# Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approach

arXiv: https://arxiv.org/abs/1710.07394 · HTML: https://mlsanity.com/paper/1710.07394/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2017.

## Built on by (most cited first, 17 of 17)

- [Latent Hatred: A Benchmark for Understanding Implicit Hate Speech](https://mlsanity.com/paper/2109.05322/) (2021)
- [Towards generalisable hate speech detection: a review on obstacles and solutions](https://mlsanity.com/paper/2102.08886/) (2021)
- [HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection](https://mlsanity.com/paper/2103.14972/) (2021)
- [Lifelong Learning of Hate Speech Classification on Social Media](https://mlsanity.com/paper/2106.02821/) (2021)
- [Tox-BART: Leveraging Toxicity Attributes for Explanation Generation of Implicit Hate Speech](https://mlsanity.com/paper/2406.03953/) (2024)
- ["Cold, Calculated, and Condescending": How AI Identifies and Explains Ableism Compared to Disabled People](https://mlsanity.com/paper/2410.03448/) (2024)
- [Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment](https://mlsanity.com/paper/2306.01105/) (2023)
- [A Modular Taxonomy for Hate Speech Definitions and Its Impact on Zero-Shot LLM Classification Performance](https://mlsanity.com/paper/2506.18576/) (2025)
- [Hidden behind the obvious: misleading keywords and implicitly abusive language on social media](https://mlsanity.com/paper/2205.01374/) (2022)
- [Making FETCH! Happen: Finding Emergent Dog Whistles Through Common Habitats](https://mlsanity.com/paper/2412.12072/) (2024)
- [Towards Weakly-Supervised Hate Speech Classification Across Datasets](https://mlsanity.com/paper/2305.02637/) (2023)
- [Cracking the Code: Enhancing Implicit Hate Speech Detection through Coding Classification](https://mlsanity.com/paper/2506.04693/) (2025)
- [How Much Hate with #china? A Preliminary Analysis on China-related Hateful Tweets Two Years After the Covid Pandemic Began](https://mlsanity.com/paper/2211.06116/) (2022)
- [Evolving Hate Speech Online: An Adaptive Framework for Detection and Mitigation](https://mlsanity.com/paper/2502.10921/) (2025)
- [AmpleHate: Amplifying the Attention for Versatile Implicit Hate Detection](https://mlsanity.com/paper/2505.19528/) (2025)
- [Specializing General-purpose LLM Embeddings for Implicit Hate Speech Detection across Datasets](https://mlsanity.com/paper/2508.20750/) (2025)
- [The Unappreciated Role of Intent in Algorithmic Moderation of Social Media Content](https://mlsanity.com/paper/2405.11030/) (2024)

## Abstract (arXiv)

In the wake of a polarizing election, social media is laden with hateful content. To address various limitations of supervised hate speech classification methods including corpus bias and huge cost of annotation, we propose a weakly supervised two-path bootstrapping approach for an online hate speech detection model leveraging large-scale unlabeled data. This system significantly outperforms hate speech detection systems that are trained in a supervised manner using manually annotated data. Applying this model on a large quantity of tweets collected before, after, and on election day reveals motivations and patterns of inflammatory language.