@inproceedings{mangalik-etal-2025-capturing,
    title = "Capturing Author Self Beliefs in Social Media Language",
    author = "Mangalik, Siddharth  and
      V Ganesan, Adithya  and
      Wheeler, Abigail B.  and
      Kerry, Nicholas  and
      Clifton, Jeremy D. W.  and
      Schwartz, H.  and
      Boyd, Ryan L.",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.69/",
    pages = "1362--1376",
    ISBN = "979-8-89176-251-0",
    abstract = "Measuring the prevalence and dimensions of self beliefs is essential for understanding human self-perception and various psychological outcomes. In this paper, we develop a novel task for classifying language that contains explicit or implicit mentions of the author{'}s self beliefs. We contribute a set of 2,000 human-annotated self beliefs, 100,000 LLM-labeled examples, and 10,000 surveyed self belief paragraphs. We then evaluate several encoder-based classifiers and training routines for this task. Our trained model, SelfAwareNet, achieved an AUC of 0.944, outperforming 0.839 from OpenAI{'}s state-of-the-art GPT-4o model. Using this model we derive data-driven categories of self beliefs and demonstrate their ability to predict valence, depression, anxiety, and stress. We release the resulting self belief classification model and annotated datasets for use in future research."
}