@inproceedings{richter-etal-2026-ttlab,
    title = "{TTL}ab at {S}em{E}val-2026 Task 10: Transformer-based Approaches for Psycholinguistic Conspiracy Detection in Social Media Discourse",
    author = "Richter, Samuel  and
      Marreddy, Mounika  and
      Mehler, Alexander",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.103/",
    pages = "727--734",
    ISBN = "979-8-89176-414-9",
    abstract = "Online platforms increasingly host conspiracy narratives that shape public debate, reduce trust in institutions, and contribute to polarization, highlighting the need for reliable automatic detection systems. In this paper, we participate in SemEval-2026 Task 10 (PsyCoMark), focusing on conspiracy detection in Reddit conversations using transformer-based models. We evaluate four approaches: raw text, structured psycholinguistic markers, a combined representation, and a stacking ensemble. Our results show that marker-based representations outperform text-only models, and that ensembling further improves robustness. These findings demonstrate the value of incorporating structured psychological cues for scalable conspiracy detection."
}