Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation

• URL: http://arxiv.org/abs/2311.01766v4
• Date: Sat, 9 Dec 2023 16:34:35 GMT
• Title: Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation
• Authors: Xin Yuan, Jie Guo, Weidong Qiu, Zheng Huang, Shujun Li
• Abstract summary: Mis- and disinformation online have become a major societal problem. One common form of mis- and disinformation is out-of-context (OOC) information. We propose a stance extraction network (SEN) that can extract the stances of different pieces of multi-modal evidence.
• Score: 13.134162427636356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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