A Benchmark Dataset for Learning to Intervene in Online Hate Speech
Jing Qian, Anna Bethke, Yinyin Liu, Elizabeth Belding, William Yang Wang
Abstract
Countering online hate speech is a critical yet challenging task, but one which can be aided by the use of Natural Language Processing (NLP) techniques. Previous research has primarily focused on the development of NLP methods to automatically and effectively detect online hate speech while disregarding further action needed to calm and discourage individuals from using hate speech in the future. In addition, most existing hate speech datasets treat each post as an isolated instance, ignoring the conversational context. In this paper, we propose a novel task of generative hate speech intervention, where the goal is to automatically generate responses to intervene during online conversations that contain hate speech. As a part of this work, we introduce two fully-labeled large-scale hate speech intervention datasets collected from Gab and Reddit. These datasets provide conversation segments, hate speech labels, as well as intervention responses written by Mechanical Turk Workers. In this paper, we also analyze the datasets to understand the common intervention strategies and explore the performance of common automatic response generation methods on these new datasets to provide a benchmark for future research.- Anthology ID:
- D19-1482
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4755–4764
- Language:
- URL:
- https://aclanthology.org/D19-1482
- DOI:
- 10.18653/v1/D19-1482
- Cite (ACL):
- Jing Qian, Anna Bethke, Yinyin Liu, Elizabeth Belding, and William Yang Wang. 2019. A Benchmark Dataset for Learning to Intervene in Online Hate Speech. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4755–4764, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- A Benchmark Dataset for Learning to Intervene in Online Hate Speech (Qian et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/D19-1482.pdf
- Code
- jing-qian/A-Benchmark-Dataset-for-Learning-to-Intervene-in-Online-Hate-Speech