Detecting fake news stories via multimodal analysis
Detecting fake news stories via multimodal analysis
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DOI：
10.1002/asi.24359
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发表时间：
2020-05-04
影响因子：
3.5
通讯作者：
Sonagara, Darshan
中科院分区：
文献类型：
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作者：
Singh, Vivek K.;Ghosh, Isha;Sonagara, Darshan
过滤、审查和验证数字信息是信息科学的核心兴趣领域。网络假新闻是一种特定类型的数字错误信息，对民主机构构成严重威胁，误导公众，并可能导致激进化和暴力。因此，假新闻的识别是情报学研究的一个重要问题。虽然人们曾多次尝试识别假新闻，但大多数这类努力都集中在一种模式(例如，仅基于文本或仅基于视觉特征)。然而，新闻文章越来越多地被框架为多模新闻故事，因此，在本工作中，我们提出了一种结合文本和视觉分析的在线新闻故事的多模方法来自动检测假新闻。根据信息处理和呈现的关键理论，我们识别出与虚假或可信的新闻文章相关的多个文本和视觉特征。然后，我们执行预测性分析，以检测与假新闻关联最强的特征。接下来，我们使用多种机器学习技术将这些功能组合到预测模型中。实验结果表明，多通道方法比单通道方法具有更好的假新闻检测性能。
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.