ProvocationProbe: Instigating Hate Speech Dataset from Twitter (2410.19687v1)
Abstract: In the recent years online social media platforms has been flooded with hateful remarks such as racism, sexism, homophobia etc. As a result, there have been many measures taken by various social media platforms to mitigate the spread of hate-speech over the internet. One particular concept within the domain of hate speech is instigating hate, which involves provoking hatred against a particular community, race, colour, gender, religion or ethnicity. In this work, we introduce \textit{ProvocationProbe} - a dataset designed to explore what distinguishes instigating hate speech from general hate speech. For this study, we collected around twenty thousand tweets from Twitter, encompassing a total of nine global controversies. These controversies span various themes including racism, politics, and religion. In this paper, i) we present an annotated dataset after comprehensive examination of all the controversies, ii) we also highlight the difference between hate speech and instigating hate speech by identifying distinguishing features, such as targeted identity attacks and reasons for hate.
- Implementation of Machine Learning to Detect Hate Speech in Bangla Language. In 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART). 317–320. https://doi.org/10.1109/SMART46866.2019.9117214
- Deep Learning for Hate Speech Detection in Tweets. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE. https://doi.org/10.1145/3041021.3054223
- A Transformer Based Approach for Abuse Detection in Code Mixed Indic Languages. ACM transactions on Asian and low-resource language information processing (2022).
- Mean Birds: Detecting Aggression and Bullying on Twitter. 13–22. https://doi.org/10.1145/3091478.3091487
- Cross-Platform Evaluation for Italian Hate Speech Detection. In CLiC-it 2019 - 6th Annual Conference of the Italian Association for Computational Linguistics. Bari, Italy. https://hal.science/hal-02381152
- Racial Bias in Hate Speech and Abusive Language Detection Datasets. In Proceedings of the Third Workshop on Abusive Language Online, Sarah T. Roberts, Joel Tetreault, Vinodkumar Prabhakaran, and Zeerak Waseem (Eds.). Association for Computational Linguistics, Florence, Italy. https://doi.org/10.18653/v1/W19-3504
- Automated Hate Speech Detection and the Problem of Offensive Language. Proceedings of the International AAAI Conference on Web and Social Media 11 (03 2017). https://doi.org/10.1609/icwsm.v11i1.14955
- Peer to Peer Hate: Hate Speech Instigators and Their Targets. Proceedings of the International AAAI Conference on Web and Social Media 12 (04 2018). https://doi.org/10.1609/icwsm.v12i1.15038
- Paula Fortuna and Sérgio Nunes. 2018. A Survey on Automatic Detection of Hate Speech in Text. ACM Comput. Surv. 51, 4, Article 85 (jul 2018), 30 pages. https://doi.org/10.1145/3232676
- Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior. Proceedings of the International AAAI Conference on Web and Social Media 12 (06 2018). https://doi.org/10.1609/icwsm.v12i1.14991
- A Large Labeled Corpus for Online Harassment Research. 229–233. https://doi.org/10.1145/3091478.3091509
- Exploring Hate Speech Detection in Multimodal Publications. 1459–1467. https://doi.org/10.1109/WACV45572.2020.9093414
- Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment. 4333–4345. https://doi.org/10.1145/3580305.3599896
- A Survey on Online User Aggression: Content Detection and Behavioural Analysis on Social Media Platforms. arXiv preprint arXiv:2311.09367 (2023).
- You are what your feeds makes you: A study of user aggressive behaviour on Twitter. Authorea Preprints (2023).
- HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection. Proceedings of the AAAI Conference on Artificial Intelligence 35 (05 2021), 14867–14875. https://doi.org/10.1609/aaai.v35i17.17745
- A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media.
- Abusive Language Detection in Online User Content. 145–153. https://doi.org/10.1145/2872427.2883062
- Uncovering the Root of Hate Speech: A Dataset for Identifying Hate Instigating Speech. 6236–6245. https://doi.org/10.18653/v1/2023.findings-emnlp.412
- Deeper Attention to Abusive User Content Moderation. 1125–1135. https://doi.org/10.18653/v1/D17-1117
- Automatic Detection of Hate Speech on Facebook Using Sentiment and Emotion Analysis. 169–174. https://doi.org/10.1109/ICAIIC.2019.8669073
- Anna Schmidt and Michael Wiegand. 2017. A Survey on Hate Speech Detection using Natural Language Processing. In Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, Lun-Wei Ku and Cheng-Te Li (Eds.). Association for Computational Linguistics, Valencia, Spain. https://doi.org/10.18653/v1/W17-1101
- Vigneshwaran Shankaran and Rajesh Sharma. 2024. Analyzing Toxicity in Deep Conversations: A Reddit Case Study. arXiv preprint arXiv:2404.07879 (2024).
- Would Your Tweet Invoke Hate on the Fly? Forecasting Hate Intensity of Reply Threads on Twitter. https://doi.org/10.1145/3447548.3467150
- Analyzing the Targets of Hate in Online Social Media. Proceedings of the International AAAI Conference on Web and Social Media 10 (03 2016). https://doi.org/10.1609/icwsm.v10i1.14811
- Zeerak Waseem. 2016. Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter. 138–142. https://doi.org/10.18653/v1/W16-5618
- Zeerak Waseem and Dirk Hovy. 2016. Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter. 88–93. https://doi.org/10.18653/v1/N16-2013
- A Quantitative Approach to Understanding Online Antisemitism. Proceedings of the International AAAI Conference on Web and Social Media 14 (05 2020), 786–797. https://doi.org/10.1609/icwsm.v14i1.7343
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.