On the Role of Images for Analyzing Claims in Social Media
This addresses the challenge of fake news detection for social media users and platforms, but it is incremental as it builds on existing multimodal models to investigate an understudied aspect.
The paper tackled the problem of understanding the role of images in detecting claims and conspiracies related to fake news on social media, by empirically evaluating visual, textual, and multimodal models across four datasets in two languages, finding that images are influential but their specific role is not well understood.
Fake news is a severe problem in social media. In this paper, we present an empirical study on visual, textual, and multimodal models for the tasks of claim, claim check-worthiness, and conspiracy detection, all of which are related to fake news detection. Recent work suggests that images are more influential than text and often appear alongside fake text. To this end, several multimodal models have been proposed in recent years that use images along with text to detect fake news on social media sites like Twitter. However, the role of images is not well understood for claim detection, specifically using transformer-based textual and multimodal models. We investigate state-of-the-art models for images, text (Transformer-based), and multimodal information for four different datasets across two languages to understand the role of images in the task of claim and conspiracy detection.