Rumor Stance Classification in Online Social Networks: The State-of-the-Art, Prospects, and Future Challenges

TL;DR

This paper reviews the state-of-the-art in rumor stance classification on social media, discussing approaches, datasets, challenges, and future directions to improve rumor verification systems.

Contribution

It provides a comprehensive survey of rumor stance classification methods, compares their performance, and highlights datasets and future research challenges.

Findings

Summarizes various rumor stance classification approaches

Analyzes performance of existing methods

Identifies limitations of current datasets

Abstract

The emergence of the Internet as a ubiquitous technology has facilitated the rapid evolution of social media as the leading virtual platform for communication, content sharing, and information dissemination. In spite of revolutionizing the way news is delivered to people, this technology has also brought along with itself inevitable demerits. One such drawback is the spread of rumors expedited by social media platforms, which may provoke doubt and fear. Therefore, it is essential to debunk rumors before their widespread use. Over the years, many studies have been conducted to develop effective rumor verification systems. One aspect of such studies focuses on rumor stance classification, which involves the task of utilizing user viewpoints regarding a rumorous post to better predict the veracity of a rumor. Relying on user stances in rumor verification has gained significant importance, for it has resulted in significant improvements in the model performance. In this paper, we conduct a comprehensive literature review of rumor stance classification in complex online social networks (OSNs). In particular, we present a thorough description of these approaches and compare their performances. Moreover, we introduce multiple datasets available for this purpose and highlight their limitations. Finally, challenges and future directions are discussed to stimulate further relevant research efforts.