# Hater-O-Genius Aggression Classification using Capsule Networks

arXiv: https://arxiv.org/abs/2105.11219 · HTML: https://mlsanity.com/paper/2105.11219/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 24 May 2021.

## Builds on (most cited first, 9 of 9)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [Aggression-annotated Corpus of Hindi-English Code-mixed Data](https://mlsanity.com/paper/1803.09402/) (2018)
- [Investigating Capsule Networks with Dynamic Routing for Text Classification](https://mlsanity.com/paper/1804.00538/) (2018)
- [Text Classification using Capsules](https://mlsanity.com/paper/1808.03976/) (2018)
- [RiTUAL-UH at TRAC 2018 Shared Task: Aggression Identification](https://mlsanity.com/paper/1807.11712/) (2018)
- [A Unified System for Aggression Identification in English Code-Mixed and Uni-Lingual Texts](https://mlsanity.com/paper/2001.05493/) (2020)

## Built on by (most cited first, 7 of 7)

- [Memotion 3: Dataset on Sentiment and Emotion Analysis of Codemixed Hindi-English Memes](https://mlsanity.com/paper/2303.09892/) (2023)
- [Offense Detection in Dravidian Languages using Code-Mixing Index based Focal Loss](https://mlsanity.com/paper/2111.06916/) (2021)
- [CONFLATOR: Incorporating Switching Point based Rotatory Positional Encodings for Code-Mixed Language Modeling](https://mlsanity.com/paper/2309.05270/) (2023)
- [Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes](https://mlsanity.com/paper/2309.06517/) (2023)
- [LLMsAgainstHate @ NLU of Devanagari Script Languages 2025: Hate Speech Detection and Target Identification in Devanagari Languages via Parameter Efficient Fine-Tuning of LLMs](https://mlsanity.com/paper/2412.17131/) (2024)
- [DeHate: A Stable Diffusion-based Multimodal Approach to Mitigate Hate Speech in Images](https://mlsanity.com/paper/2509.21787/) (2025)
- [A Survey on Online User Aggression: Content Detection and Behavioral Analysis on Social Media](https://mlsanity.com/paper/2311.09367/) (2023)

## Abstract (arXiv)

Contending hate speech in social media is one of the most challenging social problems of our time. There are various types of anti-social behavior in social media. Foremost of them is aggressive behavior, which is causing many social issues such as affecting the social lives and mental health of social media users. In this paper, we propose an end-to-end ensemble-based architecture to automatically identify and classify aggressive tweets. Tweets are classified into three categories - Covertly Aggressive, Overtly Aggressive, and Non-Aggressive. The proposed architecture is an ensemble of smaller subnetworks that are able to characterize the feature embeddings effectively. We demonstrate qualitatively that each of the smaller subnetworks is able to learn unique features. Our best model is an ensemble of Capsule Networks and results in a 65.2% F1 score on the Facebook test set, which results in a performance gain of 0.95% over the TRAC-2018 winners. The code and the model weights are publicly available at https://github.com/parthpatwa/Hater-O-Genius-Aggression-Classification-using-Capsule-Networks.