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Are Online Sports Fan Communities Becoming More Offensive? A Quantitative Review of Topics, Trends, and Toxicity of r/PremierLeague

Muhammad Zeeshan Mazhar, Tolga Buz, Yiran Su

TL;DR

This study analyzes over 1.1 million comments from Reddit's r/PremierLeague (2013–2022) to quantify topic dynamics, sentiment, and toxicity as the community grows, including a cross-year shift toward negative discourse and off-topic discussions. It integrates additional datasets (Premier League results and World Cup sentiment) and employs a multi-technique pipeline: sentiment analysis with BERTweet, topic modeling with BERTopic, and toxicity detection with RoBERTa, plus a match-result prediction task using a Random Forest. Key findings show rising negativity and toxicity, increasing focus on US-related topics and VAR, and limited predictive power of sentiment alone for match outcomes, with upvotes and certain toxic signals contributing more to predictions. The work highlights the health and growth of online sports communities, underscores potential societal concerns (racism, politics), and offers methodological insights for future multi-year socio-technical analyses of large fan communities.

Abstract

Online communities for sports fans have surged in popularity, with Reddit's r/PremierLeague emerging as a focal point for fans of one of the globe's most celebrated sports leagues. This boom has helped the Premier League make significant inroads into the US market, increasing viewership and sparking greater interest in its matches. Despite the league's broad appeal, there's still a notable gap in understanding its online fan community. Therefore, we analyzed a substantial dataset of over 1.1 million comments posted from 2013-2022 on r/PremierLeague. Our study delves into the sentiment, topics, and toxicity of these discussions, tracking trends over time, aiming to map out the conversation landscape. The rapid expansion has brought more diverse discussions, but also a worrying rise in negative sentiment and toxicity. Additionally, the subreddit has become a venue for users to voice frustrations about broader societal issues like racism, the COVID-19 pandemic, and political tensions.

Are Online Sports Fan Communities Becoming More Offensive? A Quantitative Review of Topics, Trends, and Toxicity of r/PremierLeague

TL;DR

This study analyzes over 1.1 million comments from Reddit's r/PremierLeague (2013–2022) to quantify topic dynamics, sentiment, and toxicity as the community grows, including a cross-year shift toward negative discourse and off-topic discussions. It integrates additional datasets (Premier League results and World Cup sentiment) and employs a multi-technique pipeline: sentiment analysis with BERTweet, topic modeling with BERTopic, and toxicity detection with RoBERTa, plus a match-result prediction task using a Random Forest. Key findings show rising negativity and toxicity, increasing focus on US-related topics and VAR, and limited predictive power of sentiment alone for match outcomes, with upvotes and certain toxic signals contributing more to predictions. The work highlights the health and growth of online sports communities, underscores potential societal concerns (racism, politics), and offers methodological insights for future multi-year socio-technical analyses of large fan communities.

Abstract

Online communities for sports fans have surged in popularity, with Reddit's r/PremierLeague emerging as a focal point for fans of one of the globe's most celebrated sports leagues. This boom has helped the Premier League make significant inroads into the US market, increasing viewership and sparking greater interest in its matches. Despite the league's broad appeal, there's still a notable gap in understanding its online fan community. Therefore, we analyzed a substantial dataset of over 1.1 million comments posted from 2013-2022 on r/PremierLeague. Our study delves into the sentiment, topics, and toxicity of these discussions, tracking trends over time, aiming to map out the conversation landscape. The rapid expansion has brought more diverse discussions, but also a worrying rise in negative sentiment and toxicity. Additionally, the subreddit has become a venue for users to voice frustrations about broader societal issues like racism, the COVID-19 pandemic, and political tensions.

Paper Structure

This paper contains 17 sections, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. r/PremierLeague Dataset
  5. Additional Datasets
  6. Analysis Techniques
  7. Sentiment Analysis
  8. Topic Modeling
  9. Toxicity Analysis
  10. Match Result Prediction
  11. Results
  12. General Results
  13. Sentiment Analysis
  14. Topic Modeling
  15. Toxicity Analysis
  16. ...and 2 more sections

Figures (5)

  • Figure 1: Most frequently chosen "Author flairs" as seen in comments data (users often use this flair to indicate their favorite team)
  • Figure 2: Most frequent submission flairs, indicating how often each team is posted about.
  • Figure 3: Distribution of sentiment classes across comments (left) and submissions (right)
  • Figure 4: Percentage of Sentiment by Year
  • Figure 5: Ratio of texts classified as offensive or hateful per year by the RoBERTa toxicity model