Filtering, vetting, and verifying digital information is an area of core interest in information science. Online fake news is a specific type of digital misinformation that poses serious threats to democratic institutions, misguides the public, and can lead to radicalization and violence. Hence, fake news detection is an important problem for information science research. While there have been multiple attempts to identify fake news, most of such efforts have focused on a single modality (e.g., only text‐based or only visual features). However, news articles are increasingly framed as multimodal news stories, and hence, in this work, we propose a multimodal approach combining text and visual analysis of online news stories to automatically detect fake news. Drawing on key theories of information processing and presentation, we identify multiple text and visual features that are associated with fake or credible news articles. We then perform a predictive analysis to detect features most strongly associated with fake news. Next, we combine these features in predictive models using multiple machine‐learning techniques. The experimental results indicate that a multimodal approach outperforms single‐modality approaches, allowing for better fake news detection.

中文翻译：

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通过多模态分析检测虚假新闻报道

过滤、审查和验证数字信息是信息科学的核心兴趣领域。在线假新闻是一种特定类型的数字错误信息，对民主制度构成严重威胁，误导公众，并可能导致激进化和暴力。因此，假新闻检测是信息科学研究的一个重要问题。虽然已经多次尝试识别假新闻，但大多数此类努力都集中在单一模式上（例如，仅基于文本或仅视觉特征）。然而，新闻文章越来越多地被框定为多模态新闻报道，因此，在这项工作中，我们提出了一种多模态方法，结合在线新闻报道的文本和视觉分析来自动检测假新闻。借鉴信息处理和呈现的关键理论，我们识别出与虚假或可信新闻文章相关的多个文本和视觉特征。然后，我们执行预测分析以检测与假新闻最密切相关的特征。接下来，我们使用多种机器学习技术将这些特征组合到预测模型中。实验结果表明，多模态方法优于单模态方法，可以更好地检测假新闻。