Catching Stray Balls: Football, fandom, and the impact on digital discourse (2025)

Abstract: This paper examines how emotional responses to football matches influence online discourse across digital spaces on Reddit. By analysing millions of posts from dozens of subreddits, it demonstrates that real-world events trigger sentiment shifts that move across communities. It shows that negative sentiment correlates with problematic language; match outcomes directly influence sentiment and posting habits; sentiment can transfer to unrelated communities; and offers insights into the content of this shifting discourse. These findings reveal how digital spaces function not as isolated environments, but as interconnected emotional ecosystems vulnerable to cross-domain contagion triggered by real-world events, contributing to our understanding of the propagation of online toxicity. While football is used as a case-study to computationally measure affective causes and movements, these patterns have implications for understanding online communities broadly.

## introduction football fans are often negatively presented in popular culture due to connotations of hooliganism and far-right allegiances @xcite . in response, football clubs (fcs), leagues, and fans themselves have moved to exclude those who exhibit these behaviours from matches, and instead foster tolerant atmospheres through initiatives like kick it out and the rainbow laces campaign. however, while stadiums have become more inclusive, toxicity remains online @xcite . it is essential to emphasise that problematic supporters -both online and offline -represent a small minority of fans, most of whom engage positively with the sport and its communities @xcite . nevertheless, the visibility and impact of this minority warrants attention. additionally, even among well-intentioned supporters, the tribal nature of sports can occasionally lead to conflict as personal identities become entwined with team allegiances and historic rivalries @xcite @xcite . this complex landscape, with multiple fan identities interacting, has prompted growing scholarly interest in online football communities @xcite @xcite . however, much of this research treats online spaces as isolated environments, failing to capture how football discourse can emerge and move across digital contexts. this paper takes a broader view by examining the affectional dynamics of football supporter communities and their movements across online spaces. specifically, by analysing millions of posts on reddit it, first, shows that negative sentiment correlates with problematic language; second, match outcomes influence posting habits and sentiment; third, sentiment can transfer to unrelated communities; and fourth, offers insights into the content of these discourses. in doing this it demonstrates a direct relationship between real-world events and online discourse, offering new insights into the propagation and dynamics of online content. ## related work in addition to the investigations into football fandom referenced above, the affective character of football on fans is well established both offline @xcite and online @xcite , with even clubs leveraging social media to foster emotional attachment @xcite . while the dominant emotion identified in studies of online fans is negativity, which has been shown to lead to disengagement @xcite and hostility @xcite , negativity is not uniformly corrosive -fans of underperforming teams may embrace suffering as part of loyalty @xcite . that is, negative emotions in foot-ball can be both alienating and identity-forming, making it a tricky concept to define from the fan's perspective. nonetheless, it is common within football communities, and this research looks at how these emotions may impact digital communities. it has been shown that users encountering hostile online discourse experience negative psychological consequences @xcite , while disengagement from toxic environments improves well-being @xcite . however, just as negativity from a fan's perspective is difficult to conceptualise, so too is framing online negativity which takes various, and often poorly defined, forms, including hate speech, trolling, and incivility @xcite @xcite . in this context "toxicity" has emerged as an umbrella term. hanscom et al. define toxicity as "interactions directed at an entity designed to be inflammatory" (2024), with recuero highlighting how it is enabled by platform affordances (2024). the latter point is of particular interest as we examine how one affordance -inter-subreddit mobilityfacilitates emotional spillover across online spaces and influences discourse @xcite . social media's networked nature, combined with platform design choices, amplify emotional content @xcite . studies have shown that those exposed to emotionally charged social media (both positive and negative) are themselves more likely to express similar sentiments online @xcite . additionally, negative content has been shown to spread more frequently and faster than positive content @xcite . this spread has been framed in various ways as "emotional contagion" @xcite , risking feedback loops that encourage and reinforce incivility @xcite . it is the emergence of, and exposure to, negativity, that this paper investigates. computational detection of negative and harmful discourse is extensively studied @xcite @xcite @xcite . however, as a subject there are significant challenges, including language ambiguity, contextual dependencies, and data biases @xcite @xcite complex computational and conceptual landscape, this approach foregrounds the multifaceted nature through which emotions emerge and are expressed online. that is, rather than proposing a method to identify harmful discourse, we aim to track it from a potential source. ## data and methods our analysis examines 62,384,329 reddit posts taken between july 2008 and august 2024 from 41 football club subreddits. foot_0 community sizes vary considerably, with a mean 1,521,569 and median 63,606 posts (appendix a). posts were aligned with historic match results (final scores) using kickoff times plus 120-minutes (allowing for half-time breaks and added time). this covers 20,764 unique games from the top four leagues in english football, both domestic cups, the three european cup competitions, and the community shield ( three subsets of potentially problematic posts were constructed representing hate speech (281,110 posts), obscene language (9,823,428), and toxicity (586,222) ( post type negative neutral positive fc corpus 36.0% 42.3% 21.7% paired corpus 37.8% 40.7% 21.5% paired fc 38.7% 38.5% 22.8% paired non-fc 36.9% 43.0% 20.2% @xcite and the ldnoobw (2025) dataset -and toxicitymodel (corrêa, 2023), a roberta-based toxicity detector. 4 with this data the paper employs a multi-layered analytical framework to systematically investigate the relationships between real-world football events, online sentiment patterns, and crosscommunity discourse. the approach progresses through four analytical phases: first, establishing the correlation between negative sentiment and problematic language. second, measuring relationships between match outcomes and sentiment within fc communities. third, identifying and correlating post sentiment across unrelated online spaces. fourth, quantify linguistic features through lexical matching and machine learning to provide insights into the content of the identified discourses. through this methodical progression, we establish not only statistical relationships but also temporal precedence, providing insights into the potential causal mechanisms underlying emotional spillover in digital spaces. ## sentiment and harmful discourse the relationship between negative sentiment and the potentially problematic posts was assessed by comparing each datasets' overall sentiment with the fc corpus. low sentiment scores correlate with problematic posts (figure 1 ). additionally, density plots show distributions between problematic posts and the fc corpus notably 4 only 10% of our corpus was tested with toxicitymodel due to limited computational resources. differ: all three are negatively skewed and strongly unimodal, while the main corpus is balanced and multimodal. the effect sizes are strong for the hate speech and toxic datasets (cliff's δ = -0.5, -0.47) and moderate for the obscene language dataset (δ = -0.3). the latter is visible in obscenity aligning slightly more closely with the fc corpus' positive sentiment, indicating -as one may expect -that obscene language is not universally negative. however, in all three cases, one is more likely to find negative posts in the problematic datasets than the fc corpus, suggesting sentiment may serve as a supplementary signal when detecting potentially problematic content. ## football results as a causal pathway to make meaningful claims about the movement of sentiment across digital spaces, one must investigate the sources of sentiment. without this, the direction of emotional contagion is difficult to assess. football provides an ideal case-study, as it offers clear time-stamped real-world events that can be linked to subsequent posting behaviours in related online spaces. this section, therefore, assess relationships between match outcomes and sentiment patterns using posts in fc subreddits. ## cross-community sentiment spread using a dataset of paired posts from club and nonclub subreddits (the latter being posts made within 10-minutes of the former) we investigated how sentiment categories (negative, neutral, and positive) across post pairs are correlated (kendall's τ ). 8 the analysis reveals a statistically significant -but weak -correlation between post sentiment by the same users in fc and non-fc subreddits ( following existing research showing that emotionally charged content spreads online more quickly @xcite , we also included these relationships with neutral posts made in fc subreddits removed. here correlations during matches strengthen further (τ = 0.118 to τ = 0.146), indicating emotionally charged users are more consistent in their emotional valence across community contexts, suggesting that heightened emotional states during matches may create stronger patterns of sentiment consistency in broader online behaviour. while the strength of the correlations remain modest in absolute terms, their relative changes and statistical significance across a large sample represent real patterns of user behaviour, and are evidence for emotional spillover. to further assess the association patterns between sentiment, a pearson χ²-test was used. ## post contents to understand what these paired posts linguistically represent we analysed their language features. to do this we identified posts containing profanity (taken from the ldnoobw), violent words, intensifiers, exclamation marks, and in all-caps. 9 these are taken to be indicators of negative and emotionally charged discourse. the table shows statistically significant correlations (positive τ ) between a user's posts' linguistic features in different subreddits. this suggests some consistency in individual communication styles. correlations are also stronger during matches for all features, suggesting that a user's language in fc contexts is more strongly predictive of their language in non-fc contexts while games are taking place. this strengthening of cross-subreddit linguistic patterns during matches indicates a potential causal relationship, where the heightened emotional states triggered by football events not only affect users' expressions within their particular football communities, but actively spills over to intensify their emotional communication elsewhere. we combined toxicitymodel's probability scores with tweetnlp's sentiment scores to identify and assess posts that were tagged as toxic and highly negative. on examination many (but not all) of these would be considered highly offensive. an illustrative and anonymised set of pairs is found in appendix b. these offer qualitative evidence that emotionally charged content crosses digital spaces, and that the negativity that spreads can be toxic, and thus potentially harmful. ## discussion and conclusion this analysis aimed to reveal a potential causal pathway for the dissemination of negativity across digital spaces. in doing this it showed that football match outcomes appear to influence fans' emotional states, which manifest in online sentiment patterns and posting behaviours that subsequently cross community boundaries. the analysis provides evidence for the relationship between football match events and fluctuations in online sentiment. by examining both aggregated patterns across thousands of matches and granular minute-by-minute reactions during specific games, we observed consistent temporal alignment between on-field events and sentiment shifts in online communities, with sentiment trajectories diverging predictably based on match outcomes and in-game moments. further, we identified an asymmetric relationship in posting habits. in the context of football club supporter communities, this manifested as heightened negative sentiment following losses, moderate negativity after draws, and mildly positive sentiment after wins -despite wins generating more posts on average. these findings support @xcite on the prominence of negative sentiment online, as well as @xcite research showing losses have greater emotional impact on fans than wins. more broadly, they align with established psychological principles of negativity bias in emotional processing, where negative experiences tend to have more profound impacts than positive ones @xcite . building on evidence of event-driven sentiment dynamics, we examined how these may trigger cross-community emotional spillover by analysing paired posts from the same users in club and nonclub subreddits, and showed statistically significant correlations between sentiment across these community boundaries. additionally, these correlations were stronger both during matches, and when the fc-related post was emotionally charged @xcite . finally, following our initial analysis showing a relationship between negativity and potentially problematic language, we examined linguistic features across paired posts. we found significant correlations between specific linguistic markers indicative of negative and emotionally charged discourse. additionally, these correlations were stronger during matches than at other times. this provides evidence that not only sentiment, but problematic language patterns, can transfer across communities with real-world events potentially triggering cascades of toxic discourse. these findings reveal how digital spaces function not as isolated environments, but are both directly impacted by external factors (real-world events) and are themselves interconnected emotional ecosystems vulnerable to cross-domain contagion -a phenomenon with significant implications for understanding the propagation of harmful speech online, and its existence beyond its originating contexts. while beyond the scope of this paper, these findings suggest several avenues for future research, including practical implications for platform moderation and design. first, the predictable nature of event-driven sentiment spillover could inform automated monitoring systems that increase vigilance or moderation thresholds within communities during high-risk periods (such as major sporting events or politically charged moments). second, the temporal patterns identified may provide a basis for predictive models that automatically flag users exhibiting negative sentiment in external communities for enhanced monitoring. more broadly, the demonstration that sentiment and linguistic toxicity markers transfer across seemingly unrelated digital spaces suggests future work explore whether limiting cross-community mobility during emotionally charged periods could reduce harmful spillover while preserving networked discourse benefits. in concluding, it must again be noted that football fandom has been used in this context not because it is uniquely toxic. on the contrary, analyses not included in this paper show that these subreddits are often remarkably open and welcoming spaces where personal and political topics are discussed in civil and polite manners @xcite . instead, football has been used as a case-study able to computationally measure affectional movements tied to known events. that is, football is one arena for discourse, and it is discourse itself that is central to this study. future work will apply the methodology developed here to analyse emotional spill-over in other domains to further examine the generalisability and implications for cross-community sentiment transfer.