@inproceedings{heinrich-etal-2024-automatic-identification,
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.",
}
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%0 Conference Proceedings
%T Automatic Identification of COVID-19-Related Conspiracy Narratives in German Telegram Channels and Chats
%A Heinrich, Philipp
%A Blombach, Andreas
%A Doan Dang, Bao Minh
%A Zilio, Leonardo
%A Havenstein, Linda
%A Dykes, Nathan
%A Evert, Stephanie
%A Schäfer, Fabian
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F heinrich-etal-2024-automatic-identification
%X 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.
%U https://aclanthology.org/2024.lrec-main.173
%P 1932-1943
Markdown (Informal)
[Automatic Identification of COVID-19-Related Conspiracy Narratives in German Telegram Channels and Chats](https://aclanthology.org/2024.lrec-main.173) (Heinrich et al., LREC-COLING 2024)
ACL
- Philipp Heinrich, Andreas Blombach, Bao Minh Doan Dang, Leonardo Zilio, Linda Havenstein, Nathan Dykes, Stephanie Evert, and Fabian Schäfer. 2024. Automatic Identification of COVID-19-Related Conspiracy Narratives in German Telegram Channels and Chats. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 1932–1943, Torino, Italia. ELRA and ICCL.