61,578 research outputs found
Location Prediction: Communities Speak Louder than Friends
Humans are social animals, they interact with different communities of
friends to conduct different activities. The literature shows that human
mobility is constrained by their social relations. In this paper, we
investigate the social impact of a person's communities on his mobility,
instead of all friends from his online social networks. This study can be
particularly useful, as certain social behaviors are influenced by specific
communities but not all friends. To achieve our goal, we first develop a
measure to characterize a person's social diversity, which we term `community
entropy'. Through analysis of two real-life datasets, we demonstrate that a
person's mobility is influenced only by a small fraction of his communities and
the influence depends on the social contexts of the communities. We then
exploit machine learning techniques to predict users' future movement based on
their communities' information. Extensive experiments demonstrate the
prediction's effectiveness.Comment: ACM Conference on Online Social Networks 2015, COSN 201
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
On Facebook, most ties are weak
Pervasive socio-technical networks bring new conceptual and technological
challenges to developers and users alike. A central research theme is
evaluation of the intensity of relations linking users and how they facilitate
communication and the spread of information. These aspects of human
relationships have been studied extensively in the social sciences under the
framework of the "strength of weak ties" theory proposed by Mark Granovetter.13
Some research has considered whether that theory can be extended to online
social networks like Facebook, suggesting interaction data can be used to
predict the strength of ties. The approaches being used require handling
user-generated data that is often not publicly available due to privacy
concerns. Here, we propose an alternative definition of weak and strong ties
that requires knowledge of only the topology of the social network (such as who
is a friend of whom on Facebook), relying on the fact that online social
networks, or OSNs, tend to fragment into communities. We thus suggest
classifying as weak ties those edges linking individuals belonging to different
communities and strong ties as those connecting users in the same community. We
tested this definition on a large network representing part of the Facebook
social graph and studied how weak and strong ties affect the
information-diffusion process. Our findings suggest individuals in OSNs
self-organize to create well-connected communities, while weak ties yield
cohesion and optimize the coverage of information spread.Comment: Accepted version of the manuscript before ACM editorial work. Check
http://cacm.acm.org/magazines/2014/11/179820-on-facebook-most-ties-are-weak/
for the final versio
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