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Social media content routinely incorporates multi-modal design to coveyinformation and shape meanings, and sway interpretations toward desirableimplications, but the choices and outcomes of using both texts and visualimages have not been sufficiently studied. This work proposes a computationalapproach to analyze the outcome of persuasive information in multi-modalcontent, focusing on two aspects, popularity and reliability, inCOVID-19-related news articles shared on Twitter. The two aspects areintertwined in the spread of misinformation: for example, an unreliable articlethat aims to misinform has to attain some popularity. This work has severalcontributions. First, we propose a multi-modal (image and text) approach toeffectively identify popularity and reliability of information sourcessimultaneously. Second, we identify textual and visual elements that arepredictive to information popularity and reliability. Third, by modelingcross-modal relations and similarity, we are able to uncover how unreliablearticles construct multi-modal meaning in a distorted, biased fashion. Our workdemonstrates how to use multi-modal analysis for understanding influentialcontent and has implications to social media literacy and engagement.

Visual Persuasion in COVID-19 Social Media Content: A Multi-Modal Characterization