Stanceosaurus: Classifying Stance Towards Multicultural Misinformation (2022.emnlp-main)
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| Challenge: | Existing corpora focus on misinformation spreading within western countries. |
| Approach: | They present a new corpus of tweets annotated with stance towards 250 misinformation claims. |
| Outcome: | The proposed method achieves 53.1 F1 on Hindi and 50.4 F1 in Arabic without any target-language fine-tuning. |
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| Challenge: | Current approaches to fact-checking are time-consuming and tedious. |
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Anis Charfi, Mabrouka Ben-Sghaier, Andria Samy Raouf Atalla, Raghda Akasheh, Sara Al-Emadi, Wajdi Zaghouani
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| Challenge: | stance detection is a method to determine the attitude of a text with respect to a specific topic or claim. |
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A Survey on Stance Detection for Mis- and Disinformation Identification (2022.findings-naacl)
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| Challenge: | Understanding attitudes expressed in texts plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentional false information). |
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| Challenge: | Stance detection is a task that focuses on the classification of a writer’s viewpoint towards a target. |
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CoFE: A New Dataset of Intra-Multilingual Multi-target Stance Classification from an Online European Participatory Democracy Platform (2022.aacl-short)
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| Challenge: | Stance Recognition is a useful tool for many real-life applications, from misinformation detection to poll verification. |
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Tribrid: Stance Classification with Neural Inconsistency Detection (2021.emnlp-main)
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| Challenge: | a new neural architecture can be used to classify stances on social media without relying on linguistic features. |
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