Visual Persuasion in COVID-19 Social Media Content: A Multi-Modal Characterization
Visual Persuasion in COVID-19 Social Media Content: A Multi-Modal Characterization
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DOI：
10.1145/3487553.3524647
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Mesut Erhan Unal;Adriana Kovashka;Wen-Ting Chung;Yu-Ru Lin
中科院分区：
文献类型：
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作者：
Mesut Erhan Unal;Adriana Kovashka;Wen-Ting Chung;Yu-Ru Lin
社交媒体内容通常采用多模态设计来传递信息和塑造意义，并将解释转向理想的含义，但使用文本和视觉图像的选择和影响尚未得到充分研究。这项工作提出了一种计算方法，以分析在Twitter上分享的COVID-19相关新闻文章中，说服性多模态内容对受欢迎度和可靠性的影响。这两个方面在错误信息的传播中交织在一起：例如，一篇旨在提供错误信息的不可靠文章必须获得一定的知名度。这项工作有几个贡献。首先，我们提出了一个多模态（图像和文本）的方法，以有效地确定流行性和可靠性的信息源同时。其次，我们确定的文本和视觉元素，是预测信息的普及和可靠性。第三，通过建模跨模态关系和相似性，我们能够揭示不可靠的文章是如何以扭曲，偏见的方式构建多模态意义的。我们的工作展示了如何使用多模态分析来理解有影响力的内容，并对社交媒体素养和参与度产生影响。
Social media content routinely incorporates multi-modal design to covey information and shape meanings, and sway interpretations toward desirable implications, but the choices and impacts of using both texts and visual images have not been sufficiently studied. This work proposes a computational approach to analyze the impacts of persuasive multi-modal content on 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.