Solutions to Detect and Analyze Online Radicalization : A Survey
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paper_title: Studying Hate Crime with the Internet: What Makes Racists Advocate Racial Violence?

paper_content:

We conducted semistructured interviews with 38 participants in White racist Internet chat rooms, examining the extent to which people would, in this unique environment, advocate interracial violence in response to purported economic and cultural threats. Capitalizing on the anonymity and candor of chat room interactions, this study provides an unusual perspective on extremist attitudes. We experimentally manipulated the nature and proximity of the threats. Qualitative and quantitative analyses indicate that the respondents were most threatened by interracial marriage and, to a lesser extent, Blacks moving into White neighborhoods. In contrast, job competition posed by Blacks evoked very little advocacy of violence. The study affords an assessment of the advantages and limitations of Internet-based research with clandestine populations.
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paper_title: White Supremacist Networks on the Internet

paper_content:

Abstract In this paper we use methods of social network analysis to examine the interorganizational structure of the white supremacist movement. Treating links between Internet websites as ties of affinity, communication, or potential coordination, we investigate the structural properties of connections among white supremacist groups. White supremacism appears to be a relatively decentralized movement with multiple centers of influence, but without sharp cleavages between factions. Interorganizational links are stronger among groups with a special interest in mutual affirmation of their intellectual legitimacy (Holocaust revisionists) or cultural identity (racist skinheads) and weaker among groups that compete for members (political parties) or customers (commercial enterprises). The network is relatively isolated from both mainstream conservatives and other extremist groups. Christian Identity theology appears ineffective as a unifying creed of the movement, while Nazi sympathies are pervasive. Recruitme...
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paper_title: Hate Online: A Content Analysis of Extremist Internet Sites

paper_content:

Extremists, such as hate groups espousing racial supremacy or separation, have established an online presence. A content analysis of 157 extremist web sites selected through purposive sampling was conducted using two raters per site. The sample represented a variety of extremist groups and included both organized groups and sites maintained by apparently unaffiliated individuals. Among the findings were that the majority of sites contained external links to other extremist sites (including international sites), that roughly half the sites included multimedia content, and that half contained racist symbols. A third of the sites disavowed racism or hatred, yet one third contained material from supremacist literature. A small percentage of sites specifically urged violence. These and other findings suggest that the Internet may be an especially powerful tool for extremists as a means of reaching an international audience, recruiting members, linking diverse extremist groups, and allowing maximum image control.
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paper_title: Protection of Critical National Information Infrastructure (CNII) against cyber terrorism: Development of strategy and policy framework

paper_content:

Critical National Information Infrastructure (CNII) is crucial to the survivability of a nation that the destruction or disruption of these systems and communication networks would significantly affect the economic strength, image, defense and security, government capabilities to function, and public health and safety. CNII would probably become an attractive target for terrorists. There is a need to have a strategy at the national level for the protection of CNII from cyber terrorism activities.
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paper_title: Web Mining Research: A Survey

paper_content:

With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.
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paper_title: Machine learning in automated text categorization

paper_content:

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.
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paper_title: The World-Wide Web: quagmire or gold mine?

paper_content:

An efficient continuous wave solid state laser is described. An array of semiconductor diode lasers is employed to generate a pump light source at an absorption wavelength for a solid state laser such as Nd:YAG laser. The array of diode lasers is pulsed at a high repetition rate to produce narrow light pulses which are directed upon an end surface of the solid state laser rod. The pump pulses rate is selected sufficiently high to establish CW operation of the solid state laser while the duty cycle for the pump-light source is sufficiently low to avoid excessive junction heating of the diode lasers, which would substantially reduce their output power.
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paper_title: Hate Online: A Content Analysis of Extremist Internet Sites

paper_content:

Extremists, such as hate groups espousing racial supremacy or separation, have established an online presence. A content analysis of 157 extremist web sites selected through purposive sampling was conducted using two raters per site. The sample represented a variety of extremist groups and included both organized groups and sites maintained by apparently unaffiliated individuals. Among the findings were that the majority of sites contained external links to other extremist sites (including international sites), that roughly half the sites included multimedia content, and that half contained racist symbols. A third of the sites disavowed racism or hatred, yet one third contained material from supremacist literature. A small percentage of sites specifically urged violence. These and other findings suggest that the Internet may be an especially powerful tool for extremists as a means of reaching an international audience, recruiting members, linking diverse extremist groups, and allowing maximum image control.
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paper_title: Web mining: information and pattern discovery on the World Wide Web

paper_content:

Application of data mining techniques to the World Wide Web, referred to as Web mining, has been the focus of several recent research projects and papers. However, there is no established vocabulary, leading to confusion when comparing research efforts. The term Web mining has been used in two distinct ways. The first, called Web content mining in this paper, is the process of information discovery from sources across the World Wide Web. The second, called Web usage mining, is the process of mining for user browsing and access patterns. We define Web mining and present an overview of the various research issues, techniques, and development efforts. We briefly describe WEBMINER, a system for Web usage mining, and conclude the paper by listing research issues.
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paper_title: Mining communities and their relationships in blogs: A study of online hate groups

paper_content:

Blogs, often treated as the equivalence of online personal diaries, have become one of the fastest growing types of Web-based media. Everyone is free to express their opinions and emotions very easily through blogs. In the blogosphere, many communities have emerged, which include hate groups and racists that are trying to share their ideology, express their views, or recruit new group members. It is important to analyze these virtual communities, defined based on membership and subscription linkages, in order to monitor for activities that are potentially harmful to society. While many Web mining and network analysis techniques have been used to analyze the content and structure of the Web sites of hate groups on the Internet, these techniques have not been applied to the study of hate groups in blogs. To address this issue, we have proposed a semi-automated approach in this research. The proposed approach consists of four modules, namely blog spider, information extraction, network analysis, and visualization. We applied this approach to identify and analyze a selected set of 28 anti-Blacks hate groups (820 bloggers) on Xanga, one of the most popular blog hosting sites. Our analysis results revealed some interesting demographical and topological characteristics in these groups, and identified at least two large communities on top of the smaller ones. The study also demonstrated the feasibility in applying the proposed approach in the study of hate groups and other related communities in blogs.
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paper_title: Mining the Web's Link Structure

paper_content:

The Web is a hypertext body of approximately 300 million pages that continues to grow at roughly a million pages per day. Page variation is more prodigious than the data's raw scale: taken as a whole, the set of Web pages lacks a unifying structure and shows far more authoring style and content variation than that seen in traditional text document collections. This level of complexity makes an "off-the-shelf" database management and information retrieval solution impossible. To date, index based search engines for the Web have been the primary tool by which users search for information. Such engines can build giant indices that let you quickly retrieve the set of all Web pages containing a given word or string. Experienced users can make effective use of such engines for tasks that can be solved by searching for tightly constrained key words and phrases. These search engines are, however, unsuited for a wide range of equally important tasks. In particular, a topic of any breadth will typically contain several thousand or million relevant Web pages. How then, from this sea of pages, should a search engine select the correct ones-those of most value to the user? Clever is a search engine that analyzes hyperlinks to uncover two types of pages: authorities, which provide the best source of information on a given topic; and hubs, which provide collections of links to authorities. We outline the thinking that went into Clever's design, report briefly on a study that compared Clever's performance to that of Yahoo and AltaVista, and examine how our system is being extended and updated.
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paper_title: Persuasive storytelling by hate groups online

paper_content:

Increasingly, hate groups have used the Internet to express their viewpoints, sell their paraphernalia, and recruit new members. This study explored the effectiveness of persuasive storytelling found on White supremacist Web pages. One hundred eight adolescents participated in a longitudinal study (pretest, Time 1, Time 2) conducted online. A 3 (receptivity: negatively inclined, neutral, predisposed) x 2 (narrative: high vs. low perceived) x 2 (message explicitness: implicit vs. explicit) factorial design found that immediately following exposure, high-narrative and implicit messages were more persuasive than low-narrative and explicit messages. Interestingly, high-narrative and implicit effects decayed, whereas low-narrative and explicit message effects endured or increased slightly over time. In addition, people's receptivity interacted with the message factors to further mediate persuasion. For example, disagreeing individuals resisted low-narrative, explicit messages significantly more than any other condition. The article concludes with a discussion of the social implications of these findings.
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paper_title: Community structure in social and biological networks

paper_content:

A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known—a collaboration network and a food web—and find that it detects significant and informative community divisions in both cases.
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paper_title: Visualizing Social Networks

paper_content:

The use of visual images is common in many branches of science. And reviewers often suggest that such images are important for progress in the various fields (Koestler, 1964; Arnheim, 1970; Taylor, 1971; Tukey, 1972; Klovdahl, 1981; Tufte, 1983; Belien and Leenders). The historian Alfred Crosby (1997) has gone much further. He has proposed that visualization is one of only two factors that are responsible for the explosive development of all of modern science. The other is measurement.
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paper_title: Mining the Web's Link Structure

paper_content:

The Web is a hypertext body of approximately 300 million pages that continues to grow at roughly a million pages per day. Page variation is more prodigious than the data's raw scale: taken as a whole, the set of Web pages lacks a unifying structure and shows far more authoring style and content variation than that seen in traditional text document collections. This level of complexity makes an "off-the-shelf" database management and information retrieval solution impossible. To date, index based search engines for the Web have been the primary tool by which users search for information. Such engines can build giant indices that let you quickly retrieve the set of all Web pages containing a given word or string. Experienced users can make effective use of such engines for tasks that can be solved by searching for tightly constrained key words and phrases. These search engines are, however, unsuited for a wide range of equally important tasks. In particular, a topic of any breadth will typically contain several thousand or million relevant Web pages. How then, from this sea of pages, should a search engine select the correct ones-those of most value to the user? Clever is a search engine that analyzes hyperlinks to uncover two types of pages: authorities, which provide the best source of information on a given topic; and hubs, which provide collections of links to authorities. We outline the thinking that went into Clever's design, report briefly on a study that compared Clever's performance to that of Yahoo and AltaVista, and examine how our system is being extended and updated.
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paper_title: Finding and evaluating community structure in networks

paper_content:

We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.
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paper_title: Mining the Web's Link Structure

paper_content:

The Web is a hypertext body of approximately 300 million pages that continues to grow at roughly a million pages per day. Page variation is more prodigious than the data's raw scale: taken as a whole, the set of Web pages lacks a unifying structure and shows far more authoring style and content variation than that seen in traditional text document collections. This level of complexity makes an "off-the-shelf" database management and information retrieval solution impossible. To date, index based search engines for the Web have been the primary tool by which users search for information. Such engines can build giant indices that let you quickly retrieve the set of all Web pages containing a given word or string. Experienced users can make effective use of such engines for tasks that can be solved by searching for tightly constrained key words and phrases. These search engines are, however, unsuited for a wide range of equally important tasks. In particular, a topic of any breadth will typically contain several thousand or million relevant Web pages. How then, from this sea of pages, should a search engine select the correct ones-those of most value to the user? Clever is a search engine that analyzes hyperlinks to uncover two types of pages: authorities, which provide the best source of information on a given topic; and hubs, which provide collections of links to authorities. We outline the thinking that went into Clever's design, report briefly on a study that compared Clever's performance to that of Yahoo and AltaVista, and examine how our system is being extended and updated.
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paper_title: Terrorism Knowledge Discovery Project : A Knowledge Discovery Approach to Addressing the Threats of Terrorism

paper_content:

Ever since the 9–11 incident, the multidisciplinary field of terrorism has experienced tremendous growth. As the domain has benefited greatly from recent advances in information technologies, more complex and challenging new issues have emerged from numerous counter-terrorism-related research communities as well as governments of all levels. In this paper, we describe an advanced knowledge discovery approach to addressing terrorism threats. We experimented with our approach in a project called Terrorism Knowledge Discovery Project that consists of several custom-built knowledge portals. The main focus of this project is to provide advanced methodologies for analyzing terrorism research, terrorists, and the terrorized groups (victims). Once completed, the system can also become a major learning resource and tool that the general community can use to heighten their awareness and understanding of global terrorism phenomenon, to learn how best they can respond to terrorism and, eventually, to garner significant grass root support for the government’s efforts to keep America safe.
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paper_title: Identification of extremist videos in online video sharing sites

paper_content:

Web 2.0 has become an effective grassroots communication platform for extremists to promote their ideas, share resources, and communicate among each other. As an important component of Web 2.0, online video sharing sites such as YouTube and Google video have also been utilized by extremist groups to distribute videos. This study presented a framework for identifying extremist videos in online video sharing sites by using user-generated text content such as comments, video descriptions, and titles without downloading the videos. Text features including lexical features, syntactic features and content specific features were first extracted. Then Information Gain was used for feature selection, and Support Vector Machine was deployed for classification. The exploratory experiment showed that our proposed framework is effective for identifying online extremist videos, with the F-measure as high as 82%.
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paper_title: Multi-lingual detection of terrorist content on the web

paper_content:

Since the web is increasingly used by terrorist organizations for propaganda, disinformation, and other purposes, the ability to automatically detect terrorist-related content in multiple languages can be extremely useful. In this paper we describe a new, classification-based approach to multi-lingual detection of terrorist documents. The proposed approach builds upon the recently developed graph-based web document representation model combined with the popular C4.5 decision-tree classification algorithm. Evaluation is performed on a collection of 648 web documents in Arabic language. The results demonstrate that documents downloaded from several known terrorist sites can be reliably discriminated from the content of Arabic news reports using a simple decision tree.
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paper_title: Classifying racist texts using a support vector machine

paper_content:

In this poster we present an overview of the techniques we used to develop and evaluate a text categorisation system to automatically classify racist texts. Detecting racism is difficult because the presence of indicator words is insufficient to indicate racist texts, unlike some other text classification tasks. Support Vector Machines (SVM) are used to automatically categorise web pages based on whether or not they are racist. Different interpretations of what constitutes a term are taken, and in this poster we look at three representations of a web page within an SVM -- bag-of-words, bigrams and part-of-speech tags.
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paper_title: Dark web forums portal: Searching and analyzing jihadist forums

paper_content:

With the advent of Web 2.0, the Web is acting as a platform which enables end-user content generation. As a major type of social media in Web 2.0, Web forums facilitate intensive interactions among participants. International Jihadist groups often use Web forums to promote violence and distribute propaganda materials. These Dark Web forums are heterogeneous and widely distributed. Therefore, how to access and analyze the forum messages and interactions among participants is becoming an issue. This paper presents a general framework for Web forum data integration. Specifically, a Web-based knowledge portal, the Dark Web Forums Portal, is built based on the framework. The portal incorporates the data collected from different international Jihadist forums and provides several important analysis functions, including forum browsing and searching (in single forum and across multiple forums), forum statistics analysis, multilingual translation, and social network visualization. Preliminary results of our user study show that the Dark Web Forums Portal helps users locate information quickly and effectively. Users found the forum statistics analysis, multilingual translation, and social network visualization functions of the portal to be particularly valuable.
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paper_title: Topic-based social network analysis for virtual communities of interests in the Dark Web

paper_content:

The study of extremist groups and their interaction is a crucial task in order to maintain homeland security and peace. Tools such as social networks analysis and text mining have contributed to the understanding of this kind of groups in order to develop counter-terrorism applications. This work addresses the topic-based community key members extraction problem, for which our method combines both text mining and social network analysis techniques. This is achieved by first applying latent Dirichlet allocation to build two topic-based social networks: one social network oriented towards the thread creator point-of-view, and the other one oriented towards the repliers of the overall forum. Then, by using different Social Network Analysis measures, topic-based key members are evaluated using as benchmark a social network built using the plain documents. Experiments were performed using an English language based forum available in the Dark Web portal.
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paper_title: The topology of dark networks

paper_content:

Knowing the structure of criminal and terrorist networks could provide the technical insight needed to disrupt their activities.
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paper_title: Mining communities and their relationships in blogs: A study of online hate groups

paper_content:

Blogs, often treated as the equivalence of online personal diaries, have become one of the fastest growing types of Web-based media. Everyone is free to express their opinions and emotions very easily through blogs. In the blogosphere, many communities have emerged, which include hate groups and racists that are trying to share their ideology, express their views, or recruit new group members. It is important to analyze these virtual communities, defined based on membership and subscription linkages, in order to monitor for activities that are potentially harmful to society. While many Web mining and network analysis techniques have been used to analyze the content and structure of the Web sites of hate groups on the Internet, these techniques have not been applied to the study of hate groups in blogs. To address this issue, we have proposed a semi-automated approach in this research. The proposed approach consists of four modules, namely blog spider, information extraction, network analysis, and visualization. We applied this approach to identify and analyze a selected set of 28 anti-Blacks hate groups (820 bloggers) on Xanga, one of the most popular blog hosting sites. Our analysis results revealed some interesting demographical and topological characteristics in these groups, and identified at least two large communities on top of the smaller ones. The study also demonstrated the feasibility in applying the proposed approach in the study of hate groups and other related communities in blogs.
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paper_title: On the Topology of the Dark Web of Terrorist Groups

paper_content:

In recent years, terrorist groups have used the WWW to spread their ideologies, disseminate propaganda, and recruit members. Studying the terrorist websites may help us understand the characteristics of these websites and predict terrorist activities. In this paper, we propose to apply network topological analysis methods on systematically collected the terrorist website data and to study the structural characteristics at the Web page level. We conducted a case study using the methods on three collections of Middle-Eastern, US domestic, and Latin-American terrorist websites. We found that these three networks have the small-world and scale-free characteristics. We also found that smaller size websites which share same interests tend to make stronger inter-website linkage relationships.
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paper_title: Sentiment and affect analysis of Dark Web forums: Measuring radicalization on the internet

paper_content:

Dark Web forums are heavily used by extremist and terrorist groups for communication, recruiting, ideology sharing, and radicalization. These forums often have relevance to the Iraqi insurgency or Al-Qaeda and are of interest to security and intelligence organizations. This paper presents an automated approach to sentiment and affect analysis of selected radical international Ahadist Dark Web forums. The approach incorporates a rich textual feature representation and machine learning techniques to identify and measure the sentiment polarities and affect intensities expressed in forum communications. The results of sentiment and affect analysis performed on two large-scale Dark Web forums are presented, offering insight into the communities and participants.
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paper_title: Mining communities and their relationships in blogs: A study of online hate groups

paper_content:

Blogs, often treated as the equivalence of online personal diaries, have become one of the fastest growing types of Web-based media. Everyone is free to express their opinions and emotions very easily through blogs. In the blogosphere, many communities have emerged, which include hate groups and racists that are trying to share their ideology, express their views, or recruit new group members. It is important to analyze these virtual communities, defined based on membership and subscription linkages, in order to monitor for activities that are potentially harmful to society. While many Web mining and network analysis techniques have been used to analyze the content and structure of the Web sites of hate groups on the Internet, these techniques have not been applied to the study of hate groups in blogs. To address this issue, we have proposed a semi-automated approach in this research. The proposed approach consists of four modules, namely blog spider, information extraction, network analysis, and visualization. We applied this approach to identify and analyze a selected set of 28 anti-Blacks hate groups (820 bloggers) on Xanga, one of the most popular blog hosting sites. Our analysis results revealed some interesting demographical and topological characteristics in these groups, and identified at least two large communities on top of the smaller ones. The study also demonstrated the feasibility in applying the proposed approach in the study of hate groups and other related communities in blogs.
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paper_title: A multi-region empirical study on the internet presence of global extremist organizations

paper_content:

Extremist organizations are heavily utilizing Internet technologies to increase their abilities to influence the world. Studying those global extremist organizations' Internet presence would allow us to better understand extremist organizations' technical sophistication and their propaganda plans. In this work, we explore an integrated approach for collecting and analyzing extremist Internet presence. We employed automatic Web crawling techniques to build a comprehensive international extremist Web collection. We then used a systematic content analysis tool called the Dark Web Attribute System to analyze and compare these extremist organizations' Internet usage from three perspectives: technical sophistication, content richness, and Web interactivity. By studying 1.7 million multimedia Web documents from around 224 Web sites of extremist organizations, we found that while all extremist organizations covered in this study demonstrate high level of technical sophistication in their Web presence, Middle Eastern extremists are among the most sophisticated groups in both technical sophistication and media richness. US groups are the most active in supporting Internet communications. Our analysis results will help domain experts deepen their understanding on the global extremism movements and make better counter-extremism measures on the Internet.
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paper_title: Affect Intensity Analysis of Dark Web Forums

paper_content:

Affects play an important role in influencing people's perceptions and decision making. Affect analysis is useful for measuring the presence of hate, violence, and the resulting propaganda dissemination across extremist groups. In this study we performed affect analysis of U.S. and Middle Eastern extremist group forum postings. We constructed an affect lexicon using a probabilistic disambiguation technique to measure the usage of violence and hate affects. These techniques facilitate in depth analysis of multilingual content. The proposed approach was evaluated by applying it across 16 U.S. supremacist and Middle Eastern extremist group forums. Analysis across regions reveals that the Middle Eastern test bed forums have considerably greater violence intensity than the U.S. groups. There is also a strong linear relationship between the usage of hate and violence across the Middle Eastern messages.
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paper_title: Discovering topics from dark websites

paper_content:

Analysis of dark websites is important for developing effective combating strategies against terrorism or extremists when more and more scattered terrorist cells use the ubiquity of the Internet to form communities in virtual space with fairly low costs. Terrorists or extremists anonymously set up various web sites embedded in the public Internet, exchanging ideology, spreading propaganda, and recruiting new members. In this paper, we propose a framework to discover latent topics via analyzing contents of dark websites. The content and data from dark websites are gathered and extracted by crawlers and exported to documents. Latent Dirichlet Allocation (LDA) algorithm is used to analyze the extracted documents so as to discover latent topics from web sites of terrorists or extremists. In contrast to the traditional Information Retrieval (IR) schemes, LDA-based analysis assigns a probability to a document and captures exchangeability of both words and documents. Our work helps to gain insights into the structure and communities of terrorists and extremists.
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paper_title: Anomaly detection in extremist web forums using a dynamical systems approach

paper_content:

In this paper, we present preliminary results of analyzing data from the Dark Web collection using a dynamical systems approach for unsupervised anomaly detection. The goal is to provide a robust, focus-of-attention mechanism to identify emerging threats in time-dependent, unlabelled data sets. In our method, finite-time Lyapunov exponents are used to characterize the time evolution of both the directed network structure and the distribution of text attributes in the forum messages. We provide a description of the technique and a summary of the initial anomaly detection results. We conclude with a summary of results and a brief discussion of promising avenues for future research.
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paper_title: Mapping Dark Web Geolocation

paper_content:

In this paper we first provide a brief review of the Dark Web project of the University of Arizona Artificial Intelligence Lab. We then report our research design and case study that aim to identify the geolocation of the countries, cities, and ISPs that host selected international Jihadist web sites. We provide an overview of key relevant Internet functionality and architecture and present techniques for exploiting networking technologies for locating servers and resources. Significant findings from our case study and suggestion for future research are also presented.
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paper_title: Mining communities and their relationships in blogs: A study of online hate groups

paper_content:

Blogs, often treated as the equivalence of online personal diaries, have become one of the fastest growing types of Web-based media. Everyone is free to express their opinions and emotions very easily through blogs. In the blogosphere, many communities have emerged, which include hate groups and racists that are trying to share their ideology, express their views, or recruit new group members. It is important to analyze these virtual communities, defined based on membership and subscription linkages, in order to monitor for activities that are potentially harmful to society. While many Web mining and network analysis techniques have been used to analyze the content and structure of the Web sites of hate groups on the Internet, these techniques have not been applied to the study of hate groups in blogs. To address this issue, we have proposed a semi-automated approach in this research. The proposed approach consists of four modules, namely blog spider, information extraction, network analysis, and visualization. We applied this approach to identify and analyze a selected set of 28 anti-Blacks hate groups (820 bloggers) on Xanga, one of the most popular blog hosting sites. Our analysis results revealed some interesting demographical and topological characteristics in these groups, and identified at least two large communities on top of the smaller ones. The study also demonstrated the feasibility in applying the proposed approach in the study of hate groups and other related communities in blogs.
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Title: Solutions to Detect and Analyze Online Radicalization: A Survey

Section 1: INTRODUCTION
Description 1: This section introduces the concept of online radicalization and outlines its significance and challenges.

Section 2: Countering Online Radicalization : Technical Challenges
Description 2: This section describes the technical challenges faced by security analysts when detecting and analyzing radical online content.

Section 3: Survey: Problem Definition
Description 3: This section presents the objectives and scope of the survey, including the attributes identified for literature.

Section 4: Contributions
Description 4: This section outlines the primary contributions of the survey to the field of automated solutions for online radicalization detection and analysis.

Section 5: LITERATURE REVIEW
Description 5: This section reviews previous work and research methodologies on the subject of automated detection and analysis of online radicalization.

Section 6: FACETS FOR ARTICLE CLASSIFICATION
Description 6: This section discusses the classification scheme used to categorize the literature on online radicalization detection and analysis.

Section 7: Online Radicalization Detection : Facets
Description 7: This section elaborates on facets specific to the detection of online radicalization, including techniques, data types, modalities, and evaluation metrics.

Section 8: Online Radicalization Analysis : Facets
Description 8: This section discusses facets pertaining to the analysis of online radicalization, focusing on network and content-based analysis, modalities, and evaluation metrics.

Section 9: SURVEY OF AUTOMATED SOLUTIONS FOR ONLINE RADICALIZATION DETECTION
Description 9: This section provides an in-depth survey of various techniques used to detect online radicalization automatically.

Section 10: Link-Based Bootstrapping (LBB) Techniques for Detecting Online Radicalization
Description 10: This section focuses on LBB techniques and their application in detecting online radical content.

Section 11: Text Classification Techniques for Detecting Online Radicalization
Description 11: This section examines the use of text classification techniques to detect radical content online.

Section 12: Other Techniques for Detecting Online Radicalization
Description 12: This section highlights alternative methods and approaches for detecting online radicalization.

Section 13: SURVEY OF SOLUTIONS FOR ONLINE RADICALIZATION ANALYSIS
Description 13: This section surveys techniques used for analyzing online radical content.

Section 14: Network-Based Analysis for Online Radical Content
Description 14: This section explores the use of network-based analysis techniques to understand the structure and behavior of radical groups online.

Section 15: Content-Based Analysis for Online Radical Content
Description 15: This section delves into content-based analysis methods to examine the nature and purpose of extremist content.

Section 16: Other Solutions for Analyzing Online Radical Content
Description 16: This section presents additional approaches used to analyze online radical content.

Section 17: DISCUSSION
Description 17: This section discusses observed trends, limitations, and synthesizes insights drawn from the surveyed literature.

Section 18: RESEARCH GAPS
Description 18: This section identifies gaps and open research problems in the domain of detecting and analyzing online radicalization.

Section 19: CONCLUSION
Description 19: This section concludes the survey, summarizing the key findings, contributions, and suggesting potential future research directions.