Rumor Detection on Social Media: Datasets, Methods and Opportunities

TL;DR

This paper reviews recent research on rumor detection on social media, covering datasets, methods, and future opportunities, highlighting the importance of analyzing content and social context with machine learning.

Contribution

It provides a comprehensive overview of datasets, methodologies, and new research directions in social media rumor detection, integrating recent studies and insights.

Findings

Compilation of key rumor detection datasets

Analysis of content and social context approaches

Identification of promising future research directions

Abstract

Social media platforms have been used for information and news gathering, and they are very valuable in many applications. However, they also lead to the spreading of rumors and fake news. Many efforts have been taken to detect and debunk rumors on social media by analyzing their content and social context using machine learning techniques. This paper gives an overview of the recent studies in the rumor detection field. It provides a comprehensive list of datasets used for rumor detection, and reviews the important studies based on what types of information they exploit and the approaches they take. And more importantly, we also present several new directions for future research.