@inproceedings{soufleri-ananiadou-2025-enhancing,
    title = "Enhancing Stress Detection on Social Media Through Multi-Modal Fusion of Text and Synthesized Visuals",
    author = "Soufleri, Efstathia  and
      Ananiadou, Sophia",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Miwa, Makoto  and
      Tsujii, Junichi",
    booktitle = "ACL 2025",
    month = aug,
    year = "2025",
    address = "Viena, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bionlp-1.4/",
    pages = "34--43",
    ISBN = "979-8-89176-275-6",
    abstract = "Social media platforms generate an enormous volume of multi-modal data, yet stress detection research has predominantly relied on text-based analysis. In this work, we propose a novel framework that integrates textual content with synthesized visual cues to enhance stress detection. Using the generative model DALL{\textperiodcentered}E, we synthesize images from social media posts, which are then fused with text through the multi-modal capabilities of a pre-trained CLIP model. Our approach is evaluated on the Dreaddit dataset, where a classifier trained on frozen CLIP features achieves 94.90{\%} accuracy, and full fine-tuning further improves performance to 98.41{\%}. These results underscore the integration of synthesized visuals with textual data not only enhances stress detection but also offers a robust method over traditional text-only methods, paving the way for innovative approaches in mental health monitoring and social media analytics."
}