73,674 research outputs found
Automated Crowdturfing Attacks and Defenses in Online Review Systems
Malicious crowdsourcing forums are gaining traction as sources of spreading
misinformation online, but are limited by the costs of hiring and managing
human workers. In this paper, we identify a new class of attacks that leverage
deep learning language models (Recurrent Neural Networks or RNNs) to automate
the generation of fake online reviews for products and services. Not only are
these attacks cheap and therefore more scalable, but they can control rate of
content output to eliminate the signature burstiness that makes crowdsourced
campaigns easy to detect.
Using Yelp reviews as an example platform, we show how a two phased review
generation and customization attack can produce reviews that are
indistinguishable by state-of-the-art statistical detectors. We conduct a
survey-based user study to show these reviews not only evade human detection,
but also score high on "usefulness" metrics by users. Finally, we develop novel
automated defenses against these attacks, by leveraging the lossy
transformation introduced by the RNN training and generation cycle. We consider
countermeasures against our mechanisms, show that they produce unattractive
cost-benefit tradeoffs for attackers, and that they can be further curtailed by
simple constraints imposed by online service providers
Hoaxy: A Platform for Tracking Online Misinformation
Massive amounts of misinformation have been observed to spread in
uncontrolled fashion across social media. Examples include rumors, hoaxes, fake
news, and conspiracy theories. At the same time, several journalistic
organizations devote significant efforts to high-quality fact checking of
online claims. The resulting information cascades contain instances of both
accurate and inaccurate information, unfold over multiple time scales, and
often reach audiences of considerable size. All these factors pose challenges
for the study of the social dynamics of online news sharing. Here we introduce
Hoaxy, a platform for the collection, detection, and analysis of online
misinformation and its related fact-checking efforts. We discuss the design of
the platform and present a preliminary analysis of a sample of public tweets
containing both fake news and fact checking. We find that, in the aggregate,
the sharing of fact-checking content typically lags that of misinformation by
10--20 hours. Moreover, fake news are dominated by very active users, while
fact checking is a more grass-roots activity. With the increasing risks
connected to massive online misinformation, social news observatories have the
potential to help researchers, journalists, and the general public understand
the dynamics of real and fake news sharing.Comment: 6 pages, 6 figures, submitted to Third Workshop on Social News On the
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