Some Like it Hoax: Automated Fake News Detection in Social Networks

Abstract

In recent years, the reliability of information on the Internet has emerged as a crucial issue of modern society. Social network sites (SNSs) have revolutionized the way in which information is spread by allowing users to freely share content. As a consequence, SNSs are also increasingly used as vectors for the diffusion of misinformation and hoaxes. The amount of disseminated information and the rapidity of its diffusion make it practically impossible to assess reliability in a timely manner, highlighting the need for automatic hoax detection systems.As a contribution towards this objective, we show that Facebook posts can be classified with high accuracy as hoaxes or non-hoaxes on the basis of the users who "liked" them. We present two classification techniques, one based on logistic regression, the other on a novel adaptation of boolean crowdsourcing algorithms. On a dataset consisting of 15,500 Facebook posts and 909,236 users, we obtain classification accuracies exceeding 99% even when the training set contains less than 1% of the posts. We further show that our techniques are robust: they work even when we restrict our attention to the users who like both hoax and non-hoax posts. These results suggest that mapping the diffusion pattern of information can be a useful component of automatic hoax detection systems.

Knowledge Enhanced Multi-modal Fake News Detection

“…In another example, Tacchini et al [39] propose to detect fake news based on users who liked them on Facebook. They tested logistic regression-based and harmonic Boolean label crowdsourcingbased methods, and their results suggest that both methods can achieve high accuracy.…”

FTR-18: Collecting rumours on football transfer news

“…Once a rumour is identified, we build a crawler using the Twitter's streaming API 7 to collect posts related to the rumour. In compliance to the established language restrictions, we only harvest tweets written in the languages of the FTR-18 dataset.…”

“…Social networks, blogs/micro-blogs and other untrusted online news sources have gained popularity, thus allowing any user to produce unverified content. This modern kind of media enables real-time proliferation of news stories, and, as consequence, increases the diffusion of rumours, hoaxes and misinformation to a global audience [7]. As news content is continuously published online, the speed in which it is disseminated hinders human fact-checking activity.…”

Multimodal Emergent Fake News Detection via Meta Neural Process Networks

“…Task Challenges. Thus far, various fake news detection methods, including both traditional learning [5,32] and deep learning based models [21-23, 26, 28, 35] have been exploited to identify fake news. Despite the success of deep learning models with large amounts of labeled datasets, the algorithms still suffer in the cases where fake news detection is needed on emergent events.…”