@inproceedings{zhang-etal-2025-exploring-artificial,
    title = "Exploring Artificial Image Generation for Stance Detection",
    author = "Zhang, Zhengkang  and
      Wang, Zhongqing  and
      Zhou, Guodong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1004/",
    pages = "19857--19872",
    ISBN = "979-8-89176-332-6",
    abstract = "Stance detection is a task aimed at identifying and analyzing the author{'}s stance from text. Previous studies have primarily focused on the text, which may not fully capture the implicit stance conveyed by the author. To address this limitation, we propose a novel approach that transforms original texts into artificially generated images and uses the visual representation to enhance stance detection. Our approach first employs a text-to-image model to generate candidate images for each text. These images are carefully crafted to adhere to three specific criteria: textual relevance, target consistency, and stance consistency. Next, we introduce a comprehensive evaluation framework to select the optimal image for each text from its generated candidates. Subsequently, we introduce a multimodal stance detection model that leverages both the original textual content and the generated image to identify the author{'}s stance. Experiments demonstrate the effectiveness of our approach and highlight the importance of artificially generated images for stance detection."
}