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Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation
- Source :
- In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4268-4280, Singapore. Association for Computational Linguistics
- Publication Year :
- 2023
-
Abstract
- Mis- and disinformation online have become a major societal problem as major sources of online harms of different kinds. One common form of mis- and disinformation is out-of-context (OOC) information, where different pieces of information are falsely associated, e.g., a real image combined with a false textual caption or a misleading textual description. Although some past studies have attempted to defend against OOC mis- and disinformation through external evidence, they tend to disregard the role of different pieces of evidence with different stances. Motivated by the intuition that the stance of evidence represents a bias towards different detection results, we propose a stance extraction network (SEN) that can extract the stances of different pieces of multi-modal evidence in a unified framework. Moreover, we introduce a support-refutation score calculated based on the co-occurrence relations of named entities into the textual SEN. Extensive experiments on a public large-scale dataset demonstrated that our proposed method outperformed the state-of-the-art baselines, with the best model achieving a performance gain of 3.2% in accuracy.<br />Comment: Accepted and published by EMNLP 2023. Details can be found in https://aclanthology.org/2023.emnlp-main.259
Details
- Database :
- arXiv
- Journal :
- In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4268-4280, Singapore. Association for Computational Linguistics
- Publication Type :
- Report
- Accession number :
- edsarx.2311.01766
- Document Type :
- Working Paper