@inproceedings{gera-neal-2026-understanding,
    title = "Understanding the Linguistic Cues Behind Stance Detection",
    author = "Gera, Parush  and
      Neal, Tempestt",
    editor = "Mohammad, Saif M.  and
      Ousidhoum, Nedjma",
    booktitle = "Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*{SEM} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.starsem-conference.20/",
    pages = "302--316",
    ISBN = "979-8-89176-413-2",
    abstract = "Stance detection seeks to determine whether a text expresses a position in favor of, against, or neutral toward a target. Despite advances in neural architectures, performance remains inconsistent across datasets. To better understand these disparities, we analyze over 75K samples from four benchmark datasets using six neural models, focusing on stylistic and pragmatic language features rather than architectures or external knowledge. We extract 43 features spanning lexical richness, syntactic complexity, affective tone, and hedging, and assess their impact through both Logistic Regression and SHAP analyses. Our findings reveal distinct stylistic profiles for each stance: favor is best detected when expressed concisely with minimal hedging; against when paired with strong negative emotions and greater lexical variety; and none when texts are lexically simple and emotionally neutral. Across classes, errors arise from excessive complexity, mixed emotional signals, and overuse of hedging. These results advance understanding of what drives success and failure in stance detection."
}