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

Topicality and Social Impact: Diverse Messages but Focused Messengers

Are users who comment on a variety of matters more likely to achieve high influence than those who delve into one focused field? Do general Twitter hashtags, such as #lol, tend to be more popular than novel ones, such as #instantlyinlove? Questions like these demand a way to detect topics hidden behind messages associated with an individual or a hashtag, and a gauge of similarity among these topics. Here we develop such an approach to identify clusters of similar hashtags by detecting communities in the hashtag co-occurrence network. Then the topical diversity of a user's interests is quantified by the entropy of her hashtags across different topic clusters. A similar measure is applied to hashtags, based on co-occurring tags. We find that high topical diversity of early adopters or co-occurring tags implies high future popularity of hashtags. In contrast, low diversity helps an individual accumulate social influence. In short, diverse messages and focused messengers are more likely to gain impact.Comment: 9 pages, 7 figures, 6 table

A categorisation framework for a feature-level analysis of social network sites

Social media (SM) have enabled new forms of communication, interaction, and connectivity that affect individuals on a personal and professional level. But SM is a broad term that encompasses a wide range of technologies with both distinct and shared capabilities. In addition, while there is an agreed-upon definition of these systems, a comprehensive list of features and their affordances does not exist. Hence, this study sought to create a feature-level categorisation framework for analysing the use of social network sites (SNS). This categorisation was undertaken using the concept of affordances, which framed the high-level characteristics as well as distinct SNS features, to better understand the divergence in SNS capabilities and inform the study of different types of SM. The framework was created from an analysis of the literature on SNS affordances and a system investigation into three types of SNS (Facebook, YouTube and Twitter). The comprehensive review was undertaken using two families of SNS affordances (social and content affordances) identified in the literature to categorise and compare the platforms. The study reveals a diverse collection of features which afford behaviour in six areas of activity: profile building, social connectivity, social interactivity, content discovery, content sharing and content aggregation. Finally, the framework provides a basis from which the usage and management of SM within organisations can be more rigorously investigated

Exploring the affordances of social networking sites: an analysis of three networks

Social network sites (SNS) are becoming increasingly important, both for individuals and organizations. These systems have affected social and cultural activities, work practices, and in particular the ways in which we discover, share and consume information goods. The functionality of SNS is emergent, shaped by user appropriation choices. In this paper, affordances are proposed as a way to understand the potential uses and future evolution of SNS. Affordances describe the characteristics of an interactive system which suggests how the system should be used. The objective of this study is to explore the affordances of SNS. The study comprises an inventory of the affordances of three popular SNS. The study reveals a diverse collection of software features which afford user behaviour in six areas of activity: social connectivity, social interactivity, profile management, content discovery, content sharing and content aggregation. The findings of the study provide a rich foundation for future research on user appropriation of SNS, the future evolution of SNS, and the design of SNS systems

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

EXPLORING THE AFFORDANCES OF SOCIAL NETWORK SITES: AN ANALYSIS OF THREE NETWORKS

Social network sites (SNS) are becoming increasingly important, both for individuals and organizations. These systems have affected social and cultural activities, work practices, and in particular the ways in which we discover, share and consume information goods. The functionality of SNS is emergent, shaped by user appropriation choices. In this paper, affordances are proposed as a way to understand the potential uses and future evolution of SNS. Affordances describe the characteristics of an interactive system which suggests how the system should be used. The objective of this study is to explore the affordances of SNS. The study comprises an inventory of the affordances of three popular SNS. The study reveals a diverse collection of software features which afford user behaviour in six areas of activity: social connectivity, social interactivity, profile management, content discovery, content sharing and content aggregation. The findings of the study provide a rich foundation for future research on user appropriation of SNS, the future evolution of SNS, and the design of SNS systems