# A Survey on Online User Aggression: Content Detection and Behavioral Analysis on Social Media

arXiv: https://arxiv.org/abs/2311.09367 · HTML: https://mlsanity.com/paper/2311.09367/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 169 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Builds on (most cited first, 22 of 22)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://mlsanity.com/paper/1708.00524/) (2017)
- [Spread of hate speech in online social media](https://mlsanity.com/paper/1812.01693/) (2018)
- ["HOT" ChatGPT: The promise of ChatGPT in detecting and discriminating hateful, offensive, and toxic comments on social media](https://mlsanity.com/paper/2304.10619/) (2023)
- [Aggression-annotated Corpus of Hindi-English Code-mixed Data](https://mlsanity.com/paper/1803.09402/) (2018)
- [Like trainer, like bot? Inheritance of bias in algorithmic content moderation](https://mlsanity.com/paper/1707.01477/) (2017)
- [Developing a Multilingual Annotated Corpus of Misogyny and Aggression](https://mlsanity.com/paper/2003.07428/) (2020)
- [Mean Birds: Detecting Aggression and Bullying on Twitter](https://mlsanity.com/paper/1702.06877/) (2017)
- [Multilingual Offensive Language Identification for Low-resource Languages](https://mlsanity.com/paper/2105.05996/) (2021)
- [Detecting Cyberbullying and Cyberaggression in Social Media](https://mlsanity.com/paper/1907.08873/) (2019)
- [Hater-O-Genius Aggression Classification using Capsule Networks](https://mlsanity.com/paper/2105.11219/) (2021)
- [The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse](https://mlsanity.com/paper/2111.10390/) (2021)
- [RiTUAL-UH at TRAC 2018 Shared Task: Aggression Identification](https://mlsanity.com/paper/1807.11712/) (2018)
- [LSTMs with Attention for Aggression Detection](https://mlsanity.com/paper/1807.06151/) (2018)
- [A Unified System for Aggression Identification in English Code-Mixed and Uni-Lingual Texts](https://mlsanity.com/paper/2001.05493/) (2020)
- [On the Aggression Diffusion Modeling and Minimization in Online Social Networks](https://mlsanity.com/paper/2005.10646/) (2020)
- [Modeling Aggression Propagation on Social Media](https://mlsanity.com/paper/2002.10131/) (2020)
- [A Streaming Machine Learning Framework for Online Aggression Detection on Twitter](https://mlsanity.com/paper/2006.10104/) (2020)
- [minOffense: Inter-Agreement Hate Terms for Stable Rules, Concepts, Transitivities, and Lattices](https://mlsanity.com/paper/2305.17984/) (2023)

## Abstract (arXiv)

The rise of social media platforms has led to an increase in cyber-aggressive behavior, encompassing a broad spectrum of hostile behavior, including cyberbullying, online harassment, and the dissemination of offensive and hate speech. These behaviors have been associated with significant societal consequences, ranging from online anonymity to real-world outcomes such as depression, suicidal tendencies, and, in some instances, offline violence. Recognizing the societal risks associated with unchecked aggressive content, this paper delves into the field of Aggression Content Detection and Behavioral Analysis of Aggressive Users, aiming to bridge the gap between disparate studies. In this paper, we analyzed the diversity of definitions and proposed a unified cyber-aggression definition. We examine the comprehensive process of Aggression Content Detection, spanning from dataset creation, feature selection and extraction, and detection algorithm development. Further, we review studies on Behavioral Analysis of Aggression that explore the influencing factors, consequences, and patterns associated with cyber-aggressive behavior. This systematic literature review is a cross-examination of content detection and behavioral analysis in the realm of cyber-aggression. The integrated investigation reveals the effectiveness of incorporating sociological insights into computational techniques for preventing cyber-aggressive behavior. Finally, the paper concludes by identifying research gaps and encouraging further progress in the unified domain of socio-computational aggressive behavior analysis.