6,758 research outputs found
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
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
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
Extraction and classification of dense communities in the Web
The World Wide Web (WWW) is rapidly becoming important for society as a medium for sharing data, information and services, and there is a growing interest in tools for understanding collective behaviors and emerging phenomena in the WWW. In this paper we focus on the problem of searching and classifying communities in the web. Loosely speaking a community is a group of pages related to a common interest. More formally communities have been associated in the computer science literature with the existence of a locally dense sub-graph of the web-graph (where web pages are nodes and hyper-links are arcs of the web-graph) The core of our contribution is a new scalable algorithm for finding relatively dense subgraphs in massive graphs. We apply our algorithm on web-graphs built on three publicly available large crawls of the web (with raw sizes up to 120M nodes and 1G arcs). The effectiveness of our algorithm in finding dense subgraphs is demonstrated experimentally by embedding artificial communities in the web-graph and counting how many of these are blindly found. Effectiveness increases with the size and density of the communities: it is close to 100% for dense communities of a hundred nodes or more. Moreover it is still about 80% even for small communities of twenty nodes and density at 50% of the arcs present. We complete our Community Watch system by clustering the communities found in the web-graph into homogeneous groups by topic and labelling each group by representative keywords
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