# A Unified Deep Learning Architecture for Abuse Detection

arXiv: https://arxiv.org/abs/1802.00385 · HTML: https://mlsanity.com/paper/1802.00385/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2018.

## Built on by (most cited first, 30 of 37)

- [HateMM: A Multi-Modal Dataset for Hate Video Classification](https://mlsanity.com/paper/2305.03915/) (2023)
- [Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go](https://mlsanity.com/paper/2103.00153/) (2021)
- [Explainable AI: current status and future directions](https://mlsanity.com/paper/2107.07045/) (2021)
- [DeepHate: Hate Speech Detection via Multi-Faceted Text Representations](https://mlsanity.com/paper/2103.11799/) (2021)
- [Hate Personified: Investigating the role of LLMs in content moderation](https://mlsanity.com/paper/2410.02657/) (2024)
- ["Subverting the Jewtocracy": Online Antisemitism Detection Using Multimodal Deep Learning](https://mlsanity.com/paper/2104.05947/) (2021)
- [Proactively Reducing the Hate Intensity of Online Posts via Hate Speech Normalization](https://mlsanity.com/paper/2206.04007/) (2022)
- [Tox-BART: Leveraging Toxicity Attributes for Explanation Generation of Implicit Hate Speech](https://mlsanity.com/paper/2406.03953/) (2024)
- [ToxVidLM: A Multimodal Framework for Toxicity Detection in Code-Mixed Videos](https://mlsanity.com/paper/2405.20628/) (2024)
- [Contextual-Lexicon Approach for Abusive Language Detection](https://mlsanity.com/paper/2104.12265/) (2021)
- [Character-level HyperNetworks for Hate Speech Detection](https://mlsanity.com/paper/2111.06336/) (2021)
- [Predicting Hate Intensity of Twitter Conversation Threads](https://mlsanity.com/paper/2206.08406/) (2022)
- [Reliable Decision from Multiple Subtasks through Threshold Optimization: Content Moderation in the Wild](https://mlsanity.com/paper/2208.07522/) (2022)
- [Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment](https://mlsanity.com/paper/2306.01105/) (2023)
- [IIITT@LT-EDI-EACL2021-Hope Speech Detection: There is always Hope in Transformers](https://mlsanity.com/paper/2104.09066/) (2021)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [Demonstrations Are All You Need: Advancing Offensive Content Paraphrasing using In-Context Learning](https://mlsanity.com/paper/2310.10707/) (2023)
- [Hatemongers ride on echo chambers to escalate hate speech diffusion](https://mlsanity.com/paper/2302.02479/) (2023)
- [Using LLMs to discover emerging coded antisemitic hate-speech in extremist social media](https://mlsanity.com/paper/2401.10841/) (2024)
- [Enriching Abusive Language Detection with Community Context](https://mlsanity.com/paper/2206.08445/) (2022)
- [Probing Critical Learning Dynamics of PLMs for Hate Speech Detection](https://mlsanity.com/paper/2402.02144/) (2024)
- [Transfer Learning Approach for Arabic Offensive Language Detection System -- BERT-Based Model](https://mlsanity.com/paper/2102.05708/) (2021)
- [Safer Conversational AI as a Source of User Delight](https://mlsanity.com/paper/2304.09865/) (2023)
- [Focal Inferential Infusion Coupled with Tractable Density Discrimination for Implicit Hate Detection](https://mlsanity.com/paper/2309.11896/) (2023)
- [KTCR: Improving Implicit Hate Detection with Knowledge Transfer driven Concept Refinement](https://mlsanity.com/paper/2410.15314/) (2024)
- [Revealing Hidden Mechanisms of Cross-Country Content Moderation with Natural Language Processing](https://mlsanity.com/paper/2503.05280/) (2025)
- [Multimodal Hate Detection Using Dual-Stream Graph Neural Networks](https://mlsanity.com/paper/2509.13515/) (2025)
- [Fine-Tuning Approach for Arabic Offensive Language Detection System: BERT-Based Model](https://mlsanity.com/paper/2203.03542/) (2022)
- [InfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks](https://mlsanity.com/paper/2402.14702/) (2024)
- [Monitoring the evolution of antisemitic discourse on extremist social media using BERT](https://mlsanity.com/paper/2403.05548/) (2024)

## Abstract (arXiv)

Hate speech, offensive language, sexism, racism and other types of abusive behavior have become a common phenomenon in many online social media platforms. In recent years, such diverse abusive behaviors have been manifesting with increased frequency and levels of intensity. This is due to the openness and willingness of popular media platforms, such as Twitter and Facebook, to host content of sensitive or controversial topics. However, these platforms have not adequately addressed the problem of online abusive behavior, and their responsiveness to the effective detection and blocking of such inappropriate behavior remains limited. In the present paper, we study this complex problem by following a more holistic approach, which considers the various aspects of abusive behavior. To make the approach tangible, we focus on Twitter data and analyze user and textual properties from different angles of abusive posting behavior. We propose a deep learning architecture, which utilizes a wide variety of available metadata, and combines it with automatically-extracted hidden patterns within the text of the tweets, to detect multiple abusive behavioral norms which are highly inter-related. We apply this unified architecture in a seamless, transparent fashion to detect different types of abusive behavior (hate speech, sexism vs. racism, bullying, sarcasm, etc.) without the need for any tuning of the model architecture for each task. We test the proposed approach with multiple datasets addressing different and multiple abusive behaviors on Twitter. Our results demonstrate that it largely outperforms the state-of-art methods (between 21 and 45% improvement in AUC, depending on the dataset).