3,960 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
A New Framework for Network Disruption
Traditional network disruption approaches focus on disconnecting or
lengthening paths in the network. We present a new framework for network
disruption that attempts to reroute flow through critical vertices via vertex
deletion, under the assumption that this will render those vertices vulnerable
to future attacks. We define the load on a critical vertex to be the number of
paths in the network that must flow through the vertex. We present
graph-theoretic and computational techniques to maximize this load, firstly by
removing either a single vertex from the network, secondly by removing a subset
of vertices.Comment: Submitted for peer review on September 13, 201
Organizational Chart Inference
Nowadays, to facilitate the communication and cooperation among employees, a
new family of online social networks has been adopted in many companies, which
are called the "enterprise social networks" (ESNs). ESNs can provide employees
with various professional services to help them deal with daily work issues.
Meanwhile, employees in companies are usually organized into different
hierarchies according to the relative ranks of their positions. The company
internal management structure can be outlined with the organizational chart
visually, which is normally confidential to the public out of the privacy and
security concerns. In this paper, we want to study the IOC (Inference of
Organizational Chart) problem to identify company internal organizational chart
based on the heterogeneous online ESN launched in it. IOC is very challenging
to address as, to guarantee smooth operations, the internal organizational
charts of companies need to meet certain structural requirements (about its
depth and width). To solve the IOC problem, a novel unsupervised method Create
(ChArT REcovEr) is proposed in this paper, which consists of 3 steps: (1)
social stratification of ESN users into different social classes, (2)
supervision link inference from managers to subordinates, and (3) consecutive
social classes matching to prune the redundant supervision links. Extensive
experiments conducted on real-world online ESN dataset demonstrate that Create
can perform very well in addressing the IOC problem.Comment: 10 pages, 9 figures, 1 table. The paper is accepted by KDD 201
- …