21,222 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
How Do Tor Users Interact With Onion Services?
Onion services are anonymous network services that are exposed over the Tor
network. In contrast to conventional Internet services, onion services are
private, generally not indexed by search engines, and use self-certifying
domain names that are long and difficult for humans to read. In this paper, we
study how people perceive, understand, and use onion services based on data
from 17 semi-structured interviews and an online survey of 517 users. We find
that users have an incomplete mental model of onion services, use these
services for anonymity and have varying trust in onion services in general.
Users also have difficulty discovering and tracking onion sites and
authenticating them. Finally, users want technical improvements to onion
services and better information on how to use them. Our findings suggest
various improvements for the security and usability of Tor onion services,
including ways to automatically detect phishing of onion services, more clear
security indicators, and ways to manage onion domain names that are difficult
to remember.Comment: Appeared in USENIX Security Symposium 201
Resource Letter: Gravitational Lensing
This Resource Letter provides a guide to a selection of the literature on
gravitational lensing and its applications. Journal articles, books, popular
articles, and websites are cited for the following topics: foundations of
gravitational lensing, foundations of cosmology, history of gravitational
lensing, strong lensing, weak lensing, and microlensing.Comment: Resource Letter, 2012, in press
(http://ajp.dickinson.edu/Readers/resLetters.html); 21 pages, no figures;
diigo version available at
http://groups.diigo.com/group/gravitational-lensin
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We are the Change that we Seek: Information Interactions During a Change of Viewpoint
There has been considerable hype about filter bubbles and echo chambers influencing the views of information consumers. The fear is that these technologies are undermining democracy by swaying opinion and creating an uninformed, polarised populace. The literature in this space is mostly techno-centric, addressing the impact of technology. In contrast, our work is the first research in the information interaction field to examine changing viewpoints from a human-centric perspective. It provides a new understanding of view change and how we might support informed, autonomous view change behaviour. We interviewed 18 participants about a self-identified change of view, and the information touchpoints they engaged with along the way. In this paper we present the information types and sources that informed changes of viewpoint, and the ways in which our participants interacted with that information. We describe our findings in the context of the techno-centric literature and suggest principles for designing digital information environments that support user autonomy and reflection in viewpoint formation
Topic Modelling of Everyday Sexism Project Entries
The Everyday Sexism Project documents everyday examples of sexism reported by
volunteer contributors from all around the world. It collected 100,000 entries
in 13+ languages within the first 3 years of its existence. The content of
reports in various languages submitted to Everyday Sexism is a valuable source
of crowdsourced information with great potential for feminist and gender
studies. In this paper, we take a computational approach to analyze the content
of reports. We use topic-modelling techniques to extract emerging topics and
concepts from the reports, and to map the semantic relations between those
topics. The resulting picture closely resembles and adds to that arrived at
through qualitative analysis, showing that this form of topic modeling could be
useful for sifting through datasets that had not previously been subject to any
analysis. More precisely, we come up with a map of topics for two different
resolutions of our topic model and discuss the connection between the
identified topics. In the low resolution picture, for instance, we found Public
space/Street, Online, Work related/Office, Transport, School, Media harassment,
and Domestic abuse. Among these, the strongest connection is between Public
space/Street harassment and Domestic abuse and sexism in personal
relationships.The strength of the relationships between topics illustrates the
fluid and ubiquitous nature of sexism, with no single experience being
unrelated to another.Comment: preprint, under revie
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