# AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts

arXiv: https://arxiv.org/abs/2010.00038 · HTML: https://mlsanity.com/paper/2010.00038/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations](https://mlsanity.com/paper/2306.04618/) (2023)
- [ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI](https://mlsanity.com/paper/2109.09483/) (2021)
- ["Subverting the Jewtocracy": Online Antisemitism Detection Using Multimodal Deep Learning](https://mlsanity.com/paper/2104.05947/) (2021)
- [Exploring Boundaries and Intensities in Offensive and Hate Speech: Unveiling the Complex Spectrum of Social Media Discourse](https://mlsanity.com/paper/2404.12042/) (2024)
- [Deep Prompt Multi-task Network for Abuse Language Detection](https://mlsanity.com/paper/2403.05268/) (2024)
- [Will I Get Hate Speech Predicting the Volume of Abusive Replies before Posting in Social Media](https://mlsanity.com/paper/2503.03005/) (2025)

## Abstract (arXiv)

While extensive popularity of online social media platforms has made information dissemination faster, it has also resulted in widespread online abuse of different types like hate speech, offensive language, sexist and racist opinions, etc. Detection and curtailment of such abusive content is critical for avoiding its psychological impact on victim communities, and thereby preventing hate crimes. Previous works have focused on classifying user posts into various forms of abusive behavior. But there has hardly been any focus on estimating the severity of abuse and the target. In this paper, we present a first of the kind dataset with 7601 posts from Gab which looks at online abuse from the perspective of presence of abuse, severity and target of abusive behavior. We also propose a system to address these tasks, obtaining an accuracy of ~80% for abuse presence, ~82% for abuse target prediction, and ~65% for abuse severity prediction.