14,247 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
Bloggers Behavior and Emergent Communities in Blog Space
Interactions between users in cyberspace may lead to phenomena different from
those observed in common social networks. Here we analyse large data sets about
users and Blogs which they write and comment, mapped onto a bipartite graph. In
such enlarged Blog space we trace user activity over time, which results in
robust temporal patterns of user--Blog behavior and the emergence of
communities. With the spectral methods applied to the projection on weighted
user network we detect clusters of users related to their common interests and
habits. Our results suggest that different mechanisms may play the role in the
case of very popular Blogs. Our analysis makes a suitable basis for theoretical
modeling of the evolution of cyber communities and for practical study of the
data, in particular for an efficient search of interesting Blog clusters and
further retrieval of their contents by text analysis
Community structure in directed networks
We consider the problem of finding communities or modules in directed
networks. The most common approach to this problem in the previous literature
has been simply to ignore edge direction and apply methods developed for
community discovery in undirected networks, but this approach discards
potentially useful information contained in the edge directions. Here we show
how the widely used benefit function known as modularity can be generalized in
a principled fashion to incorporate the information contained in edge
directions. This in turn allows us to find communities by maximizing the
modularity over possible divisions of a network, which we do using an algorithm
based on the eigenvectors of the corresponding modularity matrix. This method
is shown to give demonstrably better results than previous methods on a variety
of test networks, both real and computer-generated.Comment: 5 pages, 3 figure
Modularity and community structure in networks
Many networks of interest in the sciences, including a variety of social and
biological networks, are found to divide naturally into communities or modules.
The problem of detecting and characterizing this community structure has
attracted considerable recent attention. One of the most sensitive detection
methods is optimization of the quality function known as "modularity" over the
possible divisions of a network, but direct application of this method using,
for instance, simulated annealing is computationally costly. Here we show that
the modularity can be reformulated in terms of the eigenvectors of a new
characteristic matrix for the network, which we call the modularity matrix, and
that this reformulation leads to a spectral algorithm for community detection
that returns results of better quality than competing methods in noticeably
shorter running times. We demonstrate the algorithm with applications to
several network data sets.Comment: 7 pages, 3 figure
Detecting Communities in Networks by Merging Cliques
Many algorithms have been proposed for detecting disjoint communities
(relatively densely connected subgraphs) in networks. One popular technique is
to optimize modularity, a measure of the quality of a partition in terms of the
number of intracommunity and intercommunity edges. Greedy approximate
algorithms for maximizing modularity can be very fast and effective. We propose
a new algorithm that starts by detecting disjoint cliques and then merges these
to optimize modularity. We show that this performs better than other similar
algorithms in terms of both modularity and execution speed.Comment: 5 pages, 7 figure
BlogForever D2.4: Weblog spider prototype and associated methodology
The purpose of this document is to present the evaluation of different solutions for capturing blogs, established methodology and to describe the developed blog spider prototype
Impact of local information in growing networks
We present a new model of the evolutionary dynamics and the growth of on-line
social networks. The model emulates people's strategies for acquiring
information in social networks, emphasising the local subjective view of an
individual and what kind of information the individual can acquire when
arriving in a new social context. The model proceeds through two phases: (a) a
discovery phase, in which the individual becomes aware of the surrounding world
and (b) an elaboration phase, in which the individual elaborates locally the
information trough a cognitive-inspired algorithm. Model generated networks
reproduce main features of both theoretical and real-world networks, such as
high clustering coefficient, low characteristic path length, strong division in
communities, and variability of degree distributions.Comment: In Proceedings Wivace 2013, arXiv:1309.712
Evolution of Ego-networks in Social Media with Link Recommendations
Ego-networks are fundamental structures in social graphs, yet the process of
their evolution is still widely unexplored. In an online context, a key
question is how link recommender systems may skew the growth of these networks,
possibly restraining diversity. To shed light on this matter, we analyze the
complete temporal evolution of 170M ego-networks extracted from Flickr and
Tumblr, comparing links that are created spontaneously with those that have
been algorithmically recommended. We find that the evolution of ego-networks is
bursty, community-driven, and characterized by subsequent phases of explosive
diameter increase, slight shrinking, and stabilization. Recommendations favor
popular and well-connected nodes, limiting the diameter expansion. With a
matching experiment aimed at detecting causal relationships from observational
data, we find that the bias introduced by the recommendations fosters global
diversity in the process of neighbor selection. Last, with two link prediction
experiments, we show how insights from our analysis can be used to improve the
effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search
and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl
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