The Future of False Information Detection on Social Media

Abstract

The massive spread of false information on social media has become a global risk, implicitly influencing public opinion and threatening social/political development. False information detection (FID) has thus become a surging research topic in recent years. As a promising and rapidly developing research field, we find that much effort has been paid to new research problems and approaches of FID. Therefore, it is necessary to give a comprehensive review of the new research trends of FID. We first give a brief review of the literature history of FID, 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 FID, which paves a promising way to tackle FID challenges. Finally, we give our views on the open issues and future research directions of FID, such as model adaptivity/generality to new events, embracing of novel machine learning models, aggregation of crowd wisdom, adversarial attack and defense in detection models, and so on.

CoAID-DEEP: An Optimized Intelligent Framework for Automated Detecting COVID-19 Misleading Information on Twitter

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