False News On Social Media: A Data-Driven Survey

TL;DR

This survey reviews recent data-driven methods for detecting, characterizing, and mitigating false news on social media, highlighting challenges and promising future directions.

Contribution

It provides a comprehensive classification of features and datasets used in false news detection studies, and discusses emerging promising approaches.

Findings

Identification of key features used in false news detection

Analysis of datasets employed in recent studies

Discussion of promising future research directions

Abstract

In the past few years, the research community has dedicated growing interest to the issue of false news circulating on social networks. The widespread attention on detecting and characterizing false news has been motivated by considerable backlashes of this threat against the real world. As a matter of fact, social media platforms exhibit peculiar characteristics, with respect to traditional news outlets, which have been particularly favorable to the proliferation of deceptive information. They also present unique challenges for all kind of potential interventions on the subject. As this issue becomes of global concern, it is also gaining more attention in academia. The aim of this survey is to offer a comprehensive study on the recent advances in terms of detection, characterization and mitigation of false news that propagate on social media, as well as the challenges and the open questions that await future research on the field. We use a data-driven approach, focusing on a classification of the features that are used in each study to characterize false information and on the datasets used for instructing classification methods. At the end of the survey, we highlight emerging approaches that look most promising for addressing false news.