58,010 research outputs found
Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
Spambot detection in online social networks is a long-lasting challenge
involving the study and design of detection techniques capable of efficiently
identifying ever-evolving spammers. Recently, a new wave of social spambots has
emerged, with advanced human-like characteristics that allow them to go
undetected even by current state-of-the-art algorithms. In this paper, we show
that efficient spambots detection can be achieved via an in-depth analysis of
their collective behaviors exploiting the digital DNA technique for modeling
the behaviors of social network users. Inspired by its biological counterpart,
in the digital DNA representation the behavioral lifetime of a digital account
is encoded in a sequence of characters. Then, we define a similarity measure
for such digital DNA sequences. We build upon digital DNA and the similarity
between groups of users to characterize both genuine accounts and spambots.
Leveraging such characterization, we design the Social Fingerprinting
technique, which is able to discriminate among spambots and genuine accounts in
both a supervised and an unsupervised fashion. We finally evaluate the
effectiveness of Social Fingerprinting and we compare it with three
state-of-the-art detection algorithms. Among the peculiarities of our approach
is the possibility to apply off-the-shelf DNA analysis techniques to study
online users behaviors and to efficiently rely on a limited number of
lightweight account characteristics
How Misinformation Spreads Through Twitter
While living in the age of information, an inherent drawback to such high exposure to content lends itself to the precarious rise of misinformation. Whether it is called “alternative facts,” “fake news,” or just incorrect information, because of its pervasiveness in nearly every political and policy discussion, the spread of misinformation is seen as one of the greatest challenges to overcome in the 21st century. As new technologies emerge, a major piece of both content creation and the perpetuation of misinformation are social media platforms like Twitter, Facebook, and YouTube. As news events emerge, whether be a pandemic, a mass shooting, or an election campaign, it is difficult to divulge the facts from fiction when so many different “facts” appear. This study looks at 14,545,945 tweets generated in the wake of the 1 October mass shooting and its second anniversary to identify how much of the public response is fogged by information pollution, to identify what kind of misinformation is spread and how it spreads on Twitter and news coverage
The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race
Recent studies in social media spam and automation provide anecdotal
argumentation of the rise of a new generation of spambots, so-called social
spambots. Here, for the first time, we extensively study this novel phenomenon
on Twitter and we provide quantitative evidence that a paradigm-shift exists in
spambot design. First, we measure current Twitter's capabilities of detecting
the new social spambots. Later, we assess the human performance in
discriminating between genuine accounts, social spambots, and traditional
spambots. Then, we benchmark several state-of-the-art techniques proposed by
the academic literature. Results show that neither Twitter, nor humans, nor
cutting-edge applications are currently capable of accurately detecting the new
social spambots. Our results call for new approaches capable of turning the
tide in the fight against this raising phenomenon. We conclude by reviewing the
latest literature on spambots detection and we highlight an emerging common
research trend based on the analysis of collective behaviors. Insights derived
from both our extensive experimental campaign and survey shed light on the most
promising directions of research and lay the foundations for the arms race
against the novel social spambots. Finally, to foster research on this novel
phenomenon, we make publicly available to the scientific community all the
datasets used in this study.Comment: To appear in Proc. 26th WWW, 2017, Companion Volume (Web Science
Track, Perth, Australia, 3-7 April, 2017
Are you going to the party: depends, who else is coming? [Learning hidden group dynamics via conditional latent tree models]
Scalable probabilistic modeling and prediction in high dimensional
multivariate time-series is a challenging problem, particularly for systems
with hidden sources of dependence and/or homogeneity. Examples of such problems
include dynamic social networks with co-evolving nodes and edges and dynamic
student learning in online courses. Here, we address these problems through the
discovery of hierarchical latent groups. We introduce a family of Conditional
Latent Tree Models (CLTM), in which tree-structured latent variables
incorporate the unknown groups. The latent tree itself is conditioned on
observed covariates such as seasonality, historical activity, and node
attributes. We propose a statistically efficient framework for learning both
the hierarchical tree structure and the parameters of the CLTM. We demonstrate
competitive performance in multiple real world datasets from different domains.
These include a dataset on students' attempts at answering questions in a
psychology MOOC, Twitter users participating in an emergency management
discussion and interacting with one another, and windsurfers interacting on a
beach in Southern California. In addition, our modeling framework provides
valuable and interpretable information about the hidden group structures and
their effect on the evolution of the time series
Partisan Asymmetries in Online Political Activity
We examine partisan differences in the behavior, communication patterns and
social interactions of more than 18,000 politically-active Twitter users to
produce evidence that points to changing levels of partisan engagement with the
American online political landscape. Analysis of a network defined by the
communication activity of these users in proximity to the 2010 midterm
congressional elections reveals a highly segregated, well clustered partisan
community structure. Using cluster membership as a high-fidelity (87% accuracy)
proxy for political affiliation, we characterize a wide range of differences in
the behavior, communication and social connectivity of left- and right-leaning
Twitter users. We find that in contrast to the online political dynamics of the
2008 campaign, right-leaning Twitter users exhibit greater levels of political
activity, a more tightly interconnected social structure, and a communication
network topology that facilitates the rapid and broad dissemination of
political information.Comment: 17 pages, 10 figures, 6 table
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