The Future of Misinformation Detection: New Perspectives and Trends

TL;DR

This paper reviews recent trends and challenges in misinformation detection, emphasizing new research directions like multimodal data fusion, early detection, and crowd intelligence to improve detection accuracy and explainability.

Contribution

It provides a comprehensive overview of emerging research problems, techniques, and future directions in misinformation detection, highlighting novel approaches and open issues.

Findings

Identification of new research challenges in MID

Discussion of multimodal data fusion techniques

Insights into crowd intelligence applications in MID

Abstract

The massive spread of misinformation in social networks has become a global risk, implicitly influencing public opinion and threatening social/political development. Misinformation detection (MID) has thus become a surging research topic in recent years. As a promising and rapid developing research field, we find that many efforts have been paid to new research problems and approaches of MID. Therefore, it is necessary to give a comprehensive review of the new research trends of MID. We first give a brief review of the literature history of MID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion, and explanatory detection. We further investigate the extraction and usage of various crowd intelligence in MID, which paves a promising way to tackle MID challenges. Finally, we give our own views on the open issues and future research directions of MID, such as model adaptivity/generality to new events, embracing of novel machine learning models, explanatory detection models, and so on.