (N = 187). Our results suggest that we succeeded in validating theoretically grounded indicators of personality in our virtual agents, and that it is feasible to place our characters on Eysenck's scales. A key finding is that the presence of behavioral characteristics reinforces the prescribed personality profiles that are already emerging from the still images. Our long-term goal is to enhance agents' ability to sustain realistic interaction with human users, and we discuss how this preliminary work may be further developed to include more systematic variation of Eysenck's personality scales.
---
paper_title: Generic personality and emotion simulation for conversational agents

paper_content:

This paper describes a generic model for personality, mood and emotion simulation for conversational virtual humans. We present a generic model for updating the parameters related to emotional behaviour, as well as a linear implementation of the generic update mechanisms. We explore how existing theories for appraisal can be integrated into the framework. Then we describe a prototype system that uses the described models in combination with a dialogue system and a talking head with synchronized speech and facial expressions
---
paper_title: Socially Assistive Robots: The Link between Personality, Empathy, Physiological Signals, and Task Performance

paper_content:

This paper describes a hands-off socially assistive therapist robot designed for monitoring, assisting, encouraging, and socially interacting with users engaged in rehabilitation exercises. We investigate the role of the robot’s embodiment, personality, empathy, and physiological signals in the hands-off therapy process, focusing mainly on the relationship between the level of extroversionintroversion of the robot and the user. We also demonstrate a behavior adaptation system capable of adjusting its social interaction parameters toward customized rehabilitation therapy based on the user’s personality traits and task performance. The experiments validate our hypotheses of mapping the user’s extroversion-introversion personality dimension to a spectrum of robot therapy styles that range from challenging to nurturing and of adapting the robot’s therapy styles based on user personality and performance.
---
paper_title: Modelling personality features by changing prosody in synthetic speech

paper_content:

This study explores how features of brand personalities can b e modelled with the prosodic parameters pitch level, pitch range, articulation rate and loudness. Experiments with parametric al diphone synthesis showed that listeners rated the prosodically changed versions better than a baseline version for the dimen- sions "sincerity", "competence", "sophistication", "excitem ent" and "ruggedness". The contribution of prosodic features such as lower pitch and an enlarged pitch range are analyzed and discussed.
---
paper_title: Can Robots Manifest Personality?: An Empirical Test of Personality Recognition, Social Responses, and Social Presence in Human–Robot Interaction

paper_content:

Personality is an essential feature for creating socially interactive robots. Studies on this dimension will facilitate enhanced human–robot interaction (HRI). Using AIBO, a social robotic pet developed by Sony, we examined the issue of personality in HRI. In this gender-balanced 2 (AIBO personality: introvert vs. extrovert) by 2 (participant personality: introvert vs. extrovert) between-subject experiment (N = 48), we found that participants could accurately recognize a robot’s personality based on its verbal and nonverbal behaviors. In addition, various complementarity attraction effects were found in HRI. Participants enjoyed interacting with a robot more when the robot’s personality was complementary to their own personalities than when the robot’s personality was similar to their own personalities. The same complementarity attraction effect was found in participants’ evaluation of the robot’s intelligence and social attraction. Participants’ feelings of social presence during the interaction were a significant mediator for the complementarity attraction effects observed. Practical and theoretical implications of the current study for the design of social robots and the study of HRI were discussed.
---
paper_title: Does computer-synthesized speech manifest personality? experimental tests of recognition, similarity-attraction, and consistency-attraction

paper_content:

Would people exhibit similarity-attraction and consistency-attraction toward unambiguously computergenerated speech even when personality is clearly not relevant? In Experiment 1, participants (extrovert or introvert) heard a synthesized voice (extrovert or introvert) on a book-buying Web site. Participants accurately recognized personality cues in text to speech and showed similarity-attraction in their evaluation of the computer voice, the book reviews, and the reviewer. Experiment 2, in a Web auction context, added personality of the text to the previous design. The results replicated Experiment 1 and demonstrated consistency (voice and text personality)-attraction. To maximize liking and trust, designers should set parameters, for example, words per minute or frequency range, that create a personality that is consistent with the user and the content being presented. The vast majority of content on computers and on the World Wide Web is textual. E-mail, documents, spreadsheets, presentation outlines, e-commerce sites, news and information sites, bulletin boards and chat rooms, advertisements, and search engines, although enhanced by graphical content, are all dominated by text. The prevalence of textual material is both a problem and an opportunity. The problem is that there are significant barriers to accessing textual content. Only half of U.S. homes have access to the Internet (The White House, 2000), and less than two-thirds have computers; these are the highest rates in the world (Samuelson, 1999, p. 52).
---
paper_title: Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship

paper_content:

Interfaces that talk and listen are populating computers, cars, call centers, and even home appliances and toys, but voice interfaces invariably frustrate rather than help. In Wired for Speech, Clifford Nass and Scott Brave reveal how interactive voice technologies can readily and effectively tap into the automatic responses all speech -- whether from human or machine -- evokes. Wired for Speech demonstrates that people are "voice-activated": we respond to voice technologies as we respond to actual people and behave as we would in any social situation. By leveraging this powerful finding, voice interfaces can truly emerge as the next frontier for efficient, user-friendly technology.Wired for Speech presents new theories and experiments and applies them to critical issues concerning how people interact with technology-based voices. It considers how people respond to a female voice in e-commerce (does stereotyping matter?), how a car's voice can promote safer driving (are "happy" cars better cars?), whether synthetic voices have personality and emotion (is sounding like a person always good?), whether an automated call center should apologize when it cannot understand a spoken request ("To Err is Interface; To Blame, Complex"), and much more. Nass and Brave's deep understanding of both social science and design, drawn from ten years of research at Nass's Stanford laboratory, produces results that often challenge conventional wisdom and common design practices. These insights will help designers and marketers build better interfaces, scientists construct better theories, and everyone gain better understandings of the future of the machines that speak with us.
---
paper_title: Modelling personality features by changing prosody in synthetic speech

paper_content:

This study explores how features of brand personalities can b e modelled with the prosodic parameters pitch level, pitch range, articulation rate and loudness. Experiments with parametric al diphone synthesis showed that listeners rated the prosodically changed versions better than a baseline version for the dimen- sions "sincerity", "competence", "sophistication", "excitem ent" and "ruggedness". The contribution of prosodic features such as lower pitch and an enlarged pitch range are analyzed and discussed.
---
paper_title: Evaluation of Four Designed Virtual Agent Personalities

paper_content:

Convincing conversational agents require a coherent set of behavioral responses that can be interpreted by a human observer as indicative of a personality. This paper discusses the continued development and subsequent evaluation of virtual agents based on sound psychological principles. We use Eysenck's theoretical basis to explain aspects of the characterization of our agents, and we describe an architecture where personality affects the agent's global behavior quality as well as their back-channel productions. Drawing on psychological research, we evaluate perception of our agents' personalities and credibility by human viewers (N = 187). Our results suggest that we succeeded in validating theoretically grounded indicators of personality in our virtual agents, and that it is feasible to place our characters on Eysenck's scales. A key finding is that the presence of behavioral characteristics reinforces the prescribed personality profiles that are already emerging from the still images. Our long-term goal is to enhance agents' ability to sustain realistic interaction with human users, and we discuss how this preliminary work may be further developed to include more systematic variation of Eysenck's personality scales.
---
paper_title: Generic personality and emotion simulation for conversational agents

paper_content:

This paper describes a generic model for personality, mood and emotion simulation for conversational virtual humans. We present a generic model for updating the parameters related to emotional behaviour, as well as a linear implementation of the generic update mechanisms. We explore how existing theories for appraisal can be integrated into the framework. Then we describe a prototype system that uses the described models in combination with a dialogue system and a talking head with synchronized speech and facial expressions
---
paper_title: Personality and facial expressions in human-robot interaction

paper_content:

This paper presents the propensities of preference for Human Robot Interaction (HRI) according to different personalities and facial expressions of humans and robots. This study is based on two types of personalities: extroverted and introverted. According to the personality, the facial expressions are distinguished to express extroversion and introversion. The design of experiment is a 3 (participant's group of personality: extrovert, intermediate, introvert) × 2 (robot's groups of personality) between-subjects experiment (N=40) in which participants interact with KMC-EXPR robots expressing either extroversion or introversion. The results showed an unprecedented hypothesis, unexpected implications and certain propensities of preferences.
---
paper_title: Does computer-synthesized speech manifest personality? experimental tests of recognition, similarity-attraction, and consistency-attraction

paper_content:

Would people exhibit similarity-attraction and consistency-attraction toward unambiguously computergenerated speech even when personality is clearly not relevant? In Experiment 1, participants (extrovert or introvert) heard a synthesized voice (extrovert or introvert) on a book-buying Web site. Participants accurately recognized personality cues in text to speech and showed similarity-attraction in their evaluation of the computer voice, the book reviews, and the reviewer. Experiment 2, in a Web auction context, added personality of the text to the previous design. The results replicated Experiment 1 and demonstrated consistency (voice and text personality)-attraction. To maximize liking and trust, designers should set parameters, for example, words per minute or frequency range, that create a personality that is consistent with the user and the content being presented. The vast majority of content on computers and on the World Wide Web is textual. E-mail, documents, spreadsheets, presentation outlines, e-commerce sites, news and information sites, bulletin boards and chat rooms, advertisements, and search engines, although enhanced by graphical content, are all dominated by text. The prevalence of textual material is both a problem and an opportunity. The problem is that there are significant barriers to accessing textual content. Only half of U.S. homes have access to the Internet (The White House, 2000), and less than two-thirds have computers; these are the highest rates in the world (Samuelson, 1999, p. 52).
---
paper_title: Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship

paper_content:

Interfaces that talk and listen are populating computers, cars, call centers, and even home appliances and toys, but voice interfaces invariably frustrate rather than help. In Wired for Speech, Clifford Nass and Scott Brave reveal how interactive voice technologies can readily and effectively tap into the automatic responses all speech -- whether from human or machine -- evokes. Wired for Speech demonstrates that people are "voice-activated": we respond to voice technologies as we respond to actual people and behave as we would in any social situation. By leveraging this powerful finding, voice interfaces can truly emerge as the next frontier for efficient, user-friendly technology.Wired for Speech presents new theories and experiments and applies them to critical issues concerning how people interact with technology-based voices. It considers how people respond to a female voice in e-commerce (does stereotyping matter?), how a car's voice can promote safer driving (are "happy" cars better cars?), whether synthetic voices have personality and emotion (is sounding like a person always good?), whether an automated call center should apologize when it cannot understand a spoken request ("To Err is Interface; To Blame, Complex"), and much more. Nass and Brave's deep understanding of both social science and design, drawn from ten years of research at Nass's Stanford laboratory, produces results that often challenge conventional wisdom and common design practices. These insights will help designers and marketers build better interfaces, scientists construct better theories, and everyone gain better understandings of the future of the machines that speak with us.
---
paper_title: Experimental study on robot personality for humanoid head robot

paper_content:

The authors have been developing human-like head robots in order to develop new head mechanisms and functions for a humanoid robot that has the ability to communicate naturally with a human by expressing human-like emotions. We think that the personality of the robot is extremely important in attaining smooth and effective communication. We introduced a robot personality that consists of the sensing personality and the expression personality. The sensing personality determines how a stimulus works for a robot's mental state. A 3D mental space was defined in the robot. Seven emotions were mapped out in the 3D mental space. After the robot determines its emotion, it expresses its emotion by the expression personality. We expressed the expression personality using a matrix. Moreover, we assigned and loaded six robot personalities into the robot. The robot can express various personalities.
---
paper_title: Knowing me knowing you: exploring effects of culture and context on perception of robot personality

paper_content:

We carry out a set of experiments to assess collaboration between human users and robots in a cross-cultural setting. This paper describes the study design and deployment of a video-based study to investigate task-dependence and cultural-background dependence of the personality trait attribution on a socially interactive robot. In Human-Robot Interaction, as well as in Human-Agent Interaction research, the attribution of personality traits towards intelligent agents has already been researched intensively in terms of the social similarity or complementary rule. We assume that searching the explanation for personality trait attribution in the similarity and complementary rule does not take into account important contextual factors. Just like people equate certain personality types to certain professions, we expect that people may have certain personality expectations depending on the context of the task the robot carries out. Because professions have different social meaning in different national culture, we also expect that these task-dependent personality preferences differ across cultures. Therefore, we suggest an experiment that considers the task-context and the cultural-background of users.
---
paper_title: Socially Assistive Robots: The Link between Personality, Empathy, Physiological Signals, and Task Performance

paper_content:

This paper describes a hands-off socially assistive therapist robot designed for monitoring, assisting, encouraging, and socially interacting with users engaged in rehabilitation exercises. We investigate the role of the robot’s embodiment, personality, empathy, and physiological signals in the hands-off therapy process, focusing mainly on the relationship between the level of extroversionintroversion of the robot and the user. We also demonstrate a behavior adaptation system capable of adjusting its social interaction parameters toward customized rehabilitation therapy based on the user’s personality traits and task performance. The experiments validate our hypotheses of mapping the user’s extroversion-introversion personality dimension to a spectrum of robot therapy styles that range from challenging to nurturing and of adapting the robot’s therapy styles based on user personality and performance.
---
paper_title: Can Robots Manifest Personality?: An Empirical Test of Personality Recognition, Social Responses, and Social Presence in Human–Robot Interaction

paper_content:

Personality is an essential feature for creating socially interactive robots. Studies on this dimension will facilitate enhanced human–robot interaction (HRI). Using AIBO, a social robotic pet developed by Sony, we examined the issue of personality in HRI. In this gender-balanced 2 (AIBO personality: introvert vs. extrovert) by 2 (participant personality: introvert vs. extrovert) between-subject experiment (N = 48), we found that participants could accurately recognize a robot’s personality based on its verbal and nonverbal behaviors. In addition, various complementarity attraction effects were found in HRI. Participants enjoyed interacting with a robot more when the robot’s personality was complementary to their own personalities than when the robot’s personality was similar to their own personalities. The same complementarity attraction effect was found in participants’ evaluation of the robot’s intelligence and social attraction. Participants’ feelings of social presence during the interaction were a significant mediator for the complementarity attraction effects observed. Practical and theoretical implications of the current study for the design of social robots and the study of HRI were discussed.
---
paper_title: Workshop on Computational Personality Recognition: Shared Task

paper_content:

In the Workshop on Computational Personality Recognition (Shared Task), we released two datasets, varying in size and genre, annotated with gold standard personality labels. This allowed participants to evaluate features and learning techniques, and even to compare the performances of their systems for personality recognition on a common benchmark. We had 8 participants to the task. In this paper we discuss the results and compare them to previous literature.
---
paper_title: The interspeech 2012 speaker trait challenge

paper_content:

Keywords: Computational Paralinguistics ; Speaker Traits ; Personality ; Likability ; Pathology Reference EPFL-CONF-174360 Record created on 2012-01-23, modified on 2017-05-10
---
paper_title: From speech to personality: mapping voice quality and intonation into personality differences

paper_content:

From a cognitive point of view, personality perception corresponds to capturing individual differences and can be thought of as positioning the people around us in an ideal personality space. The more similar the personality of two individuals, the closer their position in the space. This work shows that the mutual position of two individuals in the personality space can be inferred from prosodic features. The experiments, based on ordinal regression techniques, have been performed over a corpus of 640 speech samples comprising 322 individuals assessed in terms of personality traits by 11 human judges, which is the largest database of this type in the literature. The results show that the mutual position of two individuals can be predicted with up to 80% accuracy.
---
paper_title: Humans as feature extractors: Combining prosody and personality perception for improved speaking style recognition

paper_content:

This paper presents experiments where natural and spontaneous cognitive processes, in particular those who lead to the attribution of personality traits to unacquainted people, are used as a natural form of feature extraction. In particular, personality assessments provided by human judges are used as features to distinguish between professional and non-professional speakers. The same task is performed with prosodic features extracted with a fully automatic process for comparison purposes. Furthermore both prosodic features and personality assessments are combined. The results show that the discrimination between professional and non-professional speaking styles can be performed with an accuracy of 87.2% when using prosodic features, of 75.5% when using personality assessments, and of 90.0% when using the combination of the two.
---
paper_title: What Are They Blogging About? Personality, Topic and Motivation in Blogs.

paper_content:

Personal weblogs (blogs), provide individuals with the opportunity to write freely and express themselves online in the presence of others. In such situations, what do bloggers write about, and what are their motivations for blogging? Using a large blog corpus annotated with the LIWC text analysis program, we examine the content of blogs to provide insight into the role of personality in motivation for blogging. As predicted, we find that highly Neurotic authors use blogs to serve a cathartic or auto-therapeutic function, and reflect mainly upon themselves and negative emotions. Highly Extraverted blog authors, as expected, use blogs to document their lives at a high level, and uniquely interact directly with the reader. Additionally Extraverts use blogs to vent both positive and negative emotions. Bloggers who are high scorers on the Openness trait are more concerned with leisure activities, although they are more evaluative than intellectual, whereas high Conscientiousness bloggers tend to report daily life – and work – around them. As in other contexts, expressing positive rather than negative emotions is associated with high Agreeableness, but that trait is associated with self reference to a greater degree in blogs than elsewhere. In general, findings are consistent with other contexts indicating that bloggers tend to adapt to the possibilities of the medium, rather than try to present themselves differently.
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paper_title: Automatically Assessing Personality from Speech

paper_content:

In this paper, we present first results on applying a personality assessment paradigm to speech input, and comparing human and automatic performance on this task. We cue a professional speaker to produce speech using different personality profiles and encode the resulting vocal personality impressions in terms of the "Big Five" NEO-FFI personality traits. We then have human raters, who do not know the speaker, estimate the five factors. We analyze the recordings using signal-based acoustic and prosodic methods and observe high consistency between the acted personalities, the raters' assessments, and initial automatic classification results. This presents a first step towards being able to handle personality traits in speech, which we envision will be used in future voice-based communication between humans and machines.
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paper_title: Affective image classification using features inspired by psychology and art theory

paper_content:

Images can affect people on an emotional level. Since the emotions that arise in the viewer of an image are highly subjective, they are rarely indexed. However there are situations when it would be helpful if images could be retrieved based on their emotional content. We investigate and develop methods to extract and combine low-level features that represent the emotional content of an image, and use these for image emotion classification. Specifically, we exploit theoretical and empirical concepts from psychology and art theory to extract image features that are specific to the domain of artworks with emotional expression. For testing and training, we use three data sets: the International Affective Picture System (IAPS); a set of artistic photography from a photo sharing site (to investigate whether the conscious use of colors and textures displayed by the artists improves the classification); and a set of peer rated abstract paintings to investigate the influence of the features and ratings on pictures without contextual content. Improved classification results are obtained on the International Affective Picture System (IAPS), compared to state of the art work.
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paper_title: Using linguistic cues for the automatic recognition of personality in conversation and text

paper_content:

It is well known that utterances convey a great deal of information about the speaker in addition to their semantic content. One such type of information consists of cues to the speaker's personality traits, the most fundamental dimension of variation between humans. Recent work explores the automatic detection of other types of pragmatic variation in text and conversation, such as emotion, deception, speaker charisma, dominance, point of view, subjectivity, opinion and sentiment. Personality affects these other aspects of linguistic production, and thus personality recognition may be useful for these tasks, in addition to many other potential applications. However, to date, there is little work on the automatic recognition of personality traits. This article reports experimental results for recognition of all Big Five personality traits, in both conversation and text, utilising both self and observer ratings of personality. While other work reports classification results, we experiment with classification, regression and ranking models. For each model, we analyse the effect of different feature sets on accuracy. Results show that for some traits, any type of statistical model performs significantly better than the baseline, but ranking models perform best overall. We also present an experiment suggesting that ranking models are more accurate than multi-class classifiers for modelling personality. In addition, recognition models trained on observed personality perform better than models trained using self-reports, and the optimal feature set depends on the personality trait. A qualitative analysis of the learned models confirms previous findings linking language and personality, while revealing many new linguistic markers.
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paper_title: Unveiling the multimedia unconscious: implicit cognitive processes and multimedia content analysis

paper_content:

One of the main findings of cognitive sciences is that automatic processes of which we are unaware shape, to a significant extent, our perception of the environment. The phenomenon applies not only to the real world, but also to multimedia data we consume every day. Whenever we look at pictures, watch a video or listen to audio recordings, our conscious attention efforts focus on the observable content, but our cognition spontaneously perceives intentions, beliefs, values, attitudes and other constructs that, while being outside of our conscious awareness, still shape our reactions and behavior. So far, multimedia technologies have neglected such a phenomenon to a large extent. This paper argues that taking into account cognitive effects is possible and it can also improve multimedia approaches. As a supporting proof-of-concept, the paper shows not only that there are visual patterns correlated with the personality traits of 300 Flickr users to a statistically significant extent, but also that the personality traits (both self-assessed and attributed by others) of those users can be inferred from the images these latter post as "favourite".
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paper_title: Perceptual coding of digital audio

paper_content:

During the last decade, CD-quality digital audio has essentially replaced analog audio. Emerging digital audio applications for network, wireless, and multimedia computing systems face a series of constraints such as reduced channel bandwidth, limited storage capacity, and low cost. These new applications have created a demand for high-quality digital audio delivery at low bit rates. In response to this need, considerable research has been devoted to the development of algorithms for perceptually transparent coding of high-fidelity (CD-quality) digital audio. As a result, many algorithms have been proposed, and several have now become international and/or commercial product standards. This paper reviews algorithms for perceptually transparent coding of CD-quality digital audio, including both research and standardization activities. This paper is organized as follows. First, psychoacoustic principles are described, with the MPEG psychoacoustic signal analysis model 1 discussed in some detail. Next, filter bank design issues and algorithms are addressed, with a particular emphasis placed on the modified discrete cosine transform, a perfect reconstruction cosine-modulated filter bank that has become of central importance in perceptual audio coding. Then, we review methodologies that achieve perceptually transparent coding of FM- and CD-quality audio signals, including algorithms that manipulate transform components, subband signal decompositions, sinusoidal signal components, and linear prediction parameters, as well as hybrid algorithms that make use of more than one signal model. These discussions concentrate on architectures and applications of those techniques that utilize psychoacoustic models to exploit efficiently masking characteristics of the human receiver. Several algorithms that have become international and/or commercial standards receive in-depth treatment, including the ISO/IEC MPEG family (-1, -2, -4), the Lucent Technologies PAC/EPAC/MPAC, the Dolby AC-2/AC-3, and the Sony ATRAC/SDDS algorithms. Then, we describe subjective evaluation methodologies in some detail, including the ITU-R BS.1116 recommendation on subjective measurements of small impairments. This paper concludes with a discussion of future research directions.
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paper_title: Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship

paper_content:

Interfaces that talk and listen are populating computers, cars, call centers, and even home appliances and toys, but voice interfaces invariably frustrate rather than help. In Wired for Speech, Clifford Nass and Scott Brave reveal how interactive voice technologies can readily and effectively tap into the automatic responses all speech -- whether from human or machine -- evokes. Wired for Speech demonstrates that people are "voice-activated": we respond to voice technologies as we respond to actual people and behave as we would in any social situation. By leveraging this powerful finding, voice interfaces can truly emerge as the next frontier for efficient, user-friendly technology.Wired for Speech presents new theories and experiments and applies them to critical issues concerning how people interact with technology-based voices. It considers how people respond to a female voice in e-commerce (does stereotyping matter?), how a car's voice can promote safer driving (are "happy" cars better cars?), whether synthetic voices have personality and emotion (is sounding like a person always good?), whether an automated call center should apologize when it cannot understand a spoken request ("To Err is Interface; To Blame, Complex"), and much more. Nass and Brave's deep understanding of both social science and design, drawn from ten years of research at Nass's Stanford laboratory, produces results that often challenge conventional wisdom and common design practices. These insights will help designers and marketers build better interfaces, scientists construct better theories, and everyone gain better understandings of the future of the machines that speak with us.
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paper_title: Users of the world, unite! The challenges and opportunities of Social Media

paper_content:

The concept of Social Media is top of the agenda for many business executives today. Decision makers, as well as consultants, try to identify ways in which firms can make profitable use of applications such as Wikipedia, YouTube, Facebook, Second Life, and Twitter. Yet despite this interest, there seems to be very limited understanding of what the term ''Social Media'' exactly means; this article intends to provide some clarification. We begin by describing the concept of Social Media, and discuss how it differs from related concepts such as Web 2.0 and User Generated Content. Based on this definition, we then provide a classification of Social Media which groups applications currently subsumed under the generalized term into more specific categories by characteristic: collaborative projects, blogs, content communities, social networking sites, virtual game worlds, and virtual social worlds. Finally, we present 10 pieces of advice for companies which decide to utilize Social Media.
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paper_title: A study on personality identification using game based theory

paper_content:

Game Based Personality Profiling Application had been develop as an application to solve the problems faced by a counselor in capturing and determine personality. There are many types of personality model and theory such as Jung's Sixteen personality, Myers Briggs Types Indicators (MBTI), 5 Big factors, and Kathrine Benziger's Personality. This application uses MBTI based concept as a guideline to capture sixteen types of personality. The game genre is puzzle game, and it was developed using Netbeans tools and object-oriented technique. The client or the player will be tested by four mini games in this application so that the game Artifact Intelligent (AI) will capture the personality of the player. The system was tested using 50 samples which were chosen randomly and 77.5% of the results are based on engineering student. The result obtain was compared with the personality psychometric test paper based.
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paper_title: Do Parents Recognize Autistic Deviant Behavior Long before Diagnosis? Taking into Account Interaction Using Computational Methods

paper_content:

Background ::: To assess whether taking into account interaction synchrony would help to better differentiate autism (AD) from intellectual disability (ID) and typical development (TD) in family home movies of infants aged less than 18 months, we used computational methods. ::: ::: ::: Methodology and Principal Findings ::: First, we analyzed interactive sequences extracted from home movies of children with AD (N = 15), ID (N = 12), or TD (N = 15) through the Infant and Caregiver Behavior Scale (ICBS). Second, discrete behaviors between baby (BB) and Care Giver (CG) co-occurring in less than 3 seconds were selected as single interactive patterns (or dyadic events) for analysis of the two directions of interaction (CG→BB and BB→CG) by group and semester. To do so, we used a Markov assumption, a Generalized Linear Mixed Model, and non negative matrix factorization. Compared to TD children, BBs with AD exhibit a growing deviant development of interactive patterns whereas those with ID rather show an initial delay of development. Parents of AD and ID do not differ very much from parents of TD when responding to their child. However, when initiating interaction, parents use more touching and regulation up behaviors as early as the first semester. ::: ::: ::: Conclusion ::: When studying interactive patterns, deviant autistic behaviors appear before 18 months. Parents seem to feel the lack of interactive initiative and responsiveness of their babies and try to increasingly supply soliciting behaviors. Thus we stress that credence should be given to parents' intuition as they recognize, long before diagnosis, the pathological process through the interactive pattern with their child.
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paper_title: Bridging the Gap between Social Animal and Unsocial Machine: A Survey of Social Signal Processing

paper_content:

Social Signal Processing is the research domain aimed at bridging the social intelligence gap between humans and machines. This paper is the first survey of the domain that jointly considers its three major aspects, namely, modeling, analysis, and synthesis of social behavior. Modeling investigates laws and principles underlying social interaction, analysis explores approaches for automatic understanding of social exchanges recorded with different sensors, and synthesis studies techniques for the generation of social behavior via various forms of embodiment. For each of the above aspects, the paper includes an extensive survey of the literature, points to the most important publicly available resources, and outlines the most fundamental challenges ahead.
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paper_title: Social Signal Processing: Survey of an Emerging Domain

paper_content:

The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence - the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement - in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for social signal processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially aware computing.
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paper_title: Unveiling the multimedia unconscious: implicit cognitive processes and multimedia content analysis

paper_content:

One of the main findings of cognitive sciences is that automatic processes of which we are unaware shape, to a significant extent, our perception of the environment. The phenomenon applies not only to the real world, but also to multimedia data we consume every day. Whenever we look at pictures, watch a video or listen to audio recordings, our conscious attention efforts focus on the observable content, but our cognition spontaneously perceives intentions, beliefs, values, attitudes and other constructs that, while being outside of our conscious awareness, still shape our reactions and behavior. So far, multimedia technologies have neglected such a phenomenon to a large extent. This paper argues that taking into account cognitive effects is possible and it can also improve multimedia approaches. As a supporting proof-of-concept, the paper shows not only that there are visual patterns correlated with the personality traits of 300 Flickr users to a statistically significant extent, but also that the personality traits (both self-assessed and attributed by others) of those users can be inferred from the images these latter post as "favourite".
---
paper_title: Introverted elves & conscientious gnomes: the expression of personality in world of warcraft

paper_content:

Personality inference can be used for dynamic personalization of content or system customization. In this study, we examined whether and how personality is expressed in Virtual Worlds (VWs). Survey data from 1,040 World of Warcraft players containing demographic and personality variables was paired with their VW behavioral metrics over a four-month period. Many behavioral cues in VWs were found to be related to personality. For example, Extraverts prefer group activities over solo activities. We also found that these behavioral indicators can be used to infer a player's personality.
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paper_title: The LDOS-PerAff-1 Corpus of Face Video Clips with Affective and Personality Metadata

paper_content:

This paper presents a corpus of video clips of users responding to emotional stimuli. The corpus is unique for two reasons. First, the emotions are annotated in the valence-arousal-dominance space instead of the usual coarse basic emotions. Second, the subjects are annotated with their personality parameters which offers a new ground for further investigations on personality and emotions. The corpus has been compiled for the needs of our research on recommender system. The paper provides information about the corpus acquisition procedure, corpus basic statistics with few examples and a short description of the research work where the corpus has been used in the past.
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paper_title: Workshop on Computational Personality Recognition: Shared Task

paper_content:

In the Workshop on Computational Personality Recognition (Shared Task), we released two datasets, varying in size and genre, annotated with gold standard personality labels. This allowed participants to evaluate features and learning techniques, and even to compare the performances of their systems for personality recognition on a common benchmark. We had 8 participants to the task. In this paper we discuss the results and compare them to previous literature.
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paper_title: The interspeech 2012 speaker trait challenge

paper_content:

Keywords: Computational Paralinguistics ; Speaker Traits ; Personality ; Likability ; Pathology Reference EPFL-CONF-174360 Record created on 2012-01-23, modified on 2017-05-10
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Title: A Survey of Personality Computing

Section 1: Introduction
Description 1: This section provides an overview and motivation behind personality computing.

Section 2: Personality and Its Measurement
Description 2: This section discusses the concept of personality, particularly the trait-based models and the Big Five, as well as techniques for measuring personality.

Section 3: Personality: From Psychology to Computing
Description 3: This section introduces the interplay between personality and the use of computing technologies, including studies linking traits and technology usage.

Section 4: Personality Computing
Description 4: This section outlines the three main problems in personality computing: Automatic Personality Recognition, Automatic Personality Perception, and Automatic Personality Synthesis.

Section 5: Automatic Personality Recognition
Description 5: This section surveys the approaches to automatic personality recognition using various distal cues such as text, nonverbal communication, social media, mobile, and wearable devices, and computer games.

Section 6: Automatic Personality Perception
Description 6: This section reviews works focused on automatic personality perception, particularly from nonverbal behavior and social media.

Section 7: Automatic Personality Synthesis
Description 7: This section explores methods for artificially synthesizing perceived personality traits in speech, artificial agents, and robots.

Section 8: Towards a Research Agenda
Description 8: This section points out the open issues and challenges in personality computing and suggests future research directions.

Section 9: Conclusions
Description 9: This section provides a summary of the survey and offers final thoughts on the state-of-the-art in personality computing.