1,724 research outputs found
Fake News Detection in Social Networks via Crowd Signals
Our work considers leveraging crowd signals for detecting fake news and is
motivated by tools recently introduced by Facebook that enable users to flag
fake news. By aggregating users' flags, our goal is to select a small subset of
news every day, send them to an expert (e.g., via a third-party fact-checking
organization), and stop the spread of news identified as fake by an expert. The
main objective of our work is to minimize the spread of misinformation by
stopping the propagation of fake news in the network. It is especially
challenging to achieve this objective as it requires detecting fake news with
high-confidence as quickly as possible. We show that in order to leverage
users' flags efficiently, it is crucial to learn about users' flagging
accuracy. We develop a novel algorithm, DETECTIVE, that performs Bayesian
inference for detecting fake news and jointly learns about users' flagging
accuracy over time. Our algorithm employs posterior sampling to actively trade
off exploitation (selecting news that maximize the objective value at a given
epoch) and exploration (selecting news that maximize the value of information
towards learning about users' flagging accuracy). We demonstrate the
effectiveness of our approach via extensive experiments and show the power of
leveraging community signals for fake news detection
Social Turing Tests: Crowdsourcing Sybil Detection
As popular tools for spreading spam and malware, Sybils (or fake accounts)
pose a serious threat to online communities such as Online Social Networks
(OSNs). Today, sophisticated attackers are creating realistic Sybils that
effectively befriend legitimate users, rendering most automated Sybil detection
techniques ineffective. In this paper, we explore the feasibility of a
crowdsourced Sybil detection system for OSNs. We conduct a large user study on
the ability of humans to detect today's Sybil accounts, using a large corpus of
ground-truth Sybil accounts from the Facebook and Renren networks. We analyze
detection accuracy by both "experts" and "turkers" under a variety of
conditions, and find that while turkers vary significantly in their
effectiveness, experts consistently produce near-optimal results. We use these
results to drive the design of a multi-tier crowdsourcing Sybil detection
system. Using our user study data, we show that this system is scalable, and
can be highly effective either as a standalone system or as a complementary
technique to current tools
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
Automatic fake news detection is a challenging problem in deception
detection, and it has tremendous real-world political and social impacts.
However, statistical approaches to combating fake news has been dramatically
limited by the lack of labeled benchmark datasets. In this paper, we present
liar: a new, publicly available dataset for fake news detection. We collected a
decade-long, 12.8K manually labeled short statements in various contexts from
PolitiFact.com, which provides detailed analysis report and links to source
documents for each case. This dataset can be used for fact-checking research as
well. Notably, this new dataset is an order of magnitude larger than previously
largest public fake news datasets of similar type. Empirically, we investigate
automatic fake news detection based on surface-level linguistic patterns. We
have designed a novel, hybrid convolutional neural network to integrate
meta-data with text. We show that this hybrid approach can improve a text-only
deep learning model.Comment: ACL 201
Understanding the Detection of View Fraud in Video Content Portals
While substantial effort has been devoted to understand fraudulent activity
in traditional online advertising (search and banner), more recent forms such
as video ads have received little attention. The understanding and
identification of fraudulent activity (i.e., fake views) in video ads for
advertisers, is complicated as they rely exclusively on the detection
mechanisms deployed by video hosting portals. In this context, the development
of independent tools able to monitor and audit the fidelity of these systems
are missing today and needed by both industry and regulators.
In this paper we present a first set of tools to serve this purpose. Using
our tools, we evaluate the performance of the audit systems of five major
online video portals. Our results reveal that YouTube's detection system
significantly outperforms all the others. Despite this, a systematic evaluation
indicates that it may still be susceptible to simple attacks. Furthermore, we
find that YouTube penalizes its videos' public and monetized view counters
differently, the former being more aggressive. This means that views identified
as fake and discounted from the public view counter are still monetized. We
speculate that even though YouTube's policy puts in lots of effort to
compensate users after an attack is discovered, this practice places the burden
of the risk on the advertisers, who pay to get their ads displayed.Comment: To appear in WWW 2016, Montr\'eal, Qu\'ebec, Canada. Please cite the
conference version of this pape
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