{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/red-dot-multimodal-fact-checking-via-relevant","title":"RED-DOT: Multimodal Fact-checking via Relevant Evidence Detection","arxiv_id":"2311.09939","date":"2023-11-16","proceeding":null,"authors":["Stefanos-Iordanis Papadopoulos","Christos Koutlis","Symeon Papadopoulos","Panagiotis C. Petrantonakis"],"abstract":"Online misinformation is often multimodal in nature, i.e., it is caused by misleading associations between texts and accompanying images. To support the fact-checking process, researchers have been recently developing automatic multimodal methods that gather and analyze external information, evidence, related to the image-text pairs under examination. However, prior works assumed all external information collected from the web to be relevant. In this study, we introduce a \"Relevant Evidence Detection\" (RED) module to discern whether each piece of evidence is relevant, to support or refute the claim. Specifically, we develop the \"Relevant Evidence Detection Directed Transformer\" (RED-DOT) and explore multiple architectural variants (e.g., single or dual-stage) and mechanisms (e.g., \"guided attention\"). Extensive ablation and comparative experiments demonstrate that RED-DOT achieves significant improvements over the state-of-the-art (SotA) on the VERITE benchmark by up to 33.7%. Furthermore, our evidence re-ranking and element-wise modality fusion led to RED-DOT surpassing the SotA on NewsCLIPings+ by up to 3% without the need for numerous evidence or multiple backbone encoders. We release our code at: https://github.com/stevejpapad/relevant-evidence-detection","url_abs":"https://arxiv.org/abs/2311.09939v2","url_pdf":"https://arxiv.org/pdf/2311.09939v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"red-dot-multimodal-fact-checking-via-relevant","repo_url":"https://github.com/stevejpapad/relevant-evidence-detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"fact-checking","task_name":"Fact Checking"},{"task_slug":"misinformation","task_name":"Misinformation"},{"task_slug":"re-ranking","task_name":"Re-Ranking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.09939","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.09939"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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