224 research outputs found
Seminar Users in the Arabic Twitter Sphere
We introduce the notion of "seminar users", who are social media users
engaged in propaganda in support of a political entity. We develop a framework
that can identify such users with 84.4% precision and 76.1% recall. While our
dataset is from the Arab region, omitting language-specific features has only a
minor impact on classification performance, and thus, our approach could work
for detecting seminar users in other parts of the world and in other languages.
We further explored a controversial political topic to observe the prevalence
and potential potency of such users. In our case study, we found that 25% of
the users engaged in the topic are in fact seminar users and their tweets make
nearly a third of the on-topic tweets. Moreover, they are often successful in
affecting mainstream discourse with coordinated hashtag campaigns.Comment: to appear in SocInfo 201
Organized Behavior Classification of Tweet Sets using Supervised Learning Methods
During the 2016 US elections Twitter experienced unprecedented levels of
propaganda and fake news through the collaboration of bots and hired persons,
the ramifications of which are still being debated. This work proposes an
approach to identify the presence of organized behavior in tweets. The Random
Forest, Support Vector Machine, and Logistic Regression algorithms are each
used to train a model with a data set of 850 records consisting of 299 features
extracted from tweets gathered during the 2016 US presidential election. The
features represent user and temporal synchronization characteristics to capture
coordinated behavior. These models are trained to classify tweet sets among the
categories: organic vs organized, political vs non-political, and pro-Trump vs
pro-Hillary vs neither. The random forest algorithm performs better with
greater than 95% average accuracy and f-measure scores for each category. The
most valuable features for classification are identified as user based
features, with media use and marking tweets as favorite to be the most
dominant.Comment: 51 pages, 5 figure
Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media
Social media is often viewed as a sensor into various societal events such as
disease outbreaks, protests, and elections. We describe the use of social media
as a crowdsourced sensor to gain insight into ongoing cyber-attacks. Our
approach detects a broad range of cyber-attacks (e.g., distributed denial of
service (DDOS) attacks, data breaches, and account hijacking) in an
unsupervised manner using just a limited fixed set of seed event triggers. A
new query expansion strategy based on convolutional kernels and dependency
parses helps model reporting structure and aids in identifying key event
characteristics. Through a large-scale analysis over Twitter, we demonstrate
that our approach consistently identifies and encodes events, outperforming
existing methods.Comment: 13 single column pages, 5 figures, submitted to KDD 201
Latent Sentiment Detection in Online Social Networks: A Communications-oriented View
In this paper, we consider the problem of latent sentiment detection in
Online Social Networks such as Twitter. We demonstrate the benefits of using
the underlying social network as an Ising prior to perform network aided
sentiment detection. We show that the use of the underlying network results in
substantially lower detection error rates compared to strictly features-based
detection. In doing so, we introduce a novel communications-oriented framework
for characterizing the probability of error, based on information-theoretic
analysis. We study the variation of the calculated error exponent for several
stylized network topologies such as the complete network, the star network and
the closed-chain network, and show the importance of the network structure in
determining detection performance.Comment: 13 pages, 6 figures, Submitted to ICC 201
Quantifying echo chamber effects in information spreading over political communication networks
Echo chambers in online social networks, in which users prefer to interact
only with ideologically-aligned peers, are believed to facilitate
misinformation spreading and contribute to radicalize political discourse. In
this paper, we gauge the effects of echo chambers in information spreading
phenomena over political communication networks. Mining 12 million Twitter
messages, we reconstruct a network in which users interchange opinions related
to the impeachment of the former Brazilian President Dilma Rousseff. We define
a continuous {political position} parameter, independent of the network's
structure, that allows to quantify the presence of echo chambers in the
strongly connected component of the network, reflected in two well-separated
communities of similar sizes with opposite views of the impeachment process. By
means of simple spreading models, we show that the capability of users in
propagating the content they produce, measured by the associated spreadability,
strongly depends on their attitude. Users expressing pro-impeachment sentiments
are capable to transmit information, on average, to a larger audience than
users expressing anti-impeachment sentiments. Furthermore, the users'
spreadability is correlated to the diversity, in terms of political position,
of the audience reached. Our method can be exploited to identify the presence
of echo chambers and their effects across different contexts and shed light
upon the mechanisms allowing to break echo chambers.Comment: 9 pages, 4 figures. Supplementary Information available as ancillary
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