  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.
