Combating Fake News: A Survey on Identification and Mitigation Techniques

Abstract

The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news, and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news based on its contents or by exploiting users' engagements with the news on social media, there has been a rising interest in proactive intervention strategies to counter the spread of misinformation and its impact on society. In this survey, we describe the modern-day problem of fake news and, in particular, highlight the technical challenges associated with it. We discuss existing methods and techniques applicable to both identification and mitigation, with a focus on the significant advances in each method and their advantages and limitations. In addition, research has often been limited by the quality of existing datasets and their specific application contexts. To alleviate this problem, we comprehensively compile and summarize characteristic features of available datasets. Furthermore, we outline new directions of research to facilitate future development of effective and interdisciplinary solutions.

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Cited by in corpus (4)

• Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study
• Open Issues in Combating Fake News: Interpretability as an Opportunity
• Emotional Framing in the Spreading of False and True Claims
• Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims