Explainable Tsetlin Machine Framework for Fake News Detection with Credibility Score Assessment (2022.lrec-1)
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| Challenge: | Existing models for fake news classification are difficult to explain and quality-assure . however, they are black-box-based and lack a clear explanation of their decisions. |
| Approach: | They propose an interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM) they use conjunctive clauses to capture lexical and semantic properties of both true and fake news text and use clause ensembles to calculate the credibility of fake news. |
| Outcome: | The proposed framework outperforms baseline models on PolitiFact and GossipCop datasets in terms of accuracy and provides higher F1-score than BERT and XLNet, but lower accuracy. |
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| Challenge: | Existing methods for detecting fake news are limited due to non-transparent reasoning processes and inherent risks of integration with large language models. |
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Claim veracity assessment for explainable fake news detection (2025.coling-main)
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| Challenge: | Recent approaches to fake news detection focus on textual features without external facts, which may lead to a misrepresentation of the truth. |
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| Challenge: | Recent work on automated fact-checking does not consider external evidence, but requires rich lexicons. |
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Adapting Fake News Detection to the Era of Large Language Models (2024.findings-naacl)
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| Challenge: | a gap exists in understanding the interplay between machine-paraphrased real news, machine-generated fake news, and human-written real news . false information is easier to generate but harder to detect due to the bias of detectors against machine-generated texts . |
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Automatic Fake News Detection: Are Models Learning to Reason? (2021.acl-short)
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| Challenge: | Automated fake news detection is a critical but challenging problem in NLP . social media has accelerated the spread of fake news, threatening public safety . |
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Towards Robust Evidence-Aware Fake News Detection via Improving Semantic Perception (2024.lrec-main)
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| Challenge: | Existing methods lack sufficient semantic perception and are easily blinded by textual expressions. |
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