HQP: A Human-Annotated Dataset for Detecting Online Propaganda
Abdurahman Maarouf, Dominik Bär, Dominique Geissler, Stefan Feuerriegel
Abstract
Online propaganda poses a severe threat to the integrity of societies. However, existing datasets for detecting online propaganda have a key limitation: they were annotated using weak labels that can be noisy and even incorrect. To address this limitation, our work makes the following contributions: (1) We present HQP: a novel dataset (N=30000) for detecting online propaganda with high-quality labels. To the best of our knowledge, HQP is the first large-scale dataset for detecting online propaganda that was created through human annotation. (2) We show empirically that state-of-the-art language models fail in detecting online propaganda when trained with weak labels (AUC: 64.03). In contrast, state-of-the-art language models can accurately detect online propaganda when trained with our high-quality labels (AUC: 92.25), which is an improvement of 44%. (3) We show that prompt-based learning using a small sample of high-quality labels can still achieve a reasonable performance (AUC: 80.27) while significantly reducing the cost of labeling. (4) We extend HQP to HQP+ to test how well propaganda across different contexts can be detected. Crucially, our work highlights the importance of high-quality labels for sensitive NLP tasks such as propaganda detection.- Anthology ID:
- 2024.findings-acl.363
- Volume:
- Findings of the Association for Computational Linguistics ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand and virtual meeting
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6064–6089
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.363
- DOI:
- Cite (ACL):
- Abdurahman Maarouf, Dominik Bär, Dominique Geissler, and Stefan Feuerriegel. 2024. HQP: A Human-Annotated Dataset for Detecting Online Propaganda. In Findings of the Association for Computational Linguistics ACL 2024, pages 6064–6089, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- HQP: A Human-Annotated Dataset for Detecting Online Propaganda (Maarouf et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.findings-acl.363.pdf