Solutions to Detect and Analyze Online Radicalization : A Survey

Online Radicalization (also called Cyber-Terrorism or Extremism or Cyber-Racism or Cyber- Hate) is widespread and has become a major and growing concern to the society, governments and law enforcement agencies around the world. Research shows that various platforms on the Internet (low barrier to publish content, allows anonymity, provides exposure to millions of users and a potential of a very quick and widespread diffusion of message) such as YouTube (a popular video sharing website), Twitter (an online micro-blogging service), Facebook (a popular social networking website), online discussion forums and blogosphere are being misused for malicious intent. Such platforms are being used to form hate groups, racist communities, spread extremist agenda, incite anger or violence, promote radicalization, recruit members and create virtual organi- zations and communities. Automatic detection of online radicalization is a technically challenging problem because of the vast amount of the data, unstructured and noisy user-generated content, dynamically changing content and adversary behavior. There are several solutions proposed in the literature aiming to combat and counter cyber-hate and cyber-extremism. In this survey, we review solutions to detect and analyze online radicalization. We review 40 papers published at 12 venues from June 2003 to November 2011. We present a novel classification scheme to classify these papers. We analyze these techniques, perform trend analysis, discuss limitations of existing techniques and find out research gaps

Detecting Cohesive and 2-mode Communities in Directed and Undirected Networks

Networks are a general language for representing relational information among objects. An effective way to model, reason about, and summarize networks, is to discover sets of nodes with common connectivity patterns. Such sets are commonly referred to as network communities. Research on network community detection has predominantly focused on identifying communities of densely connected nodes in undirected networks. In this paper we develop a novel overlapping community detection method that scales to networks of millions of nodes and edges and advances research along two dimensions: the connectivity structure of communities, and the use of edge directedness for community detection. First, we extend traditional definitions of network communities by building on the observation that nodes can be densely interlinked in two different ways: In cohesive communities nodes link to each other, while in 2-mode communities nodes link in a bipartite fashion, where links predominate between the two partitions rather than inside them. Our method successfully detects both 2-mode as well as cohesive communities, that may also overlap or be hierarchically nested. Second, while most existing community detection methods treat directed edges as though they were undirected, our method accounts for edge directions and is able to identify novel and meaningful community structures in both directed and undirected networks, using data from social, biological, and ecological domains.Comment: Published in the proceedings of WSDM '1

FINDING HER MASTER’S VOICE: THE POWER OF COLLECTIVE ACTION AMONG FEMALE MUSLIM BLOGGERS

Emerging cyber-collective movements have frequently made headlines in the news. Despite the exponential growth of bloggers in Muslim countries, there is a lack of empirical study of cyber-collective actions in these countries. We analyzed the female Muslim blogosphere because very little research attempts to understand socio-political roles of female bloggers in the system where women are frequently denied freedom of expression. We collected 150 blogs from 17 countries ranging between April 2003 and July 2010 with a special focus on Al-Huwaider’s campaigns for our analysis. Bearing the analysis upon three central tenets of individual, community, and transnational perspectives, we develop novel algorithms modeling cyber-collective movements by utilizing existing social theories on collective action and computational social network analysis. This paper contributes a methodology to study the diffusion of issues in social networks and examines roles of influential community members. We also observe the transcending nature of cyber-collective movements with future possibilities for modeling transnational outreach. Using the global female Muslim blogosphere, we provide understanding of the complexity and dynamics of cyber-collective action. To the best of our knowledge, our research is the first to address the lacking fundamental research shedding light on re-framing collective action theory in online environments

Online Shaming

Online shaming is a subject of import for social philosophy in the Internet age, and not simply because shaming seems generally bad. I argue that social philosophers are well-placed to address the imaginal relationships we entertain when we engage in social media; activity in cyberspace results in more relationships than one previously had, entailing new and more responsibilities, and our relational behaviors admit of ethical assessment. I consider the stresses of social media, including the indefinite expansion of our relationships and responsibilities, and the gap between the experiences of those shamed and the shamers’ appreciation of the magnitude of what they do when they shame; I connect these to the literature suggesting that some intuitions fail to guide our ethics. I conclude that we each have more power than we believe we do or than we think carefully about exerting in our online imaginal relations. Whether we are the shamers or the shamed, we are unable to control the extent to which intangible words in cyberspace take the form of imaginal relationships that burden or brighten our self-perceptions

Automatic Full Text Analysis in Public Social Media – Adoption of a Software Prototype to Investigate Political Communication

AbstractThe relevance of web 2.0 and social networks has increased rapidly in the last years. As people use different networks for different purposes, communication is a common goal. Activities in virtual social networks result in a massive amount of information. User-generated content is clustered respect to platform, way of communication as well as quality and richness of content. Often the subject of information exchange deals with personal issues concerning events and individuals. Until now this information is not considered by means of classic information retrieval. Identifying distinct subjects, gathering and analyzing information and putting them into relationship at once are open scientific challenges. This contribution describes an approach to partial aspects of this problem by providing a prototype for full text analysis in social networks. Furthermore, based on a test scenario of analyzing postings in the field of political communication, potentials and limitations of the prototype are discussed