Abstract
Social media is a perfect breeding ground for false rumors due to the simplicity of sharing information, which may have negative implications in a variety of domains, including economics, healthcare, and politics. Previous research indicates that public reactions to false rumors are a critical indicator for determining the truthfulness of news. In social media, substantial effort has been invested in detecting and debunking rumors based on crowd stance, given that stance is a vital part of automatic news verification. This paper presents a review of recent approaches in the area of rumor verification using stance detection, which attempts to determine a given document’s stance with respect to a given piece of news. The review offers a detailed list of datasets, as well as a summary of relevant experiments and methods employed, as well as analysis of helpful features for addressing this issue. Finally, we highlight the main challenges and future directions in this field by utilizing stance detection.
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