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        <title>You Only Query Twice: Multimodal Rumor Detection via Evidential Evaluation from Dual Perspectives</title>
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    <abstract>Current rumor detectors exhibit limitations in fully exploiting responses to the source tweet as essential public opinions, and in explaining and indicating the reliability of the results obtained. Additionally, the joint utilization of both responses and the multimodal source content for detection presents challenges due to the heterogeneous nature of the data points. In this work, to address the first challenge, we initially prompt the Large Language Model (LLM) with both multimodal source content and the corresponding response set to extract contrasting evidence to enable maximal utilization of informative responses. To overcome the second challenge, we introduce an uncertainty-aware evidential evaluator to assess the evidence intensity from the multimodal source content and dual-sided reasoning, from which the final prediction is derived. As we model the second-order probability, we can effectively indicate the model‘s uncertainty (i.e., the reliability) of the results. The reasoning from the correct perspective also serves as a natural language-based explanation. To this end, the third challenge is also addressed as we fully leverage the available resources. Extensive experiments validate the effectiveness, uncertainty awareness in predictions, helpful explainability for human judgment, and superior efficiency of our approach compared to contemporary works utilizing LLMs.</abstract>
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%0 Conference Proceedings
%T You Only Query Twice: Multimodal Rumor Detection via Evidential Evaluation from Dual Perspectives
%A Chen, Junyi
%A Liu, Leyuan
%A Lan, Tian
%A Zhou, Fan
%A Zhang, Xiaosong
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F chen-etal-2025-query
%X Current rumor detectors exhibit limitations in fully exploiting responses to the source tweet as essential public opinions, and in explaining and indicating the reliability of the results obtained. Additionally, the joint utilization of both responses and the multimodal source content for detection presents challenges due to the heterogeneous nature of the data points. In this work, to address the first challenge, we initially prompt the Large Language Model (LLM) with both multimodal source content and the corresponding response set to extract contrasting evidence to enable maximal utilization of informative responses. To overcome the second challenge, we introduce an uncertainty-aware evidential evaluator to assess the evidence intensity from the multimodal source content and dual-sided reasoning, from which the final prediction is derived. As we model the second-order probability, we can effectively indicate the model‘s uncertainty (i.e., the reliability) of the results. The reasoning from the correct perspective also serves as a natural language-based explanation. To this end, the third challenge is also addressed as we fully leverage the available resources. Extensive experiments validate the effectiveness, uncertainty awareness in predictions, helpful explainability for human judgment, and superior efficiency of our approach compared to contemporary works utilizing LLMs.
%U https://aclanthology.org/2025.coling-main.230/
%P 3415-3427

Markdown (Informal)

[You Only Query Twice: Multimodal Rumor Detection via Evidential Evaluation from Dual Perspectives](https://aclanthology.org/2025.coling-main.230/) (Chen et al., COLING 2025)

• You Only Query Twice: Multimodal Rumor Detection via Evidential Evaluation from Dual Perspectives (Chen et al., COLING 2025)

ACL

• Junyi Chen, Leyuan Liu, Tian Lan, Fan Zhou, and Xiaosong Zhang. 2025. You Only Query Twice: Multimodal Rumor Detection via Evidential Evaluation from Dual Perspectives. In Proceedings of the 31st International Conference on Computational Linguistics, pages 3415–3427, Abu Dhabi, UAE. Association for Computational Linguistics.