mlsanityData to 7 Oct 2026

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

arXiv 2408.00534 ↗First posted 1 Aug 2024cs.CY cs.CLcited by 3

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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.

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

Builds on

7 of 33 references.

References · 7

  1. 2022A New Generation of Perspective API: Efficient Multilingual Character-level Transformerscited by 181
  2. 2018Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typologycited by 35
  3. 2018An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterestcited by 10
  4. 2023Critical Perspectives: A Benchmark Revealing Pitfalls in PerspectiveAPIcited by 6
  5. 2018Endorsements on Social Media: An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterestcited by 4
  6. 2020"How over is it?" Understanding the Incel Community on YouTubecited by 4
  7. 2022Characterizing Alternative Monetization Strategies on YouTubecited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.