# The Monetisation of Toxicity: Analysing YouTube Content Creators and Controversy-Driven Engagement

arXiv: https://arxiv.org/abs/2408.00534 · HTML: https://mlsanity.com/paper/2408.00534/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [A New Generation of Perspective API: Efficient Multilingual Character-level Transformers](https://mlsanity.com/paper/2202.11176/) (2022)
- [Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology](https://mlsanity.com/paper/1809.03006/) (2018)
- [An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterest](https://mlsanity.com/paper/1803.08488/) (2018)
- [Critical Perspectives: A Benchmark Revealing Pitfalls in PerspectiveAPI](https://mlsanity.com/paper/2301.01874/) (2023)
- [Endorsements on Social Media: An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterest](https://mlsanity.com/paper/1809.00620/) (2018)
- ["How over is it?" Understanding the Incel Community on YouTube](https://mlsanity.com/paper/2001.08293/) (2020)
- [Characterizing Alternative Monetization Strategies on YouTube](https://mlsanity.com/paper/2203.10143/) (2022)

## Built on by (most cited first, 3 of 3)

- [Computational Studies in Influencer Marketing: A Systematic Literature Review](https://mlsanity.com/paper/2506.14602/) (2025)
- [CPCLDETECTOR: Knowledge Enhancement and Alignment Selection for Chinese Patronizing and Condescending Language Detection](https://mlsanity.com/paper/2509.18562/) (2025)
- [Turning Trust to Transactions: Tracking Affiliate Marketing and FTC Compliance in YouTube's Influencer Economy](https://mlsanity.com/paper/2603.04383/) (2026)

## Abstract (arXiv)

YouTube is a major social media platform that plays a significant role in digital culture, with content creators at its core. These creators often engage in controversial behaviour to drive engagement, which can foster toxicity. This paper presents a quantitative analysis of controversial content on YouTube, focusing on the relationship between controversy, toxicity, and monetisation. We introduce a curated dataset comprising 20 controversial YouTube channels extracted from Reddit discussions, including 16,349 videos and more than 105 million comments. We identify and categorise monetisation cues from video descriptions into various models, including affiliate marketing and direct selling, using lists of URLs and keywords. Additionally, we train a machine learning model to measure the toxicity of comments in these videos. Our findings reveal that while toxic comments correlate with higher engagement, they negatively impact monetisation, indicating that controversy-driven interaction does not necessarily lead to financial gain. We also observed significant variation in monetisation strategies, with some creators showing extensive monetisation despite high toxicity levels. Our study introduces a curated dataset, lists of URLs and keywords to categorise monetisation, a machine learning model to measure toxicity, and is a significant step towards understanding the complex relationship between controversy, engagement, and monetisation on YouTube. The lists used for detecting and categorising monetisation cues are available on https://github.com/thalesbertaglia/toxmon.