Automatic Identification of COVID-19-Related Conspiracy Narratives in German Telegram Channels and Chats
Philipp Heinrich, Andreas Blombach, Bao Minh Doan Dang, Leonardo Zilio, Linda Havenstein, Nathan Dykes, Stephanie Evert, Fabian Schäfer
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.
BibTeX
@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"
}