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Automatic Detection of Fake News
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The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analysis on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors. In addition, we provide comparative analyses of the automatic and manual identification of fake news.
Forward citations
Cited by 7 Pith papers
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Four Shades of Life Sciences: A Dataset for Disinformation Detection in the Life Sciences
Introduces FSoLS, a four-class labeled corpus of 2,603 full-text life-science articles, and benchmarks language models that classify disinformative texts with up to 98% F1.
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MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media
MGM augments graph neural networks with globally similar media nodes and language model probabilities, improving factuality and bias classification of news outlets.
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Towards Automatic Detection of Misinformation in Online Medical Videos
A multimodal classifier can flag likely misinformative prostate cancer videos on YouTube with roughly 74% accuracy, using a new expert-labeled dataset.
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REFLEX: Self-Refining Explainable Fact-Checking via Verdict-Anchored Style Control
REFLEX improves explainable fact-checking by using verdict-anchored style control and self-disagreement signals to disentangle fact from style in LLM outputs, achieving SOTA results with minimal self-refined samples.
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Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection
A fake-news detector that spreads LLM-generated pseudo labels over a similarity graph reports state-of-the-art accuracy, but the evaluation is weakened by test-set tuning and self-label leakage at inference.
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ISMAF: Intrinsic-Social Modality Alignment and Fusion for Multimodal Rumor Detection
ISMAF reports state-of-the-art rumor detection accuracy on Weibo and PHEME by aligning text-image intrinsic features with social graph features and fusing them adaptively.
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