@inproceedings{sharma-patil-2026-improves,
title = "What Improves Multimodal Misinformation Detection? Answers from a Large-Scale Empirical Study",
author = "Sharma, Akshit and
Patil, Prashant W",
editor = "Zhang, Chen and
Allaway, Emily and
M{'}hamdi, Meryem and
Limkonchotiwat, Peerat and
Bai, Richard He and
T.Y.S.S., Santosh and
Thapa, Surendrabikram and
Li, Yinqiao",
booktitle = "Proceedings of the 10th Widening {NLP} Workshop",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.winlp-main.4/",
pages = "37--47",
ISBN = "979-8-89176-472-9",
abstract = "Multimodal misinformation is increasingly crafted to look convincing by pairing a textual claim with an image that appears to ``prove'' it. Yet in practice, building effective detectors often hinges on a small set of design choices that are rarely examined in a controlled way. In this paper, we conduct a large-scale study of multimodal design choices for misinformation detection with over 3,375 experiments- spanning three benchmark datasets and a broad range of pre-trained vision and language backbones. Through systematic comparisons and targeted robustness analyses, we distill practical guidance on which design choices help, when do they fail silently, and what aspects of the pipeline most strongly shape model behavior, answering 4 key Research Questions (RQs). We aim to provide a reliable foundation for designing stronger and more dependable multimodal misinformation detection systems, thus contributing to the broader research community."
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%0 Conference Proceedings
%T What Improves Multimodal Misinformation Detection? Answers from a Large-Scale Empirical Study
%A Sharma, Akshit
%A Patil, Prashant W.
%Y Zhang, Chen
%Y Allaway, Emily
%Y M’hamdi, Meryem
%Y Limkonchotiwat, Peerat
%Y Bai, Richard He
%Y T.Y.S.S., Santosh
%Y Thapa, Surendrabikram
%Y Li, Yinqiao
%S Proceedings of the 10th Widening NLP Workshop
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-472-9
%F sharma-patil-2026-improves
%X Multimodal misinformation is increasingly crafted to look convincing by pairing a textual claim with an image that appears to “prove” it. Yet in practice, building effective detectors often hinges on a small set of design choices that are rarely examined in a controlled way. In this paper, we conduct a large-scale study of multimodal design choices for misinformation detection with over 3,375 experiments- spanning three benchmark datasets and a broad range of pre-trained vision and language backbones. Through systematic comparisons and targeted robustness analyses, we distill practical guidance on which design choices help, when do they fail silently, and what aspects of the pipeline most strongly shape model behavior, answering 4 key Research Questions (RQs). We aim to provide a reliable foundation for designing stronger and more dependable multimodal misinformation detection systems, thus contributing to the broader research community.
%U https://aclanthology.org/2026.winlp-main.4/
%P 37-47
Markdown (Informal)
[What Improves Multimodal Misinformation Detection? Answers from a Large-Scale Empirical Study](https://aclanthology.org/2026.winlp-main.4/) (Sharma & Patil, WiNLP 2026)
ACL