Recent terrorist attacks carried out on behalf of ISIS on American and European soil by lone wolf attackers or sleeper cells remind us of the importance of understanding the dynamics of radicalization mediated by social media communication channels. In this paper, we shed light on the social media activity of a group of twenty-five thousand users whose association with ISIS online radical propaganda has been manually verified. By using a computational tool known as dynamical activity-connectivity maps, based on network and temporal activity patterns, we investigate the dynamics of social influence within ISIS supporters. We finally quantify the effectiveness of ISIS propaganda by determining the adoption of extremist content in the general population and draw a parallel between radical propaganda and epidemics spreading, highlighting that information broadcasters and influential ISIS supporters generate highly-infectious cascades of information contagion. Our findings will help generate effective countermeasures to combat the group and other forms of online extremism.
One of the first computational frameworks, proposed by Bermingham @cite_26 in 2009, combined social network analysis with sentiment detection tools to study the agenda of a radical YouTube group: the authors examined the topics discussed within the group and their polarity, to model individuals' behavior and spot signs of extremism and intolerance, seemingly more prominent among female users. The detection of extremist content (on the Web) was also the focus of a 2010 work by Qi @cite_52 . The authors applied hierarchical clustering to extremist Web pages to divide them into different pre-imposed categories (religious, anti immigration, etc.).
@cite_26 @cite_52
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Extremist political movements have proliferated on the web in recent years due to the advent of minimal publication costs coupled with near universal access, resulting in what appears to be an abundance of groups that hover on the fringe of many socially divisive issues. Whether white-supremacist, neo- Nazi, anti-abortion, black separatist, radical Christian, animal rights, or violent environmentalists, all have found a home (and voice) on the Web. These groups form social networks whose ties are predicated primarily on shared political goals. Little is known about these groups, their interconnections, their animosities, and most importantly, their growth and development and studies such as the Dark Web Project, while considering domestic extremists, have focused primarily on international terrorist groups. Yet here in the US, there has been a complex social dynamic unfolding as well. While left-wing radicalism declined throughout the 80s and 90s, right wing hate groups began to flourish. Today, the web offers a place for any brand of extremism, but little is understood about their current growth and development. While there is much to gain from in-depth studies of the content provided by these sites, there is also a surprising amount of information contained in their online network structure as manifested in links between and among these web sites. Our research follows the idea that much can be known about you by the company you keep. In this paper, we propose an approach to measure the intrinsic relationships (i.e., similarities) of a set of extremist web pages. In this model, the web presence of a group is thought of as a node in a social network and the links between these pages are the ties between groups. This approach takes the bi-directional hyperlink structure of web pages and, based on similarity scores, applies an effective multi-membership clustering algorithm known as the quasi clique merger method to cluster these web pages using a derived hierarchical tree. The experimental results show that this new similarity measurement and hierarchical clustering algorithm gives an improvement over traditional link based clustering methods. Growing use of the Internet as a major means of communication has led to the formation of cyber-communities, which have become increasingly appealing to terrorist groups due to the unregulated nature of Internet communication. Online communities enable violent extremists to increase recruitment by allowing them to build personal relationships with a worldwide audience capable of accessing uncensored content. This article presents methods for identifying the recruitment activities of violent groups within extremist social media websites. Specifically, these methods apply known techniques within supervised learning and natural language processing to the untested task of automatically identifying forum posts intended to recruit new violent extremist members. We used data from the western jihadist website Ansar AlJihad Network, which was compiled by the University of Arizona’s Dark Web Project. Multiple judges manually annotated a sample of these data, marking 192 randomly sampled posts as recruiting (Yes) or non-recruiting (No). We observed significant agreement between the judges’ labels; Cohen’s κ=(0.5,0.9) at p=0.01. We tested the feasibility of using naive Bayes models, logistic regression, classification trees, boosting, and support vector machines (SVM) to classify the forum posts. Evaluation with receiver operating characteristic (ROC) curves shows that our SVM classifier achieves an 89 area under the curve (AUC), a significant improvement over the 63 AUC performance achieved by our simplest naive Bayes model (Tukey’s test at p=0.05). To our knowledge, this is the first result reported on this task, and our analysis indicates that automatic detection of online terrorist recruitment is a feasible task. We also identify a number of important areas of future work including classifying non-English posts and measuring how recruitment posts and current events change membership numbers over time. This paper addresses the important problem of discerning hateful content in social media. We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features associated with user-related information, such as the users' tendency towards racism or sexism. These data are fed as input to the above classifiers along with the word frequency vectors derived from the textual content. Our approach has been evaluated on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state of the art solutions. More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms. We describe a semi-automated system to assist law enforcement and intelligence agencies dealing with cyber-crime related to promotion of hate and radicalization on the Internet. The focus of this work is on mining YouTube to discover hate videos, users and virtual hidden communities. Finding precise information on YouTube is a challenging task because of the huge size of the YouTube repository and a large subscriber base. We present a solution based on data mining and social network analysis (using a variety of relationships such as friends, subscriptions, favorites and related videos) to aid an analyst in discovering insightful and actionable information. Furthermore, we performed a systematic study of the features and properties of the data and hidden social networks which has implications in understanding extremism on Internet. We take a case study based approach and perform empirical validation of the proposed hypothesis. Our approach succeeded in finding hate videos which were validated manually. The Internet and social media have fuelled enormous interest in social network analysis. New tools continue to be developed and used to analyse our personal connections, with particular emphasis on detecting communities or identifying key individuals in a social network. This raises privacy concerns that are likely to exacerbate in the future. With this in mind, we ask the question ‘Can individuals or groups actively manage their connections to evade social network analysis tools?’ By addressing this question, the general public may better protect their privacy, oppressed activist groups may better conceal their existence and security agencies may better understand how terrorists escape detection. We first study how an individual can evade ‘node centrality’ analysis while minimizing the negative impact that this may have on his or her influence. We prove that an optimal solution to this problem is difficult to compute. Despite this hardness, we demonstrate how even a simple heuristic, whereby attention is restricted to the individual’s immediate neighbourhood, can be surprisingly effective in practice; for example, it could easily disguise Mohamed Atta’s leading position within the World Trade Center terrorist network. We also study how a community can increase the likelihood of being overlooked by community-detection algorithms. We propose a measure of concealment—expressing how well a community is hidden—and use it to demonstrate the effectiveness of a simple heuristic, whereby members of the community either ‘unfriend’ certain other members or ‘befriend’ some non-members in a coordinated effort to camouflage their community. The pervasiveness of Web 2.0 and social networking sites has enabled people to interact with each other easily through various social media. For instance, popular sites like Del.icio.us, Flickr, and YouTube allow users to comment on shared content (bookmarks, photos, videos), and users can tag their favorite content. Users can also connect with one another, and subscribe to or become a fan or a follower of others. These diverse activities result in a multi-dimensional network among actors, forming group structures with group members sharing similar interests or affiliations. This work systematically addresses two challenges. First, it is challenging to effectively integrate interactions over multiple dimensions to discover hidden community structures shared by heterogeneous interactions. We show that representative community detection methods for single-dimensional networks can be presented in a unified view. Based on this unified view, we present and analyze four possible integration strategies to extend community detection from single-dimensional to multi-dimensional networks. In particular, we propose a novel integration scheme based on structural features. Another challenge is the evaluation of different methods without ground truth information about community membership. We employ a novel cross-dimension network validation (CDNV) procedure to compare the performance of different methods. We use synthetic data to deepen our understanding, and real-world data to compare integration strategies as well as baseline methods in a large scale. We study further the computational time of different methods, normalization effect during integration, sensitivity to related parameters, and alternative community detection methods for integration. Online video sharing platforms such as YouTube contains several videos and users promoting hate and extremism. Due to low barrier to publication and anonymity, YouTube is misused as a platform by some users and communities to post negative videos disseminating hatred against a particular religion, country or person. We formulate the problem of identification of such malicious videos as a search problem and present a focused-crawler based approach consisting of various components performing several tasks: search strategy or algorithm, node similarity computation metric, learning from exemplary profiles serving as training data, stopping criterion, node classifier and queue manager. We implement a best-first search algorithm and conduct experiments to measure the accuracy of the proposed approach. Experimental results demonstrate that the proposed approach is effective. In online social networks people often express attitudes towards others, which forms massive sentiment links among users. Predicting the sign of sentiment links is a fundamental task in many areas such as personal advertising and public opinion analysis. Previous works mainly focus on textual sentiment classification, however, text information can only disclose the "tip of the iceberg»» about users» true opinions, of which the most are unobserved but implied by other sources of information such as social relation and users» profile. To address this problem, in this paper we investigate how to predict possibly existing sentiment links in the presence of heterogeneous information. First, due to the lack of explicit sentiment links in mainstream social networks, we establish a labeled heterogeneous sentiment dataset which consists of users» sentiment relation, social relation and profile knowledge by entity-level sentiment extraction method. Then we propose a novel and flexible end-to-end Signed Heterogeneous Information Network Embedding (SHINE) framework to extract users» latent representations from heterogeneous networks and predict the sign of unobserved sentiment links. SHINE utilizes multiple deep autoencoders to map each user into a low-dimension feature space while preserving the network structure. We demonstrate the superiority of SHINE over state-of-the-art baselines on link prediction and node recommendation in two real-world datasets. The experimental results also prove the efficacy of SHINE in cold start scenario. Many networks can be modeled as signed graphs. These include social networks, and relationships interactions networks. Detecting sub-structures in such networks helps us understand user behavior, predict links, and recommend products. In this paper, we detect dense sub-structures from a signed graph, called quasi antagonistic communities (QACs). An antagonistic community consists of two groups of users expressing positive relationships within each group but negative relationships across groups. Instead of requiring complete set of negative links across its groups, a QAC allows a small number of inter-group negative links to be missing. We propose an algorithm, Mascot, to find all maximal quasi antagonistic communities (MQACs). Mascot consists of two stages: pruning and enumeration stages. Based on the properties of QAC, we propose four pruning rules to reduce the size of candidate graphs in the pruning stage. We use an enumeration tree to enumerate all strongly connected subgraphs in a top---down fashion in the second stage before they are used to construct MQACs. We have conducted extensive experiments using synthetic signed graphs and two real networks to demonstrate the efficiency and accuracy of the Mascot algorithm. We have also found that detecting MQACs helps us to predict the signs of links. The paper explores the use of concepts in cognitive psychology to evaluate the spread of misinformation, disinformation and propaganda in online social networks. Analysing online social networks to identify metrics to infer cues of deception will enable us to measure diffusion of misinformation. The cognitive process involved in the decision to spread information involves answering four main questions viz consistency of message, coherency of message, credibility of source and general acceptability of message. We have used the cues of deception to analyse these questions to obtain solutions for preventing the spread of misinformation. We have proposed an algorithm to effectively detect deliberate spread of false information which would enable users to make informed decisions while spreading information in social networks. The computationally efficient algorithm uses the collaborative filtering property of social networks to measure the credibility of sources of information as well as quality of news items. The validation of the proposed methodology has been done on the online social network Twitter’. This paper provides a novel algorithm to automatically detect the hidden hierarchy in terrorist networks. The algorithm is based on centrality measures used in social network analysis literature. The advantage of such automatic methods is to detect key players in terrorist networks. We illustrate the algorithm over some case studies of terrorist events that have occurred in the past. The results show great promise in detecting high value individuals. With the pervasive availability of Web 2.0 and social networking sites, people can interact with each other easily through various social media. For instance, popular sites like Del.icio.us, Flickr, and YouTube allow users to comment shared content (bookmark, photos, videos), and users can tag their own favorite content. Users can also connect to each other, and subscribe to or become a fan or a follower of others. These diverse individual activities result in a multi-dimensional network among actors, forming cross-dimension group structures with group members sharing certain similarities. It is challenging to effectively integrate the network information of multiple dimensions in order to discover cross-dimension group structures. In this work, we propose a two-phase strategy to identify the hidden structures shared across dimensions in multi-dimensional networks. We extract structural features from each dimension of the network via modularity analysis, and then integrate them all to find out a robust community structure among actors. Experiments on synthetic and real-world data validate the superiority of our strategy, enabling the analysis of collective behavior underneath diverse individual activities in a large scale. The widespread adoption of social media for political communication creates unprecedented opportunities to monitor the opinions of large numbers of politically active individuals in real time. However, without a way to distinguish between users of opposing political alignments, conflicting signals at the individual level may, in the aggregate, obscure partisan differences in opinion that are important to political strategy. In this article we describe several methods for predicting the political alignment of Twitter users based on the content and structure of their political communication in the run-up to the 2010 U.S. midterm elections. Using a data set of 1,000 manually-annotated individuals, we find that a support vector machine (SVM) trained on hash tag metadata outperforms an SVM trained on the full text of users' tweets, yielding predictions of political affiliations with 91 accuracy. Applying latent semantic analysis to the content of users' tweets we identify hidden structure in the data strongly associated with political affiliation, but do not find that topic detection improves prediction performance. All of these content-based methods are outperformed by a classifier based on the segregated community structure of political information diffusion networks (95 accuracy). We conclude with a practical application of this machinery to web-based political advertising, and outline several approaches to public opinion monitoring based on the techniques developed herein. There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially “viral” ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network’s community structure and core-periphery structure. This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political “memes” over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks. The identification of popular and important topics discussed in social networks is crucial for a better understanding of societ al concerns. It is also useful for users to stay on top of trends without having to sift through vast amounts of shared information. Trend detection methods introduced so far have not used the network topology and has thus not been able to distinguish viral topics from topics that are diffused mostly through the news media. To address this gap, we propose two novel structural trend definitions we call coordinated and uncoordinated trends that use friendship information to identify topics that are discussed among clustered and distributed users respectively. Our analyses and experiments show that structural trends are significantly different from traditional trends and provide new insights into the way people share information online. We also propose a sampling technique for structural trend detection and prove that the solution yields in a gain in efficiency and is within an acceptable error bound. Experiments performed on a Twitter data set of 41.7 million nodes and 417 million posts show that even with a sampling rate of 0.005, the average precision is 0.93 for coordinated trends and 1 for uncoordinated trends. Graph clustering has been widely applied in exploring regularities emerging in relational data. Recently, the rapid development of network theory correlates graph clustering with the detection of community structure, a common and important topological characteristic of networks. Most existing methods investigate the community structure at a single topological scale. However, as shown by empirical studies, the community structure of real world networks often exhibits multiple topological descriptions, corresponding to the clustering at different resolutions. Furthermore, the detection of multiscale community structure is heavily affected by the heterogeneous distribution of node degree. It is very challenging to detect multiscale community structure in heterogeneous networks. In this paper, we propose a novel, unified framework for detecting community structure from the perspective of dimensionality reduction. Based on the framework, we first prove that the well-known Laplacian matrix for network partition and the widely-used modularity matrix for community detection are two kinds of covariance matrices used in dimensionality reduction. We then propose a novel method to detect communities at multiple topological scales within our framework. We further show that existing algorithms fail to deal with heterogeneous node degrees. We develop a novel method to handle heterogeneity of networks by introducing a rescaling transformation into the covariance matrices in our framework. Extensive tests on real world and artificial networks demonstrate that the proposed correlation matrices significantly outperform Laplacian and modularity matrices in terms of their ability to identify multiscale community structure in heterogeneous networks. Political inclinations of individuals (liberal vs. conservative) largely shape their opinions on several issues such as abortion, gun control, nuclear power, etc. These opinions are openly exerted inonline forums, news sites, the parliament, and so on. In this paper, we address the problem of quantifying political polarity of individuals and of political issues for classification and ranking. We use signed bipartite networks to represent the opinions of individuals on issues, and formulate the problem as a node classification task. We propose a linear algorithm that exploits network effects to learn both the polarity labels as well as the rankings of people and issues in a completely unsupervised manner. Through extensive experiments we demonstrate that our proposed method provides an effective, fast, and easy-to-implement solution, while outperforming three existing baseline algorithms adapted to signed networks, on real political forum and US Congress datasets.Experiments on a wide variety of synthetic graphs with varying polarity and degree distributions of the nodes further demonstrate the robustness of our approach. People are shifting from traditional news sources to online news at an incredibly fast rate. However, the technology behind online news consumption promotes content that confirms the users» existing point of view. This phenomenon has led to polarization of opinions and intolerance towards opposing views. Thus, a key problem is to model information filter bubbles on social media and design methods to eliminate them. In this paper, we use a machine-learning approach to learn a liberal-conservative ideology space on Twitter, and show how we can use the learned latent space to tackle the filter bubble problem. We model the problem of learning the liberal-conservative ideology space of social media users and media sources as a constrained non-negative matrix-factorization problem. Our model incorporates the social-network structure and content-consumption information in a joint factorization problem with shared latent factors. We validate our model and solution on a real-world Twitter dataset consisting of controversial topics, and show that we are able to separate users by ideology with over 90 purity. When applied to media sources, our approach estimates ideology scores that are highly correlated(Pearson correlation 0.9) with ground-truth ideology scores. Finally, we demonstrate the utility of our model in real-world scenarios, by illustrating how the learned ideology latent space can be used to develop exploratory and interactive interfaces that can help users in diffusing their information filter bubble. Recently community detection has attracted much interest in social media to understand the collective behaviours of users and allow individuals to be modeled in the context of the group. Most existing approaches for community detection exploit either users' social links or their published content, aiming at discovering groups of densely connected or highly similar users. They often fail to find effective communities due to excessive noise in content, sparsity in links, and heterogenous behaviours of users in social media. Further, they are unable to provide insights and rationales behind the formation of the group and the collective behaviours of the users. To tackle these challenges, we propose to discover communities in a low- dimensional latent space in which we simultaneously learn the representation of users and communities. In particular, we integrated different social views of the network into a low-dimensional latent space in which we sought dense clusters of users as communities. By imposing a Laplacian regularizer into affiliation matrix, we further incorporated prior knowledge into the community discovery process. Finally community profiles were computed by a linear operator integrating the profiles of members. Taking the wellness domain as an example, we conducted experiments on a large scale real world dataset of users tweeting about diabetes and its related concepts, which demonstrate the effectiveness of our approach in discovering and profiling user communities. Web 2.0 has led to the development and evolution of web-based communities and applications. These communities provide places for information sharing and collaboration. They also open t he door for inappropriate online activities, such as harassment, i n which some users post messages in a virtual community that are intention- ally offensive to other members of the community. It is a new and challenging task to detect online harassment; currently fe w systems attempt to solve this problem. In this paper, we use a supervised learning approach for dete ct- ing harassment. Our technique employs content features, sentiment features, and contextual features of documents. The experi mental results described herein show that our method achieves significant improvements over several baselines, including Term Frequency- Inverse Document Frequency (TFIDF) approaches. Identifica tion of online harassment is feasible when TFIDF is supplemented with sentiment and contextual feature attributes.