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Assessing the Persuasive Effect of AI-Generated Image Support of Arguments

Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026)

DOI:10.63317/2b6xxx7vmyra

Abstract

Argumentation is, at its core, an inherently verbal activity. Yet, other modalities may support arguments, one of which are images. In the argument mining community, this combination has not received much attention yet. While a few previous works studied whether images can make argumentative texts more effective in persuading people, the images that were considered matched the texts loosely only, or they were heavily text-based themselves. In this paper, we take the step to study to what extent the persuasive effect of textual arguments can be supported by images specifically created for this purpose. For a consistent experiment design, we combine NLP with image generation to synthesize both arguments and images with generative AI, for five controversial topics and for two rhetorical strategies. In two consecutive user studies, we first determine the best-matching image for each argument and then compare the perceived effect of bare textual arguments to those that are supported by an image. Our results suggest that the images may increase the persuasive effect of argumentative texts, but with variance across topics.

Details

Paper ID
lrec2026-main-641
Pages
pp. 8085-8095
BibKey
quadras-etal-2026-assessing
Editor
N/A
Publisher
European Language Resources Association (ELRA)
ISSN
2522-2686
ISBN
978-2-493814-49-4
Conference
The Fifteenth Language Resources and Evaluation Conference (LREC 2026)
Location
Palma, Mallorca, Spain
Date
11 May 2026 16 May 2026

Authors

  • MQ

    Mackwyn Quadras

  • MS

    Manfred Stede

  • HW

    Henning Wachsmuth

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