A Large-Scale English Multi-Label Twitter Dataset for Cyberbullying and Online Abuse Detection (2024)

@inproceedings{salawu-etal-2021-large, title = "A Large-Scale {E}nglish Multi-Label {T}witter Dataset for Cyberbullying and Online Abuse Detection", author = "Salawu, Semiu and Lumsden, Jo and He, Yulan", editor = "Mostafazadeh Davani, Aida and Kiela, Douwe and Lambert, Mathias and Vidgen, Bertie and Prabhakaran, Vinodkumar and Waseem, Zeerak", booktitle = "Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.woah-1.16", doi = "10.18653/v1/2021.woah-1.16", pages = "146--156", abstract = "In this paper, we introduce a new English Twitter-based dataset for cyberbullying detection and online abuse. Comprising 62,587 tweets, this dataset was sourced from Twitter using specific query terms designed to retrieve tweets with high probabilities of various forms of bullying and offensive content, including insult, trolling, profanity, sarcasm, threat, p*rn and exclusion. We recruited a pool of 17 annotators to perform fine-grained annotation on the dataset with each tweet annotated by three annotators. All our annotators are high school educated and frequent users of social media. Inter-rater agreement for the dataset as measured by Krippendorff{'}s Alpha is 0.67. Analysis performed on the dataset confirmed common cyberbullying themes reported by other studies and revealed interesting relationships between the classes. The dataset was used to train a number of transformer-based deep learning models returning impressive results.",}

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%0 Conference Proceedings%T A Large-Scale English Multi-Label Twitter Dataset for Cyberbullying and Online Abuse Detection%A Salawu, Semiu%A Lumsden, Jo%A He, Yulan%Y Mostafazadeh Davani, Aida%Y Kiela, Douwe%Y Lambert, Mathias%Y Vidgen, Bertie%Y Prabhakaran, Vinodkumar%Y Waseem, Zeerak%S Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)%D 2021%8 August%I Association for Computational Linguistics%C Online%F salawu-etal-2021-large%X In this paper, we introduce a new English Twitter-based dataset for cyberbullying detection and online abuse. Comprising 62,587 tweets, this dataset was sourced from Twitter using specific query terms designed to retrieve tweets with high probabilities of various forms of bullying and offensive content, including insult, trolling, profanity, sarcasm, threat, p*rn and exclusion. We recruited a pool of 17 annotators to perform fine-grained annotation on the dataset with each tweet annotated by three annotators. All our annotators are high school educated and frequent users of social media. Inter-rater agreement for the dataset as measured by Krippendorff’s Alpha is 0.67. Analysis performed on the dataset confirmed common cyberbullying themes reported by other studies and revealed interesting relationships between the classes. The dataset was used to train a number of transformer-based deep learning models returning impressive results.%R 10.18653/v1/2021.woah-1.16%U https://aclanthology.org/2021.woah-1.16%U https://doi.org/10.18653/v1/2021.woah-1.16%P 146-156

Markdown (Informal)

[A Large-Scale English Multi-Label Twitter Dataset for Cyberbullying and Online Abuse Detection](https://aclanthology.org/2021.woah-1.16) (Salawu et al., WOAH 2021)

• A Large-Scale English Multi-Label Twitter Dataset for Cyberbullying and Online Abuse Detection (Salawu et al., WOAH 2021)

ACL

• Semiu Salawu, Jo Lumsden, and Yulan He. 2021. A Large-Scale English Multi-Label Twitter Dataset for Cyberbullying and Online Abuse Detection. In Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021), pages 146–156, Online. Association for Computational Linguistics.