@inproceedings{heinrich-etal-2024-automatic,
    title = "Automatic Identification of {COVID}-19-Related Conspiracy Narratives in {G}erman Telegram Channels and Chats",
    author = {Heinrich, Philipp  and
      Blombach, Andreas  and
      Doan Dang, Bao Minh  and
      Zilio, Leonardo  and
      Havenstein, Linda  and
      Dykes, Nathan  and
      Evert, Stephanie  and
      Sch{\"a}fer, Fabian},
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.173",
    pages = "1932--1943",
    abstract = "We are concerned with mapping the discursive landscape of conspiracy narratives surrounding the COVID-19 pandemic. In the present study, we analyse a corpus of more than 1,000 German Telegram posts tagged with 14 fine-grained conspiracy narrative labels by three independent annotators. Since emerging narratives on social media are short-lived and notoriously hard to track, we experiment with different state-of-the-art approaches to few-shot and zero-shot text classification. We report performance in terms of ROC-AUC and in terms of optimal F1, and compare fine-tuned methods with off-the-shelf approaches and human performance.",
}

We are concerned with mapping the discursive landscape of conspiracy narratives surrounding the COVID-19 pandemic. In the present study, we analyse a corpus of more than 1,000 German Telegram posts tagged with 14 fine-grained conspiracy narrative labels by three independent annotators. Since emerging narratives on social media are short-lived and notoriously hard to track, we experiment with different state-of-the-art approaches to few-shot and zero-shot text classification. We report performance in terms of ROC-AUC and in terms of optimal F1, and compare fine-tuned methods with off-the-shelf approaches and human performance.
Heinrich, Philipp Blombach, Andreas Doan Dang, Bao Minh Zilio, Leonardo Havenstein, Linda Dykes, Nathan Evert, Stephanie Sch{\"a}fer, Fabian
Automatic Identification of COVID-19-Related Conspiracy Narratives in German Telegram Channels and Chats