Combating Fake News: A Survey on Identification and Mitigation Techniques

TL;DR

This survey reviews current techniques for identifying and mitigating fake news, discusses technical challenges, dataset limitations, and suggests future research directions for more effective solutions.

Contribution

It provides a comprehensive overview of existing methods, highlights advances and limitations, and compiles datasets to guide future interdisciplinary research.

Findings

Significant advances in fake news detection and mitigation techniques.

Limitations due to dataset quality and application context.

Proposed future research directions for improved solutions.

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.