Abstract
The explosive growth of fake news and its erosion of democracy, justice, and public trust has significantly increased the demand for accurate fake news detection. Recent advancements in this area have proposed novel techniques that aim to detect fake news by exploring how it propagates on social networks. However, to achieve fake news early detection, one is only provided with limited to no information on news propagation; hence, motivating the need to develop approaches that can detect fake news by focusing mainly on news content. In this paper, a theory-driven model is proposed for fake news detection. The method investigates news content at various levels: lexicon-level, syntax-level, semantic-level and discourse-level. We represent news at each level, relying on well-established theories in social and forensic psychology. Fake news detection is then conducted within a supervised machine learning framework. As an interdisciplinary research, our work explores potential fake news patterns, enhances the interpretability in fake news feature engineering, and studies the relationships among fake news, deception/disinformation, and clickbaits. Experiments conducted on two real-world datasets indicate that the proposed method can outperform the state-of-the-art and enable fake news early detection, even when there is limited content information.
Abstract (translated)
假新闻的爆炸性增长及其对民主、正义和公众信任的侵蚀，极大地增加了对准确检测假新闻的需求。这一领域的最新进展提出了新的技术，旨在通过探索假新闻如何在社交网络上传播来检测假新闻。然而，为了实现假新闻的早期发现，人们只提供有限的新闻传播信息，因此，需要开发出能够通过主要关注新闻内容来发现假新闻的方法。本文提出了一种基于理论的假新闻检测模型。该方法从词汇层面、句法层面、语义层面和语篇层面对新闻内容进行研究。我们在每一个层面上代表新闻，依赖于社会和法医心理学的成熟理论。然后在受监控的机器学习框架内进行假新闻检测。作为一个跨学科的研究，我们的工作探索了潜在的假新闻模式，提高了假新闻特征工程的可解释性，研究了假新闻、欺骗/虚假信息和点击诱饵之间的关系。在两个真实数据集上进行的实验表明，即使在内容信息有限的情况下，该方法仍优于现有技术，能够实现假新闻的早期检测。
URL
https://arxiv.org/abs/1904.11679
