Visual Persuasion in COVID-19 Social Media Content: A Multi-Modal Characterization
This work addresses the spread of misinformation on social media, with implications for social media literacy and engagement, but it is incremental as it applies existing multi-modal methods to a specific domain.
The authors tackled the problem of analyzing persuasive multi-modal content in COVID-19 news on Twitter by developing a computational approach to identify popularity and reliability, finding predictive textual and visual elements and uncovering how unreliable articles distort multi-modal meaning.
Social media content routinely incorporates multi-modal design to covey information and shape meanings, and sway interpretations toward desirable implications, but the choices and outcomes of using both texts and visual images have not been sufficiently studied. This work proposes a computational approach to analyze the outcome of persuasive information in multi-modal content, focusing on two aspects, popularity and reliability, in COVID-19-related news articles shared on Twitter. The two aspects are intertwined in the spread of misinformation: for example, an unreliable article that aims to misinform has to attain some popularity. This work has several contributions. First, we propose a multi-modal (image and text) approach to effectively identify popularity and reliability of information sources simultaneously. Second, we identify textual and visual elements that are predictive to information popularity and reliability. Third, by modeling cross-modal relations and similarity, we are able to uncover how unreliable articles construct multi-modal meaning in a distorted, biased fashion. Our work demonstrates how to use multi-modal analysis for understanding influential content and has implications to social media literacy and engagement.