# A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research

arXiv: https://arxiv.org/abs/1802.09416 · HTML: https://mlsanity.com/paper/1802.09416/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2018.

## Built on by (most cited first, 11 of 11)

- [Hate Speech and Counter Speech Detection: Conversational Context Does Matter](https://mlsanity.com/paper/2206.06423/) (2022)
- [A systematic review of Hate Speech automatic detection using Natural Language Processing](https://mlsanity.com/paper/2106.00742/) (2021)
- [From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets](https://mlsanity.com/paper/2404.17874/) (2024)
- [TweetBLM: A Hate Speech Dataset and Analysis of Black Lives Matter-related Microblogs on Twitter](https://mlsanity.com/paper/2108.12521/) (2021)
- [Hate Speech Detection via Dual Contrastive Learning](https://mlsanity.com/paper/2307.05578/) (2023)
- [Improving code-mixed hate detection by native sample mixing: A case study for Hindi-English code-mixed scenario](https://mlsanity.com/paper/2405.20755/) (2024)
- [Evolving Hate Speech Online: An Adaptive Framework for Detection and Mitigation](https://mlsanity.com/paper/2502.10921/) (2025)
- [Who and What? Using Linguistic Features and Annotator Characteristics to Analyze Annotation Variation](https://mlsanity.com/paper/2605.06318/) (2026)
- [LAGOON: An Analysis Tool for Open Source Communities](https://mlsanity.com/paper/2201.11657/) (2022)
- [What is the social benefit of hate speech detection research? A Systematic Review](https://mlsanity.com/paper/2409.17467/) (2024)
- [Web(er) of Hate: A Survey on How Hate Speech Is Typed](https://mlsanity.com/paper/2506.16190/) (2025)

## Abstract (arXiv)

Having a quality annotated corpus is essential especially for applied research. Despite the recent focus of Web science community on researching about cyberbullying, the community dose not still have standard benchmarks. In this paper, we publish first, a quality annotated corpus and second, an offensive words lexicon capturing different types type of harassment as (i) sexual harassment, (ii) racial harassment, (iii) appearance-related harassment, (iv) intellectual harassment, and (v) political harassment.We crawled data from Twitter using our offensive lexicon. Then relied on the human judge to annotate the collected tweets w.r.t. the contextual types because using offensive words is not sufficient to reliably detect harassment. Our corpus consists of 25,000 annotated tweets in five contextual types. We are pleased to share this novel annotated corpus and the lexicon with the research community. The instruction to acquire the corpus has been published on the Git repository.