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DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning
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Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deficit by considering external sources related to a claim. However, these methods require substantial feature modeling and rich lexicons. This paper overcomes these limitations of prior work with an end-to-end model for evidence-aware credibility assessment of arbitrary textual claims, without any human intervention. It presents a neural network model that judiciously aggregates signals from external evidence articles, the language of these articles and the trustworthiness of their sources. It also derives informative features for generating user-comprehensible explanations that makes the neural network predictions transparent to the end-user. Experiments with four datasets and ablation studies show the strength of our method.
Forward citations
Cited by 4 Pith papers
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RoE-FND: A Case-Based Reasoning Approach with Dual Verification for Fake News Detection via LLMs
RoE-FND improves LLM fake news detection by storing reflections on past reasoning errors and retrieving them as advice when judging new claims.
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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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LRCTI: A Large Language Model-Based Framework for Multi-Step Evidence Retrieval and Reasoning in Cyber Threat Intelligence Credibility Verification
LRCTI uses an LLM to summarize threat reports, retrieve evidence in several rounds, and judge each claim credible or incredible, reporting strong F1 gains on CTI-200 and PolitiFact.
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The Mass, Fake News, and Cognition Security
The paper defines Cognition Security (CogSec) as a multidisciplinary field studying cognitive impacts of fake news and outlines research challenges, techniques, and future directions.
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