Multiparticipant chat analysis: A survey
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paper_title: Annotating Preferences in Chats for Strategic Games

paper_content:

This paper describes an annotation scheme for expressions of preferences in on-line chats concerning bargaining negotiations in the online version of the competitive game Settlers of Catan.
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paper_title: Digesting Virtual "Geek" Culture: The Summarization Of Technical Internet Relay Chats

paper_content:

This paper describes a summarization system for technical chats and emails on the Linux kernel. To reflect the complexity and sophistication of the discussions, they are clustered according to subtopic structure on the sub-message level, and immediate responding pairs are identified through machine learning methods. A resulting summary consists of one or more mini-summaries, each on a subtopic from the discussion.
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paper_title: Studying the use of developer IRC meetings in open source projects

paper_content:

Open source developers communicate with each other via various online outlets. Thus far, mailing lists have been the main coordination mechanism. However, our previous study shows that the use of developer IRC meetings is increasing in recent years. In this paper, we perform a study on the IRC meetings of two large open source projects: the GTK+ and Evolution projects. We explore three dimensions: who participates in the meetings, what do they discuss and how do they run the meetings. We find 1) that a small and stable number of the participants contribute the majority of messages in meetings, 2) that there are commonly discussed topics as well as project specific topics 3) that meeting styles vary across different projects.
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paper_title: Towards educational data mining : Using data mining methods for automated chat analysis to understand and support inquiry learning processes

paper_content:

In this paper we investigate the application of data mining methods to provide learners with real-time adaptive feedback on the nature and patterns of their on-line communication while learning collaboratively. We derived two mod- els for classifying chat messages using data mining techniques and tested these on an actual data set (16). The reliability of the classification of chat messages is established by comparing the models performance to that of humans. Results indicate that the classification of messages is reasonably reliable and can thus be done automatically and in real-time. This makes it, for example, possible to increase the awareness of learners by visualizing their interaction behaviour by means of avatars. It is concluded that the application of data mining methods to educational chats is both feasible and can, over time, result in the improvement of learning environments.
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paper_title: What is chat doing in the workplace?

paper_content:

We report an empirical study of a synchronous messaging application with group-oriented functionality designed to support teams in the workplace. In particular, the tool supports group chat windows that allow members of a group to communicate with text that persists for about a day. We describe the experience of 6 globally-distributed work groups who used the tool over a period of 17 months. An analysis of use shows that the group functionality was used primarily for bursts of synchronous conversations and occasional asynchronous exchanges. The content was primarily focused on work tasks, and negotiating availability, with a smattering of non-work topics and humor. Nearly all groups were remarkably similar in the content of their group chat, although the research group chatted far more frequently than the others. We conclude with suggestions for future research, and a discussion of the place of team-oriented synchronous messaging tools in the workplace.
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paper_title: Fast Talking, Fast Shooting: Text Chat in an Online First-Person Game

paper_content:

How actively do users chat, with whom, about what, and how coherently, when they are shooting enemies and dodging bullets in a fast-paced virtual gaming environment? This paper reports on a study of public text chat in BZFlag, an open source capturethe-flag game in which user avatars are tanks. Chat data were analyzed using methods of content and discourse analysis, including analyzing the coherence of extended conversations. The findings reveal that public chat is used actively in BZFlag, primarily to react to and negotiate game play, and that extended conversations occur intermittently and are surprisingly coherent. Implications are discussed for multitasking, classifying multiplayer online games, and enhancing the chatability, or chat usability, of first-person shooter game designs.
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paper_title: Visualisation of Learners' Contributions in Chat Conversations

paper_content:

In this paper is presented a novel dialogistic, socio-cultural perspective and an associated software tool, which provide structured visualisation and analysis means of Computer Supported Collaborative Learning chat conversations. The implemented tools use knowledge-based techniques and are based on Bakhtin's dialogistic paradigm. They visualize the threading of topics and utterances in the conversation and the contributions of the participants in collaborative learning during instant messenger chats. Natural language processing based on the WordNet lexical ontology and semantic distances are used for detecting topics in the chat and their threading. The experiments with the developed application were performed with students at a course on Human-Computer Interaction in Bucharest Politehnica University.
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paper_title: Conversation Extraction in Dynamic Text Message Stream

paper_content:

Text message stream which is produced by Instant Messager and Internet Relay Chat poses interesting and challenging problems for information technologies. It is beneficial to extract the conversations in this kind of chatting message stream for information management and knowledge finding. However, the data in text message stream are usually very short and incomplete, and it requires efficiency to monitor thousands of continuous chat sessions. Many existing text mining methods encounter challenges. This paper focuses on the conversation extraction in dynamic text message stream. We design the dynamic representation for messages to combine the text content information and linguistic feature in message stream. A memory structure of reversed maximal similar relationship is developed for renewable assignments when grouping messages into conversations. We finally propose a double time window algorithm based on above methods to extract conversations in dynamic text message stream. Experiments on a real dataset shows that our method outperforms two baseline methods introduced in a recent related paper about 47% and 15% in terms of F measure respectively.
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paper_title: Communication networks in geographically distributed software development

paper_content:

In this paper, we seek to shed light on how communication networks in geographically distributed projects evolve in order to address the limits of the modular design strategy. We collected data from a geographically distributed software development project covering 39 months of activity. Our analysis showed that over time a group of developers emerge as the liaisons between formal teams and geographical locations. In addition to handling the communication and coordination load across teams and locations, those engineers contributed the most to the development effort.
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paper_title: The Impact of Synchronous Text-Based Chat on Military Command and Control

paper_content:

Abstract : This research assesses the impact of synchronous (real-time), text-based chat on military command and control (C2) processes. Chat use among the services, particularly among joint forces, has evolved in ad hoc fashion to fill gaps in currently fielded C2 systems. This growth-by- improvisation inhibits clear definition of the underlying requirements: precisely what C2 deficiencies are being addressed by text-based chat tools? Or, from a bottom-up perspective: what capabilities do text-based chat tools bring to the war fighter? In this study we employ a broad set of use-cases to further refine why operators use chat based on how they apply chat to their specific combat problems. These use cases include ongoing combat operations in ENDURING FREEDOM, counter-insurgency operations in IRAQI FREEDOM, and disaster relief operations with Joint Task Force - Katrina. The focus of this study is on establishing operators perceived requirements in light of the current capabilities delivered by the existing text-based chat tools. From these reverse-engineered requirements we propose future work to establish these communication capabilities in the next-generation C2 systems.
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paper_title: Where's the"Party"in"Multi-Party"? Analyzing the Structure of Small-Group Sociable Talk

paper_content:

Spontaneous multi-party interaction -- conversation among groups of three or more participants -- is part of daily life. While automated modeling of such interactions has received increased attention in ubiquitous computing research, there is little applied research on the organization of this highly dynamic and spontaneous sociable interaction within small groups. We report here on an applied conversation analytic study of small-group sociable talk, emphasizing structural and temporal aspects that can inform computational models. In particular, we examine the mechanics of multiple simultaneous conversational floors -- how participants initiate a new floor amidst an on-going floor, and how they subsequently show their affiliation with one floor over another. We also discuss the implications of these findings for the design of "smart" multi-party applications.
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paper_title: Disentangling Chat

paper_content:

When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately. We refer to this task as disentanglement. We present a corpus of Internet Relay Chat dialogue in which the various conversations have been manually disentangled, and evaluate annotator reliability. We propose a graph-based clustering model for disentanglement, using lexical, timing, and discourse-based features. The model's predicted disentanglements are highly correlated with manual annotations. We conclude by discussing two extensions to the model, specificity tuning and conversation start detection, both of which are promising but do not currently yield practical improvements.
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paper_title: Conversation trees and threaded chats

paper_content:

Chat programs and instant messaging services are increasingly popular among Internet users. However, basic issues with the interfaces and data structures of most forms of chat limit their utility for use in formal interactions (like group meetings) and decision-making tasks. In this paper, we discuss Threaded Text Chat, a program designed to address some of the deficiencies of current chat programs. Standard forms of chat introduce ambiguity into interaction in a number of ways, most profoundly by rupturing connections between turns and replies. Threaded Chat presents a solution to this problem by supporting the basic turn-taking structure of human conversation. While the solution introduces interface design challenges of its own, usability studies show that users' patterns of interaction in Threaded Chat are equally effective, but different (and possibly more efficient) than standard chat programs.
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paper_title: Normalization of Chinese chat language

paper_content:

Real-time communication platforms such as ICQ, MSN and online chat rooms are getting more popular than ever on the Internet. There are, however, real risks where criminals and terrorists can perpetrate illegal and criminal abuses. This highlights the security significance of accurate detection and translation of the chat language to its stand language counterpart. The language used on these platforms differs significantly from the standard language. This language, referred to as chat language, is comparatively informal, anomalous and dynamic. Such features render conventional language resources such as dictionaries, and processing tools such as parsers ineffective. In this paper, we present the NIL corpus, a chat language text collection annotated to facilitate training and testing of chat language processing algorithms. We analyse the NIL corpus to study the linguistic characteristics and contextual behaviour of a chat language. First we observe that majority of the chat terms, i.e. informal words in a chat text, is formed by phonetic mapping. We then propose the eXtended Source Channel Model (XSCM) for the normalization of the chat language, which is a process to convert messages expressed in a chat language to its standard language counterpart. Experimental results indicate that the performance of XSCM in terms of chat term recognition and normalization accuracy is superior to its Source Channel Model (SCM) counterparts, and is also more consistent over time.
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paper_title: Text chat in action

paper_content:

Synchronous text communication is becoming recognized as a valuable workplace communication medium yet some studies of group text chat indicate that its properties can lead to interactional incoherence. We consider this issue through a detailed analytic examination of text chat transcripts by showing how participants manage their interactions through considering multiple threads, turn taking and topic change. We reveal the routine practices that participants employ to create and manage coherent interaction. These practices arise from the turn taking system in operation, which facilitates straightforward repair of misunderstandings. We conclude by considering the implications of this for design and for the organisation and management of interactions of various forms.
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paper_title: Text Classification Methodologies Applied to Micro-Text in Military Chat

paper_content:

We propose methods to classify lines of military chat, or posts, which contain items of interest. We evaluated several current text categorization and feature selection methodologies on chat posts. Our chat posts are examples of 'micro-text', or text that is generally very short in length, semi-structured, and characterized by unstructured or informal grammar and language. Although this study focused specifically on tactical updates via chat, we believe the findings are applicable to content of a similar linguistic structure. Completion of this milestone is a significant first step in allowing for more complex categorization and information extraction.
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paper_title: Digesting Virtual "Geek" Culture: The Summarization Of Technical Internet Relay Chats

paper_content:

This paper describes a summarization system for technical chats and emails on the Linux kernel. To reflect the complexity and sophistication of the discussions, they are clustered according to subtopic structure on the sub-message level, and immediate responding pairs are identified through machine learning methods. A resulting summary consists of one or more mini-summaries, each on a subtopic from the discussion.
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paper_title: Conversation Thread Extraction and Topic Detection in Text-Based Chat

paper_content:

Abstract : Text-based chat systems are widely used within the Department of Defense, but the standard systems available do not provide robust capabilities for search, information retrieval, or information assurance. The objective of this research is to explore methods for the extraction of conversation threads from text-based chat systems in order to enable such tasks. As part of the research, we manually annotated over 20,000 Internet Relay Chat posts with conversation thread information and constructed a probabilistic model for automatically classifying posts according to conversation thread. We also provide an algorithm for extracting these conversation threads from the chat session in order to form discrete documents that may be used in a vector space model information retrieval system. We elaborate how this technique can be used to support search and data mining systems, as well as auditing tasks and guard functions in a security system. Using the developed probabilistic models, we have achieved classification results on par with those of human annotators.
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paper_title: Automatic Author Profiling of Online Chat Logs

paper_content:

Abstract : Now that the Internet has become easily accessible and more affordable, a larger number of people spend more time in front of a computer. Some spend so much time on the Internet that they develop virtual friendships and relationships people with whom they have regular contact via a computer screen and the Internet. While most of the dialogue exchanged online is not harmful or illegal, there are those with dishonest intentions lurking online. These people can be breaking the law by seducing a minor virtually or even going as far as meeting a minor in person. Terrorists can also use the Internet to facilitate communication and plan attacks. Since e-mail is one of the original means of communication on the Internet, methods for determining the author of an e-mail have already been studied. So far, however, no significant experimentation with online chat logs exists. The first of part of this study is comprised of generating an unbiased, random, and broad corpus of online chat logs. Having a general corpus with a wide-range of topics allows the results of this research to be applied in the most general case. Because developing a complete solution to the authorship attribution problem for chat logs is difficult, we limit our scope to predicting gender and age. The ultimate goal of this work, then, is to facilitate the jobs of law enforcers in tracking down criminals who attempt to use the Internet as a hiding place.
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paper_title: Modeling and Multiway Analysis of Chatroom Tensors

paper_content:

This work identifies the limitations of n-way data analysis techniques in multidimensional stream data, such as Internet chatroom communications data, and establishes a link between data collection and performance of these techniques. Its contributions are twofold. First, it extends data analysis to multiple dimensions by constructing n-way data arrays known as high order tensors. Chatroom tensors are generated by a simulator which collects and models actual communication data. The accuracy of the model is determined by the Kolmogorov-Smirnov goodness-of-fit test which compares the simulation data with the observed (real) data. Second, a detailed computational comparison is performed to test several data analysis techniques including SVD [1], and multiway techniques including TUCKERI, TUCKER3 [2], and PARAFAC [3].
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paper_title: Lexical and Discourse Analysis of Online Chat Dialog

paper_content:

One of the ultimate goals of natural language processing (NLP) systems is understanding the meaning of what is being transmitted, irrespective of the medium (e.g., written versus spoken) or the form (e.g., static documents versus dynamic dialogues). Although much work has been done in traditional language domains such as speech and static written text, little has yet been done in the newer communication domains enabled by the Internet, e.g., online chat and instant messaging. This is in part due to the fact that there are no annotated chat corpora available to the broader research community. The purpose of this research is to build a chat corpus, tagged with lexical (token part-of-speech labels), syntactic (post parse tree), and discourse (post classification) information. Such a corpus can then be used to develop more complex, statistical-based NLP applications that perform tasks such as author profiling, entity identification, and social network analysis.
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paper_title: Text Classification Methodologies Applied to Micro-Text in Military Chat

paper_content:

We propose methods to classify lines of military chat, or posts, which contain items of interest. We evaluated several current text categorization and feature selection methodologies on chat posts. Our chat posts are examples of 'micro-text', or text that is generally very short in length, semi-structured, and characterized by unstructured or informal grammar and language. Although this study focused specifically on tactical updates via chat, we believe the findings are applicable to content of a similar linguistic structure. Completion of this milestone is a significant first step in allowing for more complex categorization and information extraction.
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paper_title: Detecting conversing groups of chatters: a model, algorithms, and tests

paper_content:

Chatrooms, for example Internet Relay Chat, are generally multi-user, multi-channel and multiserver chat-systems which run over the Internet and provide a protocol for real-time text-based conferencing between users all over the world. While a well-trained human observer is able to understand who is chatting with whom, there are no efficient and accurate automated tools to determine the groups of users conversing with each other. A precursor to analysing evolving cyber-social phenomena is to first determine what the conversations are and which groups of chatters are involved in each conversation. We consider this problem in this paper. We propose an algorithm to discover all groups of users that are engaged in conversation. Our algorithms are based on a statistical model of a chatroom that is founded on our experience with real chatrooms. Our approach does not require any semantic analysis of the conversations, rather it is based purely on the statistical information contained in the sequence of posts. We improve the accuracy by applying some graph algorithms to clean the statistical information. We present some experimental results which indicate that one can automatically determine the conversing groups in a chatroom, purely on the basis of statistical analysis.
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paper_title: Disentangling Chat

paper_content:

When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately. We refer to this task as disentanglement. We present a corpus of Internet Relay Chat dialogue in which the various conversations have been manually disentangled, and evaluate annotator reliability. We propose a graph-based clustering model for disentanglement, using lexical, timing, and discourse-based features. The model's predicted disentanglements are highly correlated with manual annotations. We conclude by discussing two extensions to the model, specificity tuning and conversation start detection, both of which are promising but do not currently yield practical improvements.
---
paper_title: Digesting Virtual "Geek" Culture: The Summarization Of Technical Internet Relay Chats

paper_content:

This paper describes a summarization system for technical chats and emails on the Linux kernel. To reflect the complexity and sophistication of the discussions, they are clustered according to subtopic structure on the sub-message level, and immediate responding pairs are identified through machine learning methods. A resulting summary consists of one or more mini-summaries, each on a subtopic from the discussion.
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paper_title: Recognizing connotative meaning in military chat communications

paper_content:

Over the last five to seven years the use of chat in military contexts has expanded quite significantly, in some cases ::: becoming a primary means of communicating time-sensitive data to decision makers and operators. For example, during ::: humanitarian operations with Joint Task Force-Katrina, chat was used extensively to plan, task, and coordinate predeployment ::: and ongoing operations. The informal nature of chat communications allows the relay of far more ::: information than the technical content of messages. Unlike formal documents such as newspapers, chat is often emotive. ::: "Reading between the lines" to understand the connotative meaning of communication exchanges is now feasible, and ::: often important. Understanding the connotative meaning of text is necessary to enable more useful automatic ::: intelligence exploitation. The research project described in this paper was directed at recognizing user connotations of ::: uncertainty and urgency. The project built a matrix of speech features indicative of these categories of meaning, ::: developed data mining software to recognize them, and evaluated the results.
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paper_title: Automatic Author Profiling of Online Chat Logs

paper_content:

Abstract : Now that the Internet has become easily accessible and more affordable, a larger number of people spend more time in front of a computer. Some spend so much time on the Internet that they develop virtual friendships and relationships people with whom they have regular contact via a computer screen and the Internet. While most of the dialogue exchanged online is not harmful or illegal, there are those with dishonest intentions lurking online. These people can be breaking the law by seducing a minor virtually or even going as far as meeting a minor in person. Terrorists can also use the Internet to facilitate communication and plan attacks. Since e-mail is one of the original means of communication on the Internet, methods for determining the author of an e-mail have already been studied. So far, however, no significant experimentation with online chat logs exists. The first of part of this study is comprised of generating an unbiased, random, and broad corpus of online chat logs. Having a general corpus with a wide-range of topics allows the results of this research to be applied in the most general case. Because developing a complete solution to the authorship attribution problem for chat logs is difficult, we limit our scope to predicting gender and age. The ultimate goal of this work, then, is to facilitate the jobs of law enforcers in tracking down criminals who attempt to use the Internet as a hiding place.
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paper_title: Posting Act Tagging Using Transformation-Based Learning

paper_content:

In this article we present the application of transformation-based learning (TBL) [1] to the task of assigning tags to postings in online chat conversations. We define a list of posting tags that have proven useful in chat-conversation analysis. We describe the templates used for posting act tagging in the context of template selection. We extend traditional approaches used in part-of-speech tagging and dialogue act tagging by incorporating regular expressions into our templates. We close with a presentation of results that compare favorably with the application of TBL in dialogue act tagging.
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paper_title: Thread detection in dynamic text message streams

paper_content:

Text message stream is a newly emerging type of Web data which is produced in enormous quantities with the popularity of Instant Messaging and Internet Relay Chat. It is beneficial for detecting the threads contained in the text stream for various applications, including information retrieval, expert recognition and even crime prevention. Despite its importance, not much research has been conducted so far on this problem due to the characteristics of the data in which the messages are usually very short and incomplete. In this paper, we present a stringent definition of the thread detection task and our preliminary solution to it. We propose three variations of a single-pass clustering algorithm for exploiting the temporal information in the streams. An algorithm based on linguistic features is also put forward to exploit the discourse structure information. We conducted several experiments to compare our approaches with some existing algorithms on a real dataset. The results show that all three variations of the single-pass algorithm outperform the basic single-pass algorithm. Our proposed algorithm based on linguistic features improves the performance relatively by 69.5% and 9.7% when compared with the basic single-pass algorithm and the best variation algorithm in terms of F1 respectively.
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paper_title: Topic Detection and Tracking Pilot Study Final Report

paper_content:

Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative to investigate the state of the art in finding and following new events in a stream of broadcast news stories. The TDT problem consists of three major tasks: (1) segmenting a stream of data, especially recognized speech, into distinct stories; (2) identifying those news stories that are the first to discuss a new event occurring in the news; and (3) given a small number of sample news stories about an event, finding all following stories in the stream.
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paper_title: ChatTrack: Chat Room Topic Detection Using Classification

paper_content:

The traditional analysis of Internet chat room discussions places a resource burden on the intelligence community because of the time required to monitor thousands of continuous chat sessions. Chat rooms are used to discuss virtually any subject, including computer hacking and bomb making, creating a virtual sanctuary for criminals to collaborate. Given the upsurge of interest in homeland security issues, we have developed a text classification system that creates a concept-based profile that represents a summary of the topics discussed in a chat room or by an individual participant. We then discuss this basic chat profiling system and demonstrate the ability to selectively augment the standard concept database with new concepts of significance to an agent. Finally, we show how an investigator can, once alerted to a user or session of interest via the profile, retrieve details about the chat session through our chat archiving and search system.
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paper_title: Visualisation of Learners' Contributions in Chat Conversations

paper_content:

In this paper is presented a novel dialogistic, socio-cultural perspective and an associated software tool, which provide structured visualisation and analysis means of Computer Supported Collaborative Learning chat conversations. The implemented tools use knowledge-based techniques and are based on Bakhtin's dialogistic paradigm. They visualize the threading of topics and utterances in the conversation and the contributions of the participants in collaborative learning during instant messenger chats. Natural language processing based on the WordNet lexical ontology and semantic distances are used for detecting topics in the chat and their threading. The experiments with the developed application were performed with students at a course on Human-Computer Interaction in Bucharest Politehnica University.
---
paper_title: Normalization of Chinese chat language

paper_content:

Real-time communication platforms such as ICQ, MSN and online chat rooms are getting more popular than ever on the Internet. There are, however, real risks where criminals and terrorists can perpetrate illegal and criminal abuses. This highlights the security significance of accurate detection and translation of the chat language to its stand language counterpart. The language used on these platforms differs significantly from the standard language. This language, referred to as chat language, is comparatively informal, anomalous and dynamic. Such features render conventional language resources such as dictionaries, and processing tools such as parsers ineffective. In this paper, we present the NIL corpus, a chat language text collection annotated to facilitate training and testing of chat language processing algorithms. We analyse the NIL corpus to study the linguistic characteristics and contextual behaviour of a chat language. First we observe that majority of the chat terms, i.e. informal words in a chat text, is formed by phonetic mapping. We then propose the eXtended Source Channel Model (XSCM) for the normalization of the chat language, which is a process to convert messages expressed in a chat language to its standard language counterpart. Experimental results indicate that the performance of XSCM in terms of chat term recognition and normalization accuracy is superior to its Source Channel Model (SCM) counterparts, and is also more consistent over time.
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paper_title: Topic Detection and Extraction in Chat

paper_content:

Internet-based Chat environments such as Internet relay Chat and instant messaging pose a challenge for data mining and information retrieval systems due to the multi-threaded, overlapping nature of the dialog and the nonstandard usage of language. In this paper we present preliminary methods of topic detection and topic thread extraction that augment a typical TF-IDF-based vector space model approach with temporal relationship information between posts of the Chat dialog combined with WordNet hypernym augmentation. We show results that promise better performance than using only a TF-IDF bag-of-words vector space model.
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paper_title: Detecting Age in Online Chat

paper_content:

Abstract : Over 90 % of teens in the United States use the Internet, and many use it for social interaction. Due to the faceless nature of digital communication, criminals can easily pose as legitimate users to build friendship and trust with potential victims. Even though fewer youths are going to chat rooms and talking to people they do not know, the number of youths receiving aggressive solicitations for offline contact has not declined. Most sexual solicitations go unreported to law enforcement and parents. Though it is a crime for an adult to sexually exploit a minor, it is not always a crime for teens to solicit other teens. It would be of great help to law enforcement agencies if they could automatically detect adults soliciting teens versus teens soliciting other teens in online chat. This study analyzes the effectiveness of different machine learning techniques to distinguish chat conversation by teens and adults. Using proposed techniques, we classified teen and adult conversations with an accuracy of 86 %. The goal of this research is to build an automatic recognition system of adults conversing with teens, capable of detecting predators and alerting agencies or parents of possible inappropriate conversations.
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paper_title: The role and identification of dialog acts in online chat

paper_content:

In recent years, online chat has become a dominant mode of communication. This text-based medium has the potential of improving information awareness within an organization, but only if the critical information within messages can be identified and directed to where it is most needed. Such a goal has many challenges that traditional Information Extraction (IE) approaches have rarely addressed: the text is "dirty" (containing typos, misspellings, sparse punctuation, etc.), messages are fragmented and refer implicitly to previous messages and shared knowledge, messages from multiple topics are interleaved, etc. ::: ::: Past work in conversation analysis has included in-depth discussions of dialog acts, i.e., the individual utterances that comprise conversations. This paper describes how dialog acts within online chat differ from those within two-person voice conversations. It then presents methods for identifying dialog acts and the role that dialog acts play in identifying individual conversations within a chat stream. Identifying conversations is a necessary step for extracting actionable information, such as identifying individuals with specific expertise, recognizing reports of offline activities, and alerting decision makers to critical developments. Finally, we describe Chat-IE, a prototype software system that performs live dialog identification on chat streams.
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paper_title: Modeling socio-cultural phenomena in online multi-party discourse

paper_content:

We present in this paper, the application of a novel approach to computational modeling, understanding and detection of social phenomena in online multiparty discourse. A two-tiered approach was developed to detect a collection of social phenomena deployed by participants, such as topic control, task control, disagreement and involvement. We discuss how the mid-level social phenomena can be reliably detected in discourse and these measures can be used to differentiate participants of online discourse. Our approach works across different types of online chat and we show results on two specific data sets.
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paper_title: Using Speech Act Profiling for Deception Detection

paper_content:

The rising use of synchronous text-based computer-mediated communication (CMC) such as chat rooms and instant messaging in government agencies and the business world presents a potential risk to these organizations. There are no current methods for visualizing or analyzing these persistent conversations to detect deception. Speech act profiling is a method for analyzing and visualizing online conversations, and this paper shows its use for distinguishing online conversations that express uncertainty, an indicator of deception.
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paper_title: Lexical and Discourse Analysis of Online Chat Dialog

paper_content:

One of the ultimate goals of natural language processing (NLP) systems is understanding the meaning of what is being transmitted, irrespective of the medium (e.g., written versus spoken) or the form (e.g., static documents versus dynamic dialogues). Although much work has been done in traditional language domains such as speech and static written text, little has yet been done in the newer communication domains enabled by the Internet, e.g., online chat and instant messaging. This is in part due to the fact that there are no annotated chat corpora available to the broader research community. The purpose of this research is to build a chat corpus, tagged with lexical (token part-of-speech labels), syntactic (post parse tree), and discourse (post classification) information. Such a corpus can then be used to develop more complex, statistical-based NLP applications that perform tasks such as author profiling, entity identification, and social network analysis.
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paper_title: Disentangling Chat with Local Coherence Models

paper_content:

We evaluate several popular models of local discourse coherence for domain and task generality by applying them to chat disentanglement. Using experiments on synthetic multiparty conversations, we show that most models transfer well from text to dialogue. Coherence models improve results overall when good parses and topic models are available, and on a constrained task for real chat data.
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paper_title: Plans Toward Automated Chat Summarization

paper_content:

We describe the beginning stages of our work on summarizing chat, which is motivated by our observations concerning the information overload of US Navy watchstanders. We describe the challenges of summarizing chat and focus on two chat-specific types of summarizations we are interested in: thread summaries and temporal summaries. We then discuss our plans for addressing these challenges and evaluation issues.
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paper_title: A Tool for Internet Chatroom Surveillance

paper_content:

Internet chatrooms are common means of interaction and communications, and they carry valuable information about formal or ad-hoc formation of groups with diverse objectives. This work presents a fully automated surveillance system for data collection and analysis in Internet chatrooms. The system has two components: First, it has an eavesdropping tool which collects statistics on individual (chatter) and chatroom behavior. This data can be used to profile a chatroom and its chatters. Second, it has a computational discovery algorithm based on Singular Value Decomposition (SVD) to locate hidden communities and communication patterns within a chatroom. The eavesdropping tool is used for fine tuning the SVD-based discovery algorithm which can be deployed in real-time and requires no semantic information processing. The evaluation of the system on real data shows that (i) statistical properties of different chatrooms vary significantly, thus profiling is possible, (ii) SVD-based algorithm has up to 70-80% accuracy to discover groups of chatters.
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paper_title: Conversation Extraction in Dynamic Text Message Stream

paper_content:

Text message stream which is produced by Instant Messager and Internet Relay Chat poses interesting and challenging problems for information technologies. It is beneficial to extract the conversations in this kind of chatting message stream for information management and knowledge finding. However, the data in text message stream are usually very short and incomplete, and it requires efficiency to monitor thousands of continuous chat sessions. Many existing text mining methods encounter challenges. This paper focuses on the conversation extraction in dynamic text message stream. We design the dynamic representation for messages to combine the text content information and linguistic feature in message stream. A memory structure of reversed maximal similar relationship is developed for renewable assignments when grouping messages into conversations. We finally propose a double time window algorithm based on above methods to extract conversations in dynamic text message stream. Experiments on a real dataset shows that our method outperforms two baseline methods introduced in a recent related paper about 47% and 15% in terms of F measure respectively.
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paper_title: Context-based Message Expansion for Disentanglement of Interleaved Text Conversations

paper_content:

Computational processing of text exchanged in interactive venues in which participants engage in simultaneous conversations can benefit from techniques for automatically grouping overlapping sequences of messages into separate conversations, a problem known as "disentanglement." While previous methods exploit both lexical and non-lexical information that exists in conversations for this task, the inter-dependency between the meaning of a message and its temporal and social contexts is largely ignored. Our approach exploits contextual properties (both explicit and hidden) to probabilistically expand each message to provide a more accurate message representation. Extensive experimental evaluations show our approach outperforms the best previously known technique.
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paper_title: Combining Topic Models and Social Networks for Chat Data Mining

paper_content:

Informal chat-room conversations have intrinsically different properties from regular static document collections. Noise, concise expressions and dynamic, changing and interleaving nature of discussions make chat data ill-suited for analysis with an off-the-shelf text mining method. On the other hand, interactive human communication has some implicit features which may be used to enhance the results. In our research we infer social network structures from the chat data by using a few basic heuristics. We then present some preliminary results showing that the inferred social graph may be used to enhance topic identification of a chat room when combined with a state-of-the-art topic and classification models. For validation purposes we then compare the performance effects of using this social information in a topic classification task.
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paper_title: Inferring and visualizing social networks on Internet relay chat

paper_content:

Internet relay chat is a system that allows groups of people to collaborate and chat from anywhere in the world. Clearly defined by several RFC documents, it is arguably the most standard real-time chat system currently in use. This work describes a method of inferring the social network of a group of IRC users in a channel. An IRC bot is used to monitor a channel and perform a heuristic analysis of events to create a mathematical approximation of the social network. From this, the bot can produce a visualization of the inferred social network on demand. These visualizations reveal the structure of the social network, highlighting connectivity, clustering and strengths of relationships between users. Animated output allows viewers to see the evolution of the social network over time. Some novel ideas for future work are discussed, showing other useful applications of this system.
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paper_title: Text Classification Methodologies Applied to Micro-Text in Military Chat

paper_content:

We propose methods to classify lines of military chat, or posts, which contain items of interest. We evaluated several current text categorization and feature selection methodologies on chat posts. Our chat posts are examples of 'micro-text', or text that is generally very short in length, semi-structured, and characterized by unstructured or informal grammar and language. Although this study focused specifically on tactical updates via chat, we believe the findings are applicable to content of a similar linguistic structure. Completion of this milestone is a significant first step in allowing for more complex categorization and information extraction.
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paper_title: Disentangling Chat

paper_content:

When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately. We refer to this task as disentanglement. We present a corpus of Internet Relay Chat dialogue in which the various conversations have been manually disentangled, and evaluate annotator reliability. We propose a graph-based clustering model for disentanglement, using lexical, timing, and discourse-based features. The model's predicted disentanglements are highly correlated with manual annotations. We conclude by discussing two extensions to the model, specificity tuning and conversation start detection, both of which are promising but do not currently yield practical improvements.
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paper_title: Conversation trees and threaded chats

paper_content:

Chat programs and instant messaging services are increasingly popular among Internet users. However, basic issues with the interfaces and data structures of most forms of chat limit their utility for use in formal interactions (like group meetings) and decision-making tasks. In this paper, we discuss Threaded Text Chat, a program designed to address some of the deficiencies of current chat programs. Standard forms of chat introduce ambiguity into interaction in a number of ways, most profoundly by rupturing connections between turns and replies. Threaded Chat presents a solution to this problem by supporting the basic turn-taking structure of human conversation. While the solution introduces interface design challenges of its own, usability studies show that users' patterns of interaction in Threaded Chat are equally effective, but different (and possibly more efficient) than standard chat programs.
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paper_title: Alternative interfaces for chat

paper_content:

We describe some common problems experienced by users of computer-based text chat, and show how many of these problems relate to the loss of timing-specific information. We suggest that thinking of chat as a real-time streaming media data type, with status and channel indicators, might solve some of these problems. We then present a number of alternative chat interfaces along with results from user studies comparing and contrasting them both with each other and with the standard chat interface. These studies show some potential, but indicate that more work needs to be done.
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paper_title: Text chat in action

paper_content:

Synchronous text communication is becoming recognized as a valuable workplace communication medium yet some studies of group text chat indicate that its properties can lead to interactional incoherence. We consider this issue through a detailed analytic examination of text chat transcripts by showing how participants manage their interactions through considering multiple threads, turn taking and topic change. We reveal the routine practices that participants employ to create and manage coherent interaction. These practices arise from the turn taking system in operation, which facilitates straightforward repair of misunderstandings. We conclude by considering the implications of this for design and for the organisation and management of interactions of various forms.
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paper_title: Chat circles

paper_content:

Although current online chat environments provide newopportunities for communication, they are quite constrained intheir ability to convey many important pieces of socialinformation, ranging from the number of participants in aconversation to the subtle nuances of expression that enrich faceto face speech. In this paper we present Chat Circles, an abstractgraphical interface for synchronous conversa-tion. Here, presenceand activity are made manifest by changes in color and form,proximity-based filtering intuitively breaks large groups intoconversational clusters, and the archives of a conversation aremade visible through an integrated history interface. Our goal inthis work is to create a richer environment for onlinediscussions.
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paper_title: Annotating Preferences in Chats for Strategic Games

paper_content:

This paper describes an annotation scheme for expressions of preferences in on-line chats concerning bargaining negotiations in the online version of the competitive game Settlers of Catan.
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paper_title: Discourse Structure Analysis of Chat Communication

paper_content:

This article presents a research method called Discourse Structure Analysis (DSA and a software application called ChatLine that supports the analysis of chat transcripts according to DSA. The DSA method is based on manual referencing and automatic analysis of chat transcripts in order to create visualizations and measures of their message and interaction structures. The goal of DSA is to provide a comprehensive and extensible method for the data‑driven analysis of chat logs that can support both qualitative and quantitative investigations of computer‑mediated communication.
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paper_title: Gender, Communication, and Self-Presentation in Teen Chatrooms Revisited: Have Patterns Changed?

paper_content:

This study evaluates empirically the proposition implicit in much recent gender and CMC research that expressions of gender distinctness among teens in online environments are becoming less frequent and less traditional. Gender preferences were analyzed in linguistic features and communication styles in synchronous text messages, along with self-presentation in user profile pictures, drawing on data from popular English-language teen chat sites collected in 2010. Significant differences were found in speech acts, message tone, and in physical stance, dress, and social distance in profile pictures that generally conform to traditional gender stereotypes. These findings are interpreted in light of previous gender and teen CMC research, adolescent development and socialization patterns, mass media representations, and trends towards media convergence in chat platforms.
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paper_title: GENDER ENCODING OF TYPOGRAPHICAL ELEMENTS IN LITHUANIAN AND CROATIAN IRC

paper_content:

Much sociolinguistic research discusses differences between male and female language. In this paper we consider the language of Internet Relay Chat (IRC), studying if and how non-standard typography depends on the gender of the writer. We distinguish two kinds of non-standard typography that are typical of IRC users: technology-conditioned and voluntary. Our results show that use of non-standard writing variants depends on writer gender in both cases. Comparable results are found for Lithuanian and Croatian chat.
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paper_title: Code choice and code-switching in Swiss-German Internet Relay Chat rooms

paper_content:

In the German-speaking regions of Switzerland, dialect is spoken by all social groups in most communicative situations, Standard German being used only when prescribed. Swiss dialects rarely appeared in written form before the 1980s, apart from the genre of dialect literature. Due to the growing acceptance of informal writing styles in many European languages, dialect is increasingly employed for written personal communication, in particular in computer-mediated communication (CMC). In Swiss Internet Relay Chat (IRC) rooms, varieties of German are used side by side as all chatters have a command of both standard and dialectal varieties. Depending on the channel, the proportion of dialectal contributions can be as high as 90 percent. The choice of a particular variety depends on both individual preference and on the predominant variety used within a specific thread. In this paper I take a quantitative approach to language variation in IRC and demonstrate how such an approach can help embed qualitative research on code-switching in CMC.
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paper_title: Gender and Turn Allocation in a Thai Chat Room

paper_content:

This paper analyzes gender in relation to turn allocation in a popular Thai chat room on the World Wide Web. We analyze turn-taking and response patterns in light of Sacks, Schegloff and Jefferson's (1974) model of turn allocation in face-to-face conversation, taking into consideration the independent variable of participant gender. We also analyze use of, and responses to, flirtation in the chat room. Our results show that females participate more often and receive a higher rate of response from both females and males. Males, who are in the minority, must work harder to take the floor, even in their attempted flirtatious interactions. These results suggest that gender interacts with culture online in complex ways: Contrary to previous findings on gender in chat rooms, and contrary to culturally-based expectations about the subordinate status of Thai women, females appear to be relatively empowered in the Thai chat room studied here, as assessed through turn allocation patterns.
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paper_title: Language variation on Internet Relay Chat: A social network approach†

paper_content:

This paper examines linguistic variation on an Internet Relay Chat channel with respect to the hypothesis, based on the model of Milroy and Milroy (1992), that standard variants tend to be associated with weak social network ties, while vernacular variants are associated with strong network ties. An analysis of frequency of contact as a measure of tie strength reveals a structured relationship between tie strength and several linguistic variants. However, the variant features are associated with social positions in a way that does not correlate neatly with tie strength. An account of these results is proposed in terms of the social functions of the different variables and the larger social context of IRC affecting tie strength.
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paper_title: Visualisation of Learners' Contributions in Chat Conversations

paper_content:

In this paper is presented a novel dialogistic, socio-cultural perspective and an associated software tool, which provide structured visualisation and analysis means of Computer Supported Collaborative Learning chat conversations. The implemented tools use knowledge-based techniques and are based on Bakhtin's dialogistic paradigm. They visualize the threading of topics and utterances in the conversation and the contributions of the participants in collaborative learning during instant messenger chats. Natural language processing based on the WordNet lexical ontology and semantic distances are used for detecting topics in the chat and their threading. The experiments with the developed application were performed with students at a course on Human-Computer Interaction in Bucharest Politehnica University.
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paper_title: Interaction, facilitation, and deep learning in cross-cultural chat: A case study

paper_content:

Abstract This case study evaluates the potential of synchronous chat for deep learning in the context of a distance education program between two universities in different cultural contexts, with a focus on interaction and facilitation. Three rubrics—functional moves, social construction of knowledge, and teaching presence—were applied in a longitudinal content analysis of chat sessions between four adult learners in Azerbaijan and their two facilitators in the U.S. The findings reveal that although the quality of the interaction was limited by the nature of the task, language difficulties, and differing cultural expectations about instruction, conceptual negotiative activity increased over time. In conjunction with previous research on constructivist learning, these results support the view that synchronous chat can facilitate deep learning, but also suggest that the cost may outweigh the benefits, especially when language and cultural barriers must be overcome.
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paper_title: Thread-based analysis of patterns of collaborative interaction in chat

paper_content:

In this work we present a thread-based approach for analyzing synchronous collaborative math problem solving activities. Thread information is shown to be an important resource for analyzing collaborative activities, especially for conducting sequential analysis of interaction among participants of a small group. We propose a computational model based on thread information which allows us to identify patterns of interaction and their sequential organization in computer-supported collaborative environments. This approach enables us to understand important features of collaborative math problem solving in a chat environment and to envisage several useful implications for educational and design purposes.
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paper_title: Categorization of Text Chat Communication between Learners and Native Speakers of Japanese

paper_content:

ABSTRACT A number of CALL studies suggest the potential benefits of network-based communication for L2 acquisition, focusing on its nature for inducing negotiation of meaning. This study examined negotiation of meaning that took place between students and native speakers of Japanese over a series of chat conversations and attempted to categorize the difficulties encountered. The data showed that the difficulties in understanding each other did indeed trigger negotiation of meaning between students even when no specific communication tasks were given. Using discourse analysis methods, the negotiations were sorted into nine categories according to the causes of the difficulties: recognition of new word, misuse of word, pronunciation error, grammatical error, inappropriate segmentation, abbreviated sentence, sudden topic change, slow response, and inter-cultural communication gap. Through the examination of these categories of negotiation, it was found that there were some language aspects that are crucial for communication but that had been neglected in teaching, and that students would not have noticed if they had not had the opportunity to chat with native speakers. In light of these findings, the authors make pedagogical recommendations on some classroom tasks for improving chat conversations. INTRODUCTION As a result of technological innovations, new types of communication, namely network-based communication, have emerged. These new technologies, e-mail and chat in particular, are being used increasingly in second/foreign language (L2) learning environments. Many researchers regard this type of communication as a promising tool for language learning, as it allows learners to interact with native speakers from the country where their target language is spoken. Previous research suggests that it increases learners' opportunities to use the target language (Barson, Frommer, & Schwartz, 1993), induces a series of negotiations of meaning (Blake, 2000), and improves the quality of written and spoken language (Sotillo, 2000). SLA Theories Second language acquisition (SLA) theories advocate that oral interaction that requires negotiation of meaning is necessary for enhancing learners' interlanguage (Ellis, 1985; Long, 1991; Pica, 1994; Swain, 1993, 1995). Negotiation of meaning is defined as "modification and restructuring of interaction that occurs when learners and their interlocutors anticipate, perceive, or experience difficulties in message comprehensibility" (Pica, 1994, p. 495). Modification and restructuring include repetitions, confirmations, reformulations, comprehension checks, recasts, confirmation checks, and clarification requests (Long, 1996). The Interaction Hypothesis (Gass, 1997; Long, 1996, 1991) claims that resolving miscommunication (negotiation of meaning) enhances L2 learning, as it provides more opportunities for comprehensible input and modified output. The Output Hypothesis (Swain, 1993, 1995) explains that producing output is one way of testing a hypothesis about comprehensibility or linguistic well-formedness (Swain, 1995, p. 126), and that learners' hypothesis testing often invokes interaction between the learners and their interlocutor(s). Native speaker difficulties in following learners' interlanguage may trigger feedback, which in return may induce changes in the learners' output. Negotiation of meaning also occurs on occasions where the native speakers' input is above the learners' threshold level of understanding. Learners may notice a gap between their interlanguage and the language that native speakers produce, and may request clarification. The output hypothesis thus claims that the output induces negotiation of meaning and the negotiation leads to the enhancement of the learners' interlanguage. CALL Studies on Negotiation of Meaning Many CALL researchers postulate that network-based communication can facilitate second language acquisition in a similar fashion to face-to-face negotiations in classroom settings, and have found abundant evidence of comprehensible input and modified output resulting from negotiation of meaning (Blake, 2000; Kitade, 2000; Pellettieri, 2000; Warschauer, 1998). …
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paper_title: CONVERSATIONS-- AND NEGOTIATED INTERACTION-- IN TEXT AND VOICE CHAT ROOMS

paper_content:

Despite the expanded use of the Internet for language learning and practice, little attention if any has been given to the quality of interaction among English L2 speakers in conversational text or voice chat rooms. This study explored the patterns of repair moves in synchronous non-native speaker (NNS) text chat rooms in comparison to voice chat rooms on the Internet. The following questions were posed: (a) Which types of repair moves occur in text and voice chats; and (b) what are the differences, if any, between the repair moves in text chats and voice chats when time is held constant? Repair moves made by anonymous NNSs in 10, 5-minute, synchronous chat room sessions (5 text-chat sessions, 5 voice-chat sessions) were counted and analyzed using chi-square with alpha set at .05. Significant differences were found between the higher number of total repair moves made in voice chats and the smaller number in text chats. Qualitative data analysis showed that repair work in voice chats was often pronunciation-related. The study includes discussion that may affect teachers' and learners' considerations of the value of NNS chat room interaction for second language development.
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paper_title: Recognizing connotative meaning in military chat communications

paper_content:

Over the last five to seven years the use of chat in military contexts has expanded quite significantly, in some cases ::: becoming a primary means of communicating time-sensitive data to decision makers and operators. For example, during ::: humanitarian operations with Joint Task Force-Katrina, chat was used extensively to plan, task, and coordinate predeployment ::: and ongoing operations. The informal nature of chat communications allows the relay of far more ::: information than the technical content of messages. Unlike formal documents such as newspapers, chat is often emotive. ::: "Reading between the lines" to understand the connotative meaning of communication exchanges is now feasible, and ::: often important. Understanding the connotative meaning of text is necessary to enable more useful automatic ::: intelligence exploitation. The research project described in this paper was directed at recognizing user connotations of ::: uncertainty and urgency. The project built a matrix of speech features indicative of these categories of meaning, ::: developed data mining software to recognize them, and evaluated the results.
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paper_title: Taming Multiple Chat Room Collaboration: Real-Time Visual Cues to Social Networks and Emerging Threads

paper_content:

Distributed teams increasingly rely on collaboration environments, typically including chat, to link diverse experts for real time information sharing and decision-making. Current chat-based technologies enable easy exchange of information, but don’t focus on managing those information exchanges. Important cues that guide face-to-face collaboration are either lost or missing. In some military environments, operators may juggle over a dozen chat rooms in order to collaborate on complex missions. This often leads to confusion, overload, miscommunication and delayed decisions. Our technology supports chat management. A summary display bar reduces the number of chat rooms operators need open by providing high level situational awareness pointers, in real-time, to: a) rooms with increasing message activity levels, b) rooms in which important collaborators are participating (those in the operator’s social network), and c) rooms in which operator-selected keywords are used. This ability to peripherally monitor less critical chat rooms reduces operator overload, while enhancing the ability to rapidly detect important emerging discussion threads.
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paper_title: Untangling topic threads in chat-based communication: a case study

paper_content:

Analyzing chat traffic has important applications for both the military and the civilian world. This paper presents a case study of a real-world application of chat analysis in support of team training exercise in the military. It compares the results of an unsupervised learning approach with those of a supervised classification approach. The paper also discusses some of the specific challenges presented by this domain.
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paper_title: Military Textual Analysis and Chat Research

paper_content:

Military use of chat, as a grassroots technology, has become so widespread in the last fifteen years that Joint communities have just begun to scratch the surface of this robust communications technology. Historically, IRC room-based chat has been the preferred paradigm for tactical users, due to its ease of use and robustness on high-jitter networks. Migration to XMPP-based chat is now being discussed for a way forward, though this change in protocol raises many questions for the current concept of operations (CONOPS), especially for edge, tactical, users. This special session will focus on current research being performed within DoD to analyze current usage of chat, in order to assist Fleet users both today and in the future.
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paper_title: Military Situation Awareness: Facilitating Critical Event Detection in Chat

paper_content:

Chat has become a primary means of communication for military command and control decision makers. One of the most important aspects of military chat use is the ability to detect critical events quickly and accurately, a task often complicated by the large number of chat messages received during actual operations. The primary goal of this research was to identify factors that enable chat watchstanders to more rapidly identify the critical information embedded in chat messages. These factors, which include message highlighting and chatroom layout, have the potential to enhance the effectiveness and efficiency of information processing in fast-paced, information-rich operational environments. In this study, message highlighting techniques enabled users to detect 92% of highlighted events, in comparison to 83% of events that were not highlighted. These findings indicate that highlighting chat messages confers significant performance advantages, especially in conjunction with associated factors, such as chatr...
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paper_title: Real-Time Online Communications: 'Chat' Use in Navy Operations

paper_content:

Abstract : The ability to conduct a real-time conversation online has become a ubiquitous component of today's communications environment. Commonly referred to as chat or instant messaging, this mode of communication provides users with a format that facilitates multi-tasking conversation with other duties. Chat has also become a critical tool in military command and control, as evidenced by its extensive use during both Operation Enduring Freedom (OEF) and Operation Iraqi Freedom (OIF). A recent survey of fleet users during OIF provided detailed information and insights about chat usage patterns and warfighter requirements for chat tools. The results revealed positive and negative issues related to chat use in an operational environment. Issues included the need to monitor multiple chat rooms simultaneously; the lack of consistent business rules; requirements for chat rooms with different access privileges; and requirements for chat histories. The ability to work near real-time with peers is perceived to be a tremendous boost to productivity and greatly facilitates communication, cooperation, and coordination. This paper discusses the results of the survey and highlights areas where human factors and technology interventions may be able to facilitate chat use within US military command and control.
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paper_title: The Impact of Synchronous Text-Based Chat on Military Command and Control

paper_content:

Abstract : This research assesses the impact of synchronous (real-time), text-based chat on military command and control (C2) processes. Chat use among the services, particularly among joint forces, has evolved in ad hoc fashion to fill gaps in currently fielded C2 systems. This growth-by- improvisation inhibits clear definition of the underlying requirements: precisely what C2 deficiencies are being addressed by text-based chat tools? Or, from a bottom-up perspective: what capabilities do text-based chat tools bring to the war fighter? In this study we employ a broad set of use-cases to further refine why operators use chat based on how they apply chat to their specific combat problems. These use cases include ongoing combat operations in ENDURING FREEDOM, counter-insurgency operations in IRAQI FREEDOM, and disaster relief operations with Joint Task Force - Katrina. The focus of this study is on establishing operators perceived requirements in light of the current capabilities delivered by the existing text-based chat tools. From these reverse-engineered requirements we propose future work to establish these communication capabilities in the next-generation C2 systems.
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paper_title: After Action Review Tools For Team Training with Chat Communications

paper_content:

As networked digital communications proliferate in military operational command and control, chat messaging is emerging as a preferred communications method for team coordination. In areas where chat messaging provides fundamental support to command and control processes, training methods must incorporate techniques to associate and analyze chat room content to determine effectiveness of the communications. Chat room logs provide a rich source of data for analysis in after action reviews affording considerable insight into the decision-making processes among the training audience. They are also relatively unstructured and replete with competing lexicons, abbreviations, and shortcuts. The employment of multiple chat rooms and multiple interleaved dialogs within them introduces high likelihood of missed or misinterpreted communication. This presents a number of challenges for near-real-time analysis. Recent joint-service guidance in chat room protocols and structures opens new opportunities to revisit investigation of techniques for near-real-time feedback in training programs. In this paper, we describe a research effort to develop chat analysis and filtering methods for after action review tools. The investigation focuses on operational planners tackling time-sensitive problems by employing chat communication for both intelligence assessment and mission planning coordination among a diverse set of expert domains. Our research employs combinations of sorting capabilities using organizational and temporal context given by the new joint service guidance, keyword filtering techniques, and informed analysis considering statistically paired dialog participants. These techniques, when combined with visual timeline based presentation of scenario ground-truth and key milestones in the planning processes, promise to provide a more cogent and effective use of time in after action reviews.
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paper_title: DoD Collaboration and Chat Systems: Current Status and Way Ahead

paper_content:

The Department of Defense (DoD) has committed to global distributed operations as the primary method to engage in assigned missions worldwide, including crisis management, humanitarian assistance, and stability operations. In order to "command and control" (C2), this large body of dispersed personnel, DoD uses a variety of electronic methods for managing and updating its forces. This paper provides a broad overview of the major collaboration and chat systems prevalent in the DoD C2 sector (as opposed to other DoD communities of interest). Variables unique to distributed computing environments impact a user's success at effectively using collaboration tools in dispersed operations. Workforce globalization-our evolution towards a fully-netted force-where distributed personnel have the ability to execute their collaborative missions, supported by the right information in a timely manner, drives research, development, test and evaluation activities within the SSCSD laboratory. This has givenus unique insight into determining effective Web3.0 strategies.
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paper_title: Conversation Extraction in Dynamic Text Message Stream

paper_content:

Text message stream which is produced by Instant Messager and Internet Relay Chat poses interesting and challenging problems for information technologies. It is beneficial to extract the conversations in this kind of chatting message stream for information management and knowledge finding. However, the data in text message stream are usually very short and incomplete, and it requires efficiency to monitor thousands of continuous chat sessions. Many existing text mining methods encounter challenges. This paper focuses on the conversation extraction in dynamic text message stream. We design the dynamic representation for messages to combine the text content information and linguistic feature in message stream. A memory structure of reversed maximal similar relationship is developed for renewable assignments when grouping messages into conversations. We finally propose a double time window algorithm based on above methods to extract conversations in dynamic text message stream. Experiments on a real dataset shows that our method outperforms two baseline methods introduced in a recent related paper about 47% and 15% in terms of F measure respectively.
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paper_title: Context-based Message Expansion for Disentanglement of Interleaved Text Conversations

paper_content:

Computational processing of text exchanged in interactive venues in which participants engage in simultaneous conversations can benefit from techniques for automatically grouping overlapping sequences of messages into separate conversations, a problem known as "disentanglement." While previous methods exploit both lexical and non-lexical information that exists in conversations for this task, the inter-dependency between the meaning of a message and its temporal and social contexts is largely ignored. Our approach exploits contextual properties (both explicit and hidden) to probabilistically expand each message to provide a more accurate message representation. Extensive experimental evaluations show our approach outperforms the best previously known technique.
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paper_title: Combining Topic Models and Social Networks for Chat Data Mining

paper_content:

Informal chat-room conversations have intrinsically different properties from regular static document collections. Noise, concise expressions and dynamic, changing and interleaving nature of discussions make chat data ill-suited for analysis with an off-the-shelf text mining method. On the other hand, interactive human communication has some implicit features which may be used to enhance the results. In our research we infer social network structures from the chat data by using a few basic heuristics. We then present some preliminary results showing that the inferred social graph may be used to enhance topic identification of a chat room when combined with a state-of-the-art topic and classification models. For validation purposes we then compare the performance effects of using this social information in a topic classification task.
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paper_title: Normalization of Chinese chat language

paper_content:

Real-time communication platforms such as ICQ, MSN and online chat rooms are getting more popular than ever on the Internet. There are, however, real risks where criminals and terrorists can perpetrate illegal and criminal abuses. This highlights the security significance of accurate detection and translation of the chat language to its stand language counterpart. The language used on these platforms differs significantly from the standard language. This language, referred to as chat language, is comparatively informal, anomalous and dynamic. Such features render conventional language resources such as dictionaries, and processing tools such as parsers ineffective. In this paper, we present the NIL corpus, a chat language text collection annotated to facilitate training and testing of chat language processing algorithms. We analyse the NIL corpus to study the linguistic characteristics and contextual behaviour of a chat language. First we observe that majority of the chat terms, i.e. informal words in a chat text, is formed by phonetic mapping. We then propose the eXtended Source Channel Model (XSCM) for the normalization of the chat language, which is a process to convert messages expressed in a chat language to its standard language counterpart. Experimental results indicate that the performance of XSCM in terms of chat term recognition and normalization accuracy is superior to its Source Channel Model (SCM) counterparts, and is also more consistent over time.
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paper_title: Probability scoring for spelling correction

paper_content:

This paper describes a new program, CORRECT, which takes words rejected by the Unix® SPELL program, proposes a list of candidate corrections, and sorts them by probability score. The probability scores are the novel contribution of this work. They are based on a noisy channel model. It is assumed that the typist knows what words he or she wants to type but some noise is added on the way to the keyboard (in the form of typos and spelling errors). Using a classic Bayesian argument of the kind that is popular in recognition applications, especially speech recognition (Jelinek, 1985), one can often recover the intended correction,c, from a typo,t, by finding the correctionc that maximizesPr(c) Pr(t/c). The first factor,Pr(c), is a prior model of word probabilities; the second factor,Pr(t/c), is a model of the noisy channel that accounts for spelling transformations on letter sequences (insertions, deletions, substitutions and reversals). Both sets of probabilities were estimated using data collected from the Associated Press (AP) newswire over 1988 and 1989 as a training set. The AP generates about 1 million words and 500 typos per week.
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paper_title: Disentangling Chat with Local Coherence Models

paper_content:

We evaluate several popular models of local discourse coherence for domain and task generality by applying them to chat disentanglement. Using experiments on synthetic multiparty conversations, we show that most models transfer well from text to dialogue. Coherence models improve results overall when good parses and topic models are available, and on a constrained task for real chat data.
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paper_title: Thread detection in dynamic text message streams

paper_content:

Text message stream is a newly emerging type of Web data which is produced in enormous quantities with the popularity of Instant Messaging and Internet Relay Chat. It is beneficial for detecting the threads contained in the text stream for various applications, including information retrieval, expert recognition and even crime prevention. Despite its importance, not much research has been conducted so far on this problem due to the characteristics of the data in which the messages are usually very short and incomplete. In this paper, we present a stringent definition of the thread detection task and our preliminary solution to it. We propose three variations of a single-pass clustering algorithm for exploiting the temporal information in the streams. An algorithm based on linguistic features is also put forward to exploit the discourse structure information. We conducted several experiments to compare our approaches with some existing algorithms on a real dataset. The results show that all three variations of the single-pass algorithm outperform the basic single-pass algorithm. Our proposed algorithm based on linguistic features improves the performance relatively by 69.5% and 9.7% when compared with the basic single-pass algorithm and the best variation algorithm in terms of F1 respectively.
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paper_title: Untangling topic threads in chat-based communication: a case study

paper_content:

Analyzing chat traffic has important applications for both the military and the civilian world. This paper presents a case study of a real-world application of chat analysis in support of team training exercise in the military. It compares the results of an unsupervised learning approach with those of a supervised classification approach. The paper also discusses some of the specific challenges presented by this domain.
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paper_title: Modeling and Multiway Analysis of Chatroom Tensors

paper_content:

This work identifies the limitations of n-way data analysis techniques in multidimensional stream data, such as Internet chatroom communications data, and establishes a link between data collection and performance of these techniques. Its contributions are twofold. First, it extends data analysis to multiple dimensions by constructing n-way data arrays known as high order tensors. Chatroom tensors are generated by a simulator which collects and models actual communication data. The accuracy of the model is determined by the Kolmogorov-Smirnov goodness-of-fit test which compares the simulation data with the observed (real) data. Second, a detailed computational comparison is performed to test several data analysis techniques including SVD [1], and multiway techniques including TUCKERI, TUCKER3 [2], and PARAFAC [3].
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paper_title: Topic Detection and Extraction in Chat

paper_content:

Internet-based Chat environments such as Internet relay Chat and instant messaging pose a challenge for data mining and information retrieval systems due to the multi-threaded, overlapping nature of the dialog and the nonstandard usage of language. In this paper we present preliminary methods of topic detection and topic thread extraction that augment a typical TF-IDF-based vector space model approach with temporal relationship information between posts of the Chat dialog combined with WordNet hypernym augmentation. We show results that promise better performance than using only a TF-IDF bag-of-words vector space model.
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paper_title: Mining Chat-room Conversations for Social and Semantic Interactions

paper_content:

The ephemeral nature of human communication via networks today poses interesting and challenging problems for information technologists. The Intelink intelligence network, for example, has a need to monitor chat-room conversations to ensure the integrity of sensitive data being transmitted via the network. However, the sheer volume of communication in venues such as email, newsgroups, and chat precludes manual techniques of information management. It has been estimated that over 430 million instant messages, for example, are exchanged each day on the America Online network [3]. Although a not insignificant fraction of such data may be temporarily archived (e.g., newsgroups), no systematic mechanisms exist for accumulating these artifacts of communication in a form that lends itself to the construction of models of semantics [12]. In essence, dynamic techniques of analysis are needed if textual data of this nature is to be effectively mined. This article reports our progress in developing a text mining tool for analysis of chat-room conversations. Central to our efforts is the development of functionality to answer questions such as "What topics are being discussed in a chat-room?", "Who is discussing which topics?" and "Who is interacting with whom?" The objective of our research is to develop technology that can automatically identify such patterns of interaction in both social and semantic terms. In this article we report our preliminary findings in identifying threads of conversation in multi-topic, multi-person chat-rooms. We have achieved promising results in terms of precision and recall by employing pattern recognition techniques based on finite state automata. We also report the design of our approach to building models of social and semantic interactions based on our HDDI text mining infrastructure [13].
---
paper_title: Hierarchical Conversation Structure Prediction in Multi-Party Chat

paper_content:

Conversational practices do not occur at a single unit of analysis. To understand the interplay between social positioning, information sharing, and rhetorical strategy in language, various granularities are necessary. In this work we present a machine learning model for multi-party chat which predicts conversation structure across differing units of analysis. First, we mark sentence-level behavior using an information sharing annotation scheme. By taking advantage of Integer Linear Programming and a sociolinguistic framework, we enforce structural relationships between sentence-level annotations and sequences of interaction. Then, we show that clustering these sequences can effectively disentangle the threads of conversation. This model is highly accurate, performing near human accuracy, and performs analysis on-line, opening the door to real-time analysis of the discourse of conversation.
---
paper_title: Bounding and Comparing Methods for Correlation Clustering Beyond ILP

paper_content:

We evaluate several heuristic solvers for correlation clustering, the NP-hard problem of partitioning a dataset given pairwise affinities between all points. We experiment on two practical tasks, document clustering and chat disentanglement, to which ILP does not scale. On these datasets, we show that the clustering objective often, but not always, correlates with external metrics, and that local search always improves over greedy solutions. We use semi-definite programming (SDP) to provide a tighter bound, showing that simple algorithms are already close to optimality.
---
paper_title: Disentangling Chat with Local Coherence Models

paper_content:

We evaluate several popular models of local discourse coherence for domain and task generality by applying them to chat disentanglement. Using experiments on synthetic multiparty conversations, we show that most models transfer well from text to dialogue. Coherence models improve results overall when good parses and topic models are available, and on a constrained task for real chat data.
---
paper_title: Conversation Extraction in Dynamic Text Message Stream

paper_content:

Text message stream which is produced by Instant Messager and Internet Relay Chat poses interesting and challenging problems for information technologies. It is beneficial to extract the conversations in this kind of chatting message stream for information management and knowledge finding. However, the data in text message stream are usually very short and incomplete, and it requires efficiency to monitor thousands of continuous chat sessions. Many existing text mining methods encounter challenges. This paper focuses on the conversation extraction in dynamic text message stream. We design the dynamic representation for messages to combine the text content information and linguistic feature in message stream. A memory structure of reversed maximal similar relationship is developed for renewable assignments when grouping messages into conversations. We finally propose a double time window algorithm based on above methods to extract conversations in dynamic text message stream. Experiments on a real dataset shows that our method outperforms two baseline methods introduced in a recent related paper about 47% and 15% in terms of F measure respectively.
---
paper_title: Context-based Message Expansion for Disentanglement of Interleaved Text Conversations

paper_content:

Computational processing of text exchanged in interactive venues in which participants engage in simultaneous conversations can benefit from techniques for automatically grouping overlapping sequences of messages into separate conversations, a problem known as "disentanglement." While previous methods exploit both lexical and non-lexical information that exists in conversations for this task, the inter-dependency between the meaning of a message and its temporal and social contexts is largely ignored. Our approach exploits contextual properties (both explicit and hidden) to probabilistically expand each message to provide a more accurate message representation. Extensive experimental evaluations show our approach outperforms the best previously known technique.
---
paper_title: Text chat in action

paper_content:

Synchronous text communication is becoming recognized as a valuable workplace communication medium yet some studies of group text chat indicate that its properties can lead to interactional incoherence. We consider this issue through a detailed analytic examination of text chat transcripts by showing how participants manage their interactions through considering multiple threads, turn taking and topic change. We reveal the routine practices that participants employ to create and manage coherent interaction. These practices arise from the turn taking system in operation, which facilitates straightforward repair of misunderstandings. We conclude by considering the implications of this for design and for the organisation and management of interactions of various forms.
---
paper_title: Analysis of C2 and C2-lite micro-message communications

paper_content:

Microtext can be considered one form of micro-messaging which can include non-digital communications which have been digitized. We present our experiences and some results analyzing micro-messages in both military command and control (C2) and more general "C2-Lite" domains in order to predict other information such as the performance of a team, the topics of discussion, or the spread of ideas. In both situations, the data can vary by the breadth of discussion, the number of participants, and the looseness of protocol. In all cases, organizing the data, combining temporal or network analysis with increasingly statistical content analyses is critical. The underlying similarities imply that analyses created for one domain can be applied to the others.
---
paper_title: Detecting conversing groups of chatters: a model, algorithms, and tests

paper_content:

Chatrooms, for example Internet Relay Chat, are generally multi-user, multi-channel and multiserver chat-systems which run over the Internet and provide a protocol for real-time text-based conferencing between users all over the world. While a well-trained human observer is able to understand who is chatting with whom, there are no efficient and accurate automated tools to determine the groups of users conversing with each other. A precursor to analysing evolving cyber-social phenomena is to first determine what the conversations are and which groups of chatters are involved in each conversation. We consider this problem in this paper. We propose an algorithm to discover all groups of users that are engaged in conversation. Our algorithms are based on a statistical model of a chatroom that is founded on our experience with real chatrooms. Our approach does not require any semantic analysis of the conversations, rather it is based purely on the statistical information contained in the sequence of posts. We improve the accuracy by applying some graph algorithms to clean the statistical information. We present some experimental results which indicate that one can automatically determine the conversing groups in a chatroom, purely on the basis of statistical analysis.
---
paper_title: Disentangling Chat

paper_content:

When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately. We refer to this task as disentanglement. We present a corpus of Internet Relay Chat dialogue in which the various conversations have been manually disentangled, and evaluate annotator reliability. We propose a graph-based clustering model for disentanglement, using lexical, timing, and discourse-based features. The model's predicted disentanglements are highly correlated with manual annotations. We conclude by discussing two extensions to the model, specificity tuning and conversation start detection, both of which are promising but do not currently yield practical improvements.
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paper_title: Towards educational data mining : Using data mining methods for automated chat analysis to understand and support inquiry learning processes

paper_content:

In this paper we investigate the application of data mining methods to provide learners with real-time adaptive feedback on the nature and patterns of their on-line communication while learning collaboratively. We derived two mod- els for classifying chat messages using data mining techniques and tested these on an actual data set (16). The reliability of the classification of chat messages is established by comparing the models performance to that of humans. Results indicate that the classification of messages is reasonably reliable and can thus be done automatically and in real-time. This makes it, for example, possible to increase the awareness of learners by visualizing their interaction behaviour by means of avatars. It is concluded that the application of data mining methods to educational chats is both feasible and can, over time, result in the improvement of learning environments.
---
paper_title: ChatTrack: Chat Room Topic Detection Using Classification

paper_content:

The traditional analysis of Internet chat room discussions places a resource burden on the intelligence community because of the time required to monitor thousands of continuous chat sessions. Chat rooms are used to discuss virtually any subject, including computer hacking and bomb making, creating a virtual sanctuary for criminals to collaborate. Given the upsurge of interest in homeland security issues, we have developed a text classification system that creates a concept-based profile that represents a summary of the topics discussed in a chat room or by an individual participant. We then discuss this basic chat profiling system and demonstrate the ability to selectively augment the standard concept database with new concepts of significance to an agent. Finally, we show how an investigator can, once alerted to a user or session of interest via the profile, retrieve details about the chat session through our chat archiving and search system.
---
paper_title: Visualisation of Learners' Contributions in Chat Conversations

paper_content:

In this paper is presented a novel dialogistic, socio-cultural perspective and an associated software tool, which provide structured visualisation and analysis means of Computer Supported Collaborative Learning chat conversations. The implemented tools use knowledge-based techniques and are based on Bakhtin's dialogistic paradigm. They visualize the threading of topics and utterances in the conversation and the contributions of the participants in collaborative learning during instant messenger chats. Natural language processing based on the WordNet lexical ontology and semantic distances are used for detecting topics in the chat and their threading. The experiments with the developed application were performed with students at a course on Human-Computer Interaction in Bucharest Politehnica University.
---
paper_title: Topic detection in online chat

paper_content:

Abstract : The ubiquity of Internet chat applications has benefited many different segments of society. It also creates opportunities for criminal enterprise, terrorism, and espionage. This thesis proposes statistical Natural Language Processing (NLP) methods for creating systems that would detect the topic of chat in support of larger NLP goals such as information retrieval, text classification and illicit activity detection. We propose a novel method for determining the topic of chat discourse. We trained Latent Dirichlet Allocation (LDA) models on source documents and then used inferred topic distributions as feature vectors for a Support Vector Machine (SVM) classification system. We constructed LDA models in three ways: We considered the collective posts of authors as documents, hypothesizing that we could detect the topic physics given only one side of the conversation. The resultant classifiers obtained F-scores of 0.906. Next, we considered individual posts as documents, hypothesizing we could detect physics posts. The resultant classifiers obtained F-scores of 0.481. Finally, we considered physics textbook paragraphs as documents, hypothesizing that we could determine the topic of an author or a post based on an LDA model created from a textbook and a sample of noisy chat. The resultant classifiers obtained F-scores of 0.848 and 0.536 respectively.
---
paper_title: Topic Identification in Dynamical Text by Complexity Pursuit

paper_content:

The problem of analysing dynamically evolving textual data has arisen within the last few years. An example of such data is the discussion appearin g in Internet chat lines. In this Letter a recently introduced source separation method, termed as complexity pursuit, is applied to the problem of finding topics in dynamical text and is compared against several blind separation algorithms for the problem considered. Complexity pursuit is a generalisation of projection pursuit to time series and it is able to use both higher-order statistical measures and temporal dependency information in separating the topics. Experimental results on chat line and newsgroup data demonstrate that the minimum complexity time series indeed do correspond to meaningful topics inherent in the dynamical text data, and also suggest the applicability of the method to query-based retrieval from a temporally changing text stream.
---
paper_title: Chat mining: Automatically determination of chat conversations' topic in Turkish text based chat mediums

paper_content:

Mostly, the conversations taking place in chat mediums bear important information concerning the speakers. This information can vary in many fields such as tendencies, habits, attitudes, guilt situations, and intentions of the speakers. Therefore, analysis and processing of these conversations are of much importance. Many social and semantic inferences can be made from these conversations. In determining characteristics of conversations and analysis of conversations, subject designation can be grounded on. In this study, chat mining is chosen as an application of text mining, and a study concerning determination of subject in the Turkish text based chat conversations is conducted. In sorting the conversations, supervised learning methods are used in this study. As for classifiers, Naive Bayes, k-Nearest Neighbor and Support Vector Machine are used. Ninety-one percent success is achieved in determination of subject.
---
paper_title: Mining Chat-room Conversations for Social and Semantic Interactions

paper_content:

The ephemeral nature of human communication via networks today poses interesting and challenging problems for information technologists. The Intelink intelligence network, for example, has a need to monitor chat-room conversations to ensure the integrity of sensitive data being transmitted via the network. However, the sheer volume of communication in venues such as email, newsgroups, and chat precludes manual techniques of information management. It has been estimated that over 430 million instant messages, for example, are exchanged each day on the America Online network [3]. Although a not insignificant fraction of such data may be temporarily archived (e.g., newsgroups), no systematic mechanisms exist for accumulating these artifacts of communication in a form that lends itself to the construction of models of semantics [12]. In essence, dynamic techniques of analysis are needed if textual data of this nature is to be effectively mined. This article reports our progress in developing a text mining tool for analysis of chat-room conversations. Central to our efforts is the development of functionality to answer questions such as "What topics are being discussed in a chat-room?", "Who is discussing which topics?" and "Who is interacting with whom?" The objective of our research is to develop technology that can automatically identify such patterns of interaction in both social and semantic terms. In this article we report our preliminary findings in identifying threads of conversation in multi-topic, multi-person chat-rooms. We have achieved promising results in terms of precision and recall by employing pattern recognition techniques based on finite state automata. We also report the design of our approach to building models of social and semantic interactions based on our HDDI text mining infrastructure [13].
---
paper_title: Butterfly: a conversation-finding agent for Internet relay chat

paper_content:

The Internet enables groups of people throughout the world to interact to discuss issues, get assistance, learn, and socialize. However, when there are thousands of loosely defined groups in which a user could potentially participate, the problem becomes finding the groups of most interest. In this paper we focus on the domain of Internet Relay Chat real-time text messaging, and describe a “social butterfly” agent called Butterfly that samples available conversational groups and recommends ones of interest. We discuss Butterfly’s motivation, usage, realworld design constraints, implementation, and results. Finally, we introduce work in progress on a multi-agent approach that has grown out of our experience with Butterfly.
---
paper_title: Text chat in action

paper_content:

Synchronous text communication is becoming recognized as a valuable workplace communication medium yet some studies of group text chat indicate that its properties can lead to interactional incoherence. We consider this issue through a detailed analytic examination of text chat transcripts by showing how participants manage their interactions through considering multiple threads, turn taking and topic change. We reveal the routine practices that participants employ to create and manage coherent interaction. These practices arise from the turn taking system in operation, which facilitates straightforward repair of misunderstandings. We conclude by considering the implications of this for design and for the organisation and management of interactions of various forms.
---
paper_title: Combining Topic Models and Social Networks for Chat Data Mining

paper_content:

Informal chat-room conversations have intrinsically different properties from regular static document collections. Noise, concise expressions and dynamic, changing and interleaving nature of discussions make chat data ill-suited for analysis with an off-the-shelf text mining method. On the other hand, interactive human communication has some implicit features which may be used to enhance the results. In our research we infer social network structures from the chat data by using a few basic heuristics. We then present some preliminary results showing that the inferred social graph may be used to enhance topic identification of a chat room when combined with a state-of-the-art topic and classification models. For validation purposes we then compare the performance effects of using this social information in a topic classification task.
---
paper_title: Log-Based Chat Room Monitoring Using Text Categorization: A Comparative Study

paper_content:

The Internet has been utilized in several real life aspects such as online searching, distance learning, and chatting. On the other hand, the Internet has been misused in communication of crime related matters. Monitoring of such communication would aid in crime detection or even crime prevention. This paper presents a text categorization approach for automatic monitoring of chat conversations since the current monitoring techniques are basically manual, which is tedious, costly, and time consuming. We present the results of a cross method comparison between the Naive Bayes, the K-nearest neighbor, and the Support Vector Machine classifiers. Our objective is to determine the most suitable method for the data of chat conversations. Our results showed that the chat room monitoring task can be efficiently automated using the appropriate text categorization method.
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paper_title: Recognizing connotative meaning in military chat communications

paper_content:

Over the last five to seven years the use of chat in military contexts has expanded quite significantly, in some cases ::: becoming a primary means of communicating time-sensitive data to decision makers and operators. For example, during ::: humanitarian operations with Joint Task Force-Katrina, chat was used extensively to plan, task, and coordinate predeployment ::: and ongoing operations. The informal nature of chat communications allows the relay of far more ::: information than the technical content of messages. Unlike formal documents such as newspapers, chat is often emotive. ::: "Reading between the lines" to understand the connotative meaning of communication exchanges is now feasible, and ::: often important. Understanding the connotative meaning of text is necessary to enable more useful automatic ::: intelligence exploitation. The research project described in this paper was directed at recognizing user connotations of ::: uncertainty and urgency. The project built a matrix of speech features indicative of these categories of meaning, ::: developed data mining software to recognize them, and evaluated the results.
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paper_title: Posting Act Tagging Using Transformation-Based Learning

paper_content:

In this article we present the application of transformation-based learning (TBL) [1] to the task of assigning tags to postings in online chat conversations. We define a list of posting tags that have proven useful in chat-conversation analysis. We describe the templates used for posting act tagging in the context of template selection. We extend traditional approaches used in part-of-speech tagging and dialogue act tagging by incorporating regular expressions into our templates. We close with a presentation of results that compare favorably with the application of TBL in dialogue act tagging.
---
paper_title: Thread detection in dynamic text message streams

paper_content:

Text message stream is a newly emerging type of Web data which is produced in enormous quantities with the popularity of Instant Messaging and Internet Relay Chat. It is beneficial for detecting the threads contained in the text stream for various applications, including information retrieval, expert recognition and even crime prevention. Despite its importance, not much research has been conducted so far on this problem due to the characteristics of the data in which the messages are usually very short and incomplete. In this paper, we present a stringent definition of the thread detection task and our preliminary solution to it. We propose three variations of a single-pass clustering algorithm for exploiting the temporal information in the streams. An algorithm based on linguistic features is also put forward to exploit the discourse structure information. We conducted several experiments to compare our approaches with some existing algorithms on a real dataset. The results show that all three variations of the single-pass algorithm outperform the basic single-pass algorithm. Our proposed algorithm based on linguistic features improves the performance relatively by 69.5% and 9.7% when compared with the basic single-pass algorithm and the best variation algorithm in terms of F1 respectively.
---
paper_title: The role and identification of dialog acts in online chat

paper_content:

In recent years, online chat has become a dominant mode of communication. This text-based medium has the potential of improving information awareness within an organization, but only if the critical information within messages can be identified and directed to where it is most needed. Such a goal has many challenges that traditional Information Extraction (IE) approaches have rarely addressed: the text is "dirty" (containing typos, misspellings, sparse punctuation, etc.), messages are fragmented and refer implicitly to previous messages and shared knowledge, messages from multiple topics are interleaved, etc. ::: ::: Past work in conversation analysis has included in-depth discussions of dialog acts, i.e., the individual utterances that comprise conversations. This paper describes how dialog acts within online chat differ from those within two-person voice conversations. It then presents methods for identifying dialog acts and the role that dialog acts play in identifying individual conversations within a chat stream. Identifying conversations is a necessary step for extracting actionable information, such as identifying individuals with specific expertise, recognizing reports of offline activities, and alerting decision makers to critical developments. Finally, we describe Chat-IE, a prototype software system that performs live dialog identification on chat streams.
---
paper_title: Lexical and Discourse Analysis of Online Chat Dialog

paper_content:

One of the ultimate goals of natural language processing (NLP) systems is understanding the meaning of what is being transmitted, irrespective of the medium (e.g., written versus spoken) or the form (e.g., static documents versus dynamic dialogues). Although much work has been done in traditional language domains such as speech and static written text, little has yet been done in the newer communication domains enabled by the Internet, e.g., online chat and instant messaging. This is in part due to the fact that there are no annotated chat corpora available to the broader research community. The purpose of this research is to build a chat corpus, tagged with lexical (token part-of-speech labels), syntactic (post parse tree), and discourse (post classification) information. Such a corpus can then be used to develop more complex, statistical-based NLP applications that perform tasks such as author profiling, entity identification, and social network analysis.
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paper_title: Text Classification Methodologies Applied to Micro-Text in Military Chat

paper_content:

We propose methods to classify lines of military chat, or posts, which contain items of interest. We evaluated several current text categorization and feature selection methodologies on chat posts. Our chat posts are examples of 'micro-text', or text that is generally very short in length, semi-structured, and characterized by unstructured or informal grammar and language. Although this study focused specifically on tactical updates via chat, we believe the findings are applicable to content of a similar linguistic structure. Completion of this milestone is a significant first step in allowing for more complex categorization and information extraction.
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paper_title: Automatic Author Profiling of Online Chat Logs

paper_content:

Abstract : Now that the Internet has become easily accessible and more affordable, a larger number of people spend more time in front of a computer. Some spend so much time on the Internet that they develop virtual friendships and relationships people with whom they have regular contact via a computer screen and the Internet. While most of the dialogue exchanged online is not harmful or illegal, there are those with dishonest intentions lurking online. These people can be breaking the law by seducing a minor virtually or even going as far as meeting a minor in person. Terrorists can also use the Internet to facilitate communication and plan attacks. Since e-mail is one of the original means of communication on the Internet, methods for determining the author of an e-mail have already been studied. So far, however, no significant experimentation with online chat logs exists. The first of part of this study is comprised of generating an unbiased, random, and broad corpus of online chat logs. Having a general corpus with a wide-range of topics allows the results of this research to be applied in the most general case. Because developing a complete solution to the authorship attribution problem for chat logs is difficult, we limit our scope to predicting gender and age. The ultimate goal of this work, then, is to facilitate the jobs of law enforcers in tracking down criminals who attempt to use the Internet as a hiding place.
---
paper_title: ChatTrack: Chat Room Topic Detection Using Classification

paper_content:

The traditional analysis of Internet chat room discussions places a resource burden on the intelligence community because of the time required to monitor thousands of continuous chat sessions. Chat rooms are used to discuss virtually any subject, including computer hacking and bomb making, creating a virtual sanctuary for criminals to collaborate. Given the upsurge of interest in homeland security issues, we have developed a text classification system that creates a concept-based profile that represents a summary of the topics discussed in a chat room or by an individual participant. We then discuss this basic chat profiling system and demonstrate the ability to selectively augment the standard concept database with new concepts of significance to an agent. Finally, we show how an investigator can, once alerted to a user or session of interest via the profile, retrieve details about the chat session through our chat archiving and search system.
---
paper_title: Detecting Age in Online Chat

paper_content:

Abstract : Over 90 % of teens in the United States use the Internet, and many use it for social interaction. Due to the faceless nature of digital communication, criminals can easily pose as legitimate users to build friendship and trust with potential victims. Even though fewer youths are going to chat rooms and talking to people they do not know, the number of youths receiving aggressive solicitations for offline contact has not declined. Most sexual solicitations go unreported to law enforcement and parents. Though it is a crime for an adult to sexually exploit a minor, it is not always a crime for teens to solicit other teens. It would be of great help to law enforcement agencies if they could automatically detect adults soliciting teens versus teens soliciting other teens in online chat. This study analyzes the effectiveness of different machine learning techniques to distinguish chat conversation by teens and adults. Using proposed techniques, we classified teen and adult conversations with an accuracy of 86 %. The goal of this research is to build an automatic recognition system of adults conversing with teens, capable of detecting predators and alerting agencies or parents of possible inappropriate conversations.
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paper_title: Modeling socio-cultural phenomena in online multi-party discourse

paper_content:

We present in this paper, the application of a novel approach to computational modeling, understanding and detection of social phenomena in online multiparty discourse. A two-tiered approach was developed to detect a collection of social phenomena deployed by participants, such as topic control, task control, disagreement and involvement. We discuss how the mid-level social phenomena can be reliably detected in discourse and these measures can be used to differentiate participants of online discourse. Our approach works across different types of online chat and we show results on two specific data sets.
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paper_title: Using Speech Act Profiling for Deception Detection

paper_content:

The rising use of synchronous text-based computer-mediated communication (CMC) such as chat rooms and instant messaging in government agencies and the business world presents a potential risk to these organizations. There are no current methods for visualizing or analyzing these persistent conversations to detect deception. Speech act profiling is a method for analyzing and visualizing online conversations, and this paper shows its use for distinguishing online conversations that express uncertainty, an indicator of deception.
---
paper_title: A Tool for Internet Chatroom Surveillance

paper_content:

Internet chatrooms are common means of interaction and communications, and they carry valuable information about formal or ad-hoc formation of groups with diverse objectives. This work presents a fully automated surveillance system for data collection and analysis in Internet chatrooms. The system has two components: First, it has an eavesdropping tool which collects statistics on individual (chatter) and chatroom behavior. This data can be used to profile a chatroom and its chatters. Second, it has a computational discovery algorithm based on Singular Value Decomposition (SVD) to locate hidden communities and communication patterns within a chatroom. The eavesdropping tool is used for fine tuning the SVD-based discovery algorithm which can be deployed in real-time and requires no semantic information processing. The evaluation of the system on real data shows that (i) statistical properties of different chatrooms vary significantly, thus profiling is possible, (ii) SVD-based algorithm has up to 70-80% accuracy to discover groups of chatters.
---
paper_title: Detecting deception in synchronous computer-mediated communication using speech act profiling

paper_content:

Detecting deception is a complicated endeavor. Previous attempts at deception detection in computer-mediated communication have met with some success. This study shows how speech act profiling [1] can be used to aid deception detection in synchronous computer-mediated communication (S-CMC). Chat logs from an online group game where deception was introduced were subjected to speech act profiling analysis. The results provide some support to previous research showing greater uncertainty in deceptive S-CMC. Also shown is that deceivers in the specific task tend to engage in less strategizing than non-deceivers.
---
paper_title: Inferring and visualizing social networks on Internet relay chat

paper_content:

Internet relay chat is a system that allows groups of people to collaborate and chat from anywhere in the world. Clearly defined by several RFC documents, it is arguably the most standard real-time chat system currently in use. This work describes a method of inferring the social network of a group of IRC users in a channel. An IRC bot is used to monitor a channel and perform a heuristic analysis of events to create a mathematical approximation of the social network. From this, the bot can produce a visualization of the inferred social network on demand. These visualizations reveal the structure of the social network, highlighting connectivity, clustering and strengths of relationships between users. Animated output allows viewers to see the evolution of the social network over time. Some novel ideas for future work are discussed, showing other useful applications of this system.
---
paper_title: See No Evil: The Effect of Communication Medium and Motivation on Deception Detection

paper_content:

The present study reports an experiment that examines the role of communication medium and liar motivation on deception detection. Participants were randomly assigned to one of two dyadic communication conditions, text-based, computer-mediated environment or face-to-face, and to one of two motivation conditions, high or low. Participants engaged in a discussion of four topics, in which one participant was deceptive during two topics and truthful during the other two. No main effect of communication medium or motivation level was observed. However, an interaction effect suggests that highly motivated liars interacting in a text-based, computer- mediated environment were the most successful in deceiving their partners. The implications of these results are discussed both in terms of the elimination of non- verbal cues, as well as the potential advantages to the motivated liar offered by text-based media.
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paper_title: Digesting Virtual "Geek" Culture: The Summarization Of Technical Internet Relay Chats

paper_content:

This paper describes a summarization system for technical chats and emails on the Linux kernel. To reflect the complexity and sophistication of the discussions, they are clustered according to subtopic structure on the sub-message level, and immediate responding pairs are identified through machine learning methods. A resulting summary consists of one or more mini-summaries, each on a subtopic from the discussion.
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paper_title: Plans Toward Automated Chat Summarization

paper_content:

We describe the beginning stages of our work on summarizing chat, which is motivated by our observations concerning the information overload of US Navy watchstanders. We describe the challenges of summarizing chat and focus on two chat-specific types of summarizations we are interested in: thread summaries and temporal summaries. We then discuss our plans for addressing these challenges and evaluation issues.
---
paper_title: ROUGE: A Package For Automatic Evaluation Of Summaries

paper_content:

ROUGE stands for Recall-Oriented Understudy for Gisting Evaluation. It includes measures to automatically determine the quality of a summary by comparing it to other (ideal) summaries created by humans. The measures count the number of overlapping units such as n-gram, word sequences, and word pairs between the computer-generated summary to be evaluated and the ideal summaries created by humans. This paper introduces four different ROUGE measures: ROUGE-N, ROUGE-L, ROUGE-W, and ROUGE-S included in the ROUGE summarization evaluation package and their evaluations. Three of them have been used in the Document Understanding Conference (DUC) 2004, a large-scale summarization evaluation sponsored by NIST.
---
paper_title: Topic Identification in Dynamical Text by Complexity Pursuit

paper_content:

The problem of analysing dynamically evolving textual data has arisen within the last few years. An example of such data is the discussion appearin g in Internet chat lines. In this Letter a recently introduced source separation method, termed as complexity pursuit, is applied to the problem of finding topics in dynamical text and is compared against several blind separation algorithms for the problem considered. Complexity pursuit is a generalisation of projection pursuit to time series and it is able to use both higher-order statistical measures and temporal dependency information in separating the topics. Experimental results on chat line and newsgroup data demonstrate that the minimum complexity time series indeed do correspond to meaningful topics inherent in the dynamical text data, and also suggest the applicability of the method to query-based retrieval from a temporally changing text stream.
---
paper_title: Topic Identification in Dynamical Text by Complexity Pursuit

paper_content:

The problem of analysing dynamically evolving textual data has arisen within the last few years. An example of such data is the discussion appearin g in Internet chat lines. In this Letter a recently introduced source separation method, termed as complexity pursuit, is applied to the problem of finding topics in dynamical text and is compared against several blind separation algorithms for the problem considered. Complexity pursuit is a generalisation of projection pursuit to time series and it is able to use both higher-order statistical measures and temporal dependency information in separating the topics. Experimental results on chat line and newsgroup data demonstrate that the minimum complexity time series indeed do correspond to meaningful topics inherent in the dynamical text data, and also suggest the applicability of the method to query-based retrieval from a temporally changing text stream.
---
paper_title: Difficulties in establishing common ground in multiparty groups using machine translation

paper_content:

When people communicate in their native languages using machine translation, they face various problems in constructing common ground. This study investigates the difficulties of constructing common ground when multiparty groups (consisting of more than two language communities) communicate using machine translation. We compose triads whose members come from three different language communities--China, Korea, and Japan--and compare their referential communication under two conditions: in their shared second language (English) and in their native languages using machine translation. Consequently, our study suggests the importance of not only grounding between speaker and addressee but also grounding between addressees in constructing effective machine-translation-mediated communication. Furthermore, to successfully build common ground between addressees, it seems important for them to be able to monitor what is going on between a speaker and other addressees.
---
paper_title: Availability of multilingual chat communication in 3D online virtual space

paper_content:

There is a possibility that a 3D online virtual space can become a more familiar communication medium because of the widespread use of the Internet. However, language differences pose significant barriers to intercultural communications. We have developed a multilingual chat communication support system in Second Life as an initial research. Second Life is one of the most popular 3D online virtual spaces in the world. We have carried out a multilingual chat communication experiment in Second Life. From this experiment, we have found the following. (1) It is important that a machine translation machine make it visible in a 3D virtual space. A user can easily learn the usage of a machine translation. (2) There is not understanding other party's language in the problem because appearances of each avatar are free. It is important for users to show translation language pairs not to give confusion.
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paper_title: Can Real-Time Machine Translation Overcome Language Barriers in Distributed Requirements Engineering?

paper_content:

In global software projects work takes place over long distances, meaning that communication will often involve distant cultures with different languages and communication styles that, in turn, exacerbate communication problems. However, being aware of cultural distance is not sufficient to overcome many of the barriers that language differences bring in the way of global project success. In this paper, we investigate the adoption of machine translation (MT) services in synchronous text-based chat in order to overcome any language barrier existing among groups of stakeholders who are remotely negotiating software requirements. We report our findings from a simulated study that compares the efficiency and the effectiveness of two MT services, Google Translate and apertium-service, in translating the messages exchanged during four distributed requirements engineering workshops. The results show that (a) Google Translate produces significantly more adequate translations than Apertium from English to Italian; (b) both services can be used in text-based chat without disrupting real-time interaction.
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paper_title: VCA: An Experiment with a Multiparty Virtual Chat Agent

paper_content:

The purpose of this research was to advance the understanding of the behavior of small groups in online chat rooms. The research was conducted using Internet chat data collected through planned exercises with recruited participants. Analysis of the collected data led to construction of preliminary models of social behavior in online discourse. Some of these models, e.g., how to effectively change the topic of conversation, were subsequently implemented into an automated Virtual Chat Agent (VCA) prototype. VCA has been demonstrated to perform effectively and convincingly in Internet conversation in multiparty chat environments.
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paper_title: Methods for chatbot detection in distributed text-based communications

paper_content:

Distributed text-based communications (e.g., chat, instant-messaging) are facing the growing problem of malicious “chatbots” or “chatterbots” (automated communication programs posing as humans) attempting social engineering, gathering intelligence, mounting phishing attacks, spreading malware and spam, and threatening the usability and security of collaborative communication platforms. We provide supporting evidence for the suggestion that gross communication and behavioral patterns (e.g., message size, inter-message delays) can be used to passively distinguish between humans and chatbots. Further, we discuss several potential interrogation strategies for users and chat room administrators who may need to actively distinguish between a human and a chatbot, quickly and reliably, during distributed communication sessions. Interestingly, these issues are in many ways analogous to the identification problem faced by interrogators in a Turing Test, and the proposed methods and strategies might find application to and inspiration from this topic as well.
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paper_title: Cobot in LambdaMOO: An Adaptive Social Statistics Agent

paper_content:

We describe our development of Cobot, a novel software agent who lives in LambdaMOO, a popular virtual world frequented by hundreds of users. Cobot's goal was to become an actual part of that community. Here, we present a detailed discussion of the functionality that made him one of the objects most frequently interacted with in LambdaMOO, human or artificial. Cobot's fundamental power is that he has the ability to collect social statistics summarizing the quantity and quality of interpersonal interactions. Initially, Cobot acted as little more than a reporter of this information; however, as he collected more and more data, he was able to use these statistics as models that allowed him to modify his own behavior. In particular, cobot is able to use this data to "self-program," learning the proper way to respond to the actions of individual users, by observing how others interact with one another. Further, Cobot uses reinforcement learning to proactively take action in this complex social environment, and adapts his behavior based on multiple sources of human reward. Cobot represents a unique experiment in building adaptive agents who must live in and navigate social spaces.
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paper_title: Taming Multiple Chat Room Collaboration: Real-Time Visual Cues to Social Networks and Emerging Threads

paper_content:

Distributed teams increasingly rely on collaboration environments, typically including chat, to link diverse experts for real time information sharing and decision-making. Current chat-based technologies enable easy exchange of information, but don’t focus on managing those information exchanges. Important cues that guide face-to-face collaboration are either lost or missing. In some military environments, operators may juggle over a dozen chat rooms in order to collaborate on complex missions. This often leads to confusion, overload, miscommunication and delayed decisions. Our technology supports chat management. A summary display bar reduces the number of chat rooms operators need open by providing high level situational awareness pointers, in real-time, to: a) rooms with increasing message activity levels, b) rooms in which important collaborators are participating (those in the operator’s social network), and c) rooms in which operator-selected keywords are used. This ability to peripherally monitor less critical chat rooms reduces operator overload, while enhancing the ability to rapidly detect important emerging discussion threads.
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paper_title: Next-generation tactical-situation-assessment technology (TSAT): chat

paper_content:

This paper presents concepts, content, status, applications and challenges of chat as used in the military context of secure net-centric command and control. It describes the importance of chat as it contributes to situation assessment and the common operating picture, which presents current collective knowledge of the battle space. The paper discusses future chat capabilities and outlines the road ahead for the TSAT project.
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Title: Multiparticipant Chat Analysis: A Survey

Section 1: Introduction
Description 1: Provide an overview of multiparticipant chat, its uses, and the challenges involved in analyzing such chat.

Section 2: Multiparticipant chat analysis: Basic definitions and tasks
Description 2: Define terminology and fundamental tasks associated with multiparticipant chat analysis.

Section 3: Comparison to other media
Description 3: Compare the characteristics of chat messages with other forms of text communication.

Section 4: Chat corpora
Description 4: Discuss various types of chat corpora used for research, including non-annotated, annotated, and synthetic corpora.

Section 5: Artificial intelligence research foci
Description 5: Describe the many AI research foci on analyzing multiparticipant chat and define current research interests.

Section 6: Motivations for chat research
Description 6: Summarize the importance of chat analysis research from various perspectives such as user interactions, user behaviors, online learning, and military use.

Section 7: Current state of multiparticipant chat analysis
Description 7: Examine difficulties and current research progress in various areas of multiparticipant chat analysis.

Section 8: Chat preprocessing
Description 8: Discuss techniques used to prepare chat messages for analysis, including normalization methods.

Section 9: Chat room feature processing
Description 9: Explain how features like time stamps, nickname augmentation, and speaker identity help in chat analysis.

Section 10: Thread disentanglement
Description 10: Address the challenges and techniques in detecting and extracting individual conversation threads from interwoven conversations.

Section 11: Topic detection
Description 11: Describe methods for detecting topics within chat logs and the challenges involved.

Section 12: Message attribute identification
Description 12: Discuss techniques for tagging words and messages, including part-of-speech tagging and urgency detection.

Section 13: User profiling
Description 13: Address methods for discovering properties of chat users despite anonymity.

Section 14: Social phenomenon detection
Description 14: Explain techniques for detecting social interactions and behaviors among chat users.

Section 15: Automatic summarization
Description 15: Describe the process and techniques for creating textual summaries from chat messages.

Section 16: Discussion
Description 16: Highlight open issues, the need for more annotated corpora, and the potential for cross-leveraging techniques from other media.

Section 17: Conclusion
Description 17: Summarize the survey's findings and emphasize the importance of continued research in multiparticipant chat analysis.