Pith. sign in

REVIEW 4 major objections 6 minor 52 references

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

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Football match outcomes measurably shift Reddit sentiment, and that emotion spills into unrelated subreddits.

desk verdict A useful large-scale dataset and credible within-community sentiment findings, but the cross-community spillover claim is confounded by time-of-day and needs a matched baseline or softer language. read the letter →

arxiv 2506.01642 v2 pith:AJL25DYF submitted 2025-06-02 cs.SI cs.CY

Mark J. Hill This is my paper
classification cs.SIcs.CY
keywords footballfandomRedditsentimentanalysisemotionalcontagioncross-communityspilloveronlinetoxicitymatchoutcomespairedposts
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to show that real-world football matches change how fans write on Reddit, and that the emotional shift leaks into subreddits that have nothing to do with football. The author aligns 62 million posts from 41 club subreddits with 20,764 actual matches, measures each post's sentiment with a pretrained language model, and pairs posts by the same user made within ten minutes in a football and a non-football subreddit. The key result is that sentiment consistency across those paired posts roughly doubles during matches, with stronger coupling for emotionally charged and linguistically aggressive posts. If true, online communities are not self-contained: scheduled real-world events can push negative and sometimes toxic language across community boundaries, and moderation could be planned around event calendars.

What carries the argument

The load-bearing mechanism is the paired-post design: for each user among the top 1,000 posters in a club subreddit, the analysis takes a post made in a non-club subreddit within ten minutes of the club post, yielding 1,151,726 paired posts. Kendall's tau measures whether the sentiment of the two posts rank together; comparing during-match pairs with outside-match pairs isolates the football event's contribution. The sentiment scores themselves come from a normalised TweetNLP sentiment model, and problematic posts are identified through hate speech and profanity lexicons plus a toxicity detector, with linguistic features (profanity, violent words, intensifiers, exclamations, all-caps) scored across the paired posts.

What would settle it

Re-run the paired-post analysis with an outside-match baseline matched on hour-of-day, weekday, and subreddit activity; if the during-match correlation no longer exceeds the matched baseline, the cross-community spillover claim fails.

Watch

Extended reading notes

Core claim

On its own terms, the paper claims that football match outcomes are a causal pathway for sentiment shifts: losses and draws lower aggregate sentiment in club subreddits more than wins raise it, while wins raise posting volume but leave sentiment only mildly positive. It then claims that the same users carry this emotional state elsewhere, because the rank correlation between sentiment in a club post and the user's next post in a non-club subreddit rises from Kendall's tau of 0.059 outside matches to 0.118 during matches, and to 0.146 when neutral club posts are removed. Positive and negative sentiments are more likely to be matched across subreddits than chance, and linguistic markers such as profanity, violent words, intensifiers, exclamation marks and all-caps show stronger cross-community correlations during matches. The paper concludes that sentiment and problematic language transfer across digital spaces, with real-world events potentially triggering cascades of toxic discourse.

Load-bearing premise

The load-bearing premise is that a post made in a non-football subreddit within ten minutes of a club post reflects the same football-induced emotional state, rather than shared external factors such as time of day, weekday, or the platform's overall mood.

Editorial extensions

If this is right

  • Moderation systems could be tuned to expect elevated negative and toxic language in unrelated communities during and just after matches, particularly after losses and draws.
  • The strongest spillover comes from emotionally charged users, since removing neutral club posts raises the cross-community correlation from 0.118 to 0.146.
  • Negative outcomes drive the largest sentiment shifts even though wins generate more posts, so event-driven toxicity is better predicted by result valence than by posting volume.
  • The same event-aligned paired-post design can be applied to other domains with time-stamped real-world events, such as elections or major releases, to test whether emotional spillover is a general property of online discourse.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A user who posts in a football subreddit and then in an unrelated subreddit within ten minutes may already be in a consistent mood or browsing session, so the spillover effect may be partly a selection effect rather than purely match-induced; comparing against that same user's paired posts on non-match days with a similar temporal gap would separate these.
  • If the mechanism generalises, scheduled mass-emotional events such as election nights, awards ceremonies, or product launches should produce the same doubled correlation pattern in topic-adjacent and unrelated communities, which is directly testable with public data.
  • The paper's asymmetry result predicts that high-stakes losses, such as derbies or title deciders, should produce the largest tau increases and the most toxic spillover; a goal-time-level analysis would sharpen the causal timing beyond the 120-minute window.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The manuscript analyses 62,384,329 Reddit posts from 41 English football club subreddits, aligned to match kick-offs, to test whether match outcomes shape sentiment and posting behaviour and whether this sentiment spills over into non-club subreddits. It reports four findings: negative sentiment is overrepresented in posts flagged by hate-speech, obscenity, and toxicity lexicons; match losses and draws are followed by more negative sentiment while wins increase posting volume; Kendall's tau between same-user sentiment in club and non-club subreddits is higher during matches (0.118) than outside matches (0.059); and linguistic features such as profanity and exclamations correlate across contexts more strongly during matches. The paper concludes that real-world football events trigger emotional states that cross community boundaries, sometimes carrying toxic language into unrelated spaces.

Significance. If the cross-community spillover result holds, the paper would offer a valuable, large-scale demonstration of event-driven emotional contagion and toxicity transfer across online communities, with implications for platform moderation. The study has notable strengths: a very large corpus, publicly released analysis scripts, time-locked within-community analyses in Section 5 that are much less exposed to the confounding critique, and unusually candid limitations sections that acknowledge the collinearity between sentiment and toxicity and the context-specific pitfalls of lexicon-based detection. The cross-community claim, however, is currently supported by a non-matched comparison and by tables that include football-adjacent subreddits in the 'non-FC' set; these are load-bearing issues rather than cosmetic ones.

major comments (4)
  1. [Section 6, Tables 6-7] The central cross-community claim rests on a non-matched comparison between 'during match' and 'outside match' paired posts. Match kick-offs are concentrated in weekend afternoons/evenings and midweek evenings, whereas the outside-match baseline pools all other hours and days; sentiment consistency across a user's posts has known diurnal and weekly structure. The increase in Kendall's tau from 0.059 to 0.118 (and from 0.077 to 0.146 with neutral posts removed) could therefore reflect time-of-day or day-of-week effects rather than football-induced emotional transfer. This confound is not addressed in the Limitations section, which discusses contextual subreddit similarity but not temporal structure. I recommend a matched control sample (same hour-of-day and day-of-week, with comparable platform activity) or a within-user regression with time fixed effects, and reporting the difference with confidence intervals. The restriction to top-1,000 posters also leaves open whether the result generalizes beyond heavy users.
  2. [Section 8 / Section 6, Limitations] The abstract and Section 8 claim that sentiment transfers to 'unrelated communities,' but the non-FC set in Section 6 includes r/soccer and r/FantasyPL, which are football-adjacent. The Limitations section reports that the relationship remains with these subreddits removed but with a diminished tau; this is not shown in the main tables. Please make the excluded-subreddits analysis the primary specification or modify the 'unrelated' framing, because the headline claim as stated is not supported by the tables as presented.
  3. [Section 7, Table 9] The linguistic-feature analysis inherits the same temporal confound as Section 6. In addition, the raw differences (Delta tau between 0.022 and 0.056) are very small, no confidence intervals are reported, and no multiple-comparison correction is applied across five features and two conditions; with sample sizes in the hundreds of thousands, statistical significance at p<0.001 does not establish a meaningful or football-specific effect. Report effect sizes with uncertainty and preferably a pre-specified analysis of the linguistic features.
  4. [Abstract and Section 8] The causal language in the abstract ('real-world events trigger sentiment shifts') and Section 8 ('subsequently cross community boundaries') is stronger than the evidence. Section 5 itself acknowledges that causation cannot be definitively established from observational data. Given the confound in Section 6, the cross-community component should be framed as an associational finding unless the matched analysis is added.
minor comments (6)
  1. [Footnote 6] 'ANOV A' should be 'ANOVA'.
  2. [Section 6, Tables 6-7] The text should report confidence intervals or a bootstrap distribution for the Kendall's tau values and their difference; the phrase 'correlation doubles' overstates precision.
  3. [Section 4 / Limitations] The Limitations section's collinearity caveat about sentiment and toxicity should appear where the Section 4 correlation is first introduced, not only at the end, since it directly qualifies the first contribution.
  4. [Figure 4] The individual gray data points are difficult to read in a static figure; consider alpha blending or contour density to preserve the qualitative message.
  5. [References] The reference list contains several OCR-like artifacts (e.g., 'V ohl' for Vohs, 'F antasy' for Fantasy); please clean the bibliography.
  6. [Section 6, final paragraph] 'These two tests in tandem demonstrate' overstates the evidence: Kendall's tau and the chi-square standardized residuals are two views of the same pairwise association, so the sentence should not imply independent confirmation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central analyses compare external event data and external NLP outputs, the only self-citation is a non-load-bearing side remark, and the acknowledged sentiment/toxicity overlap is disclosed as a measurement limitation rather than a prediction derived from its inputs.

full rationale

The paper's derivation chain is not circular. Section 5's match-outcome sentiment and posting analyses use externally timestamped match results with TweetNLP sentiment scores applied to FC subreddit posts; the link is empirical, not built into either input. Section 6's cross-community spillover analysis uses same-user paired posts within a 10-minute window and compares Kendall's tau during versus outside matches; the sentiment model is an external classifier applied independently to both posts, so the during-match increase is not definitionally forced. The only self-citation (Hill and Zaborowski 2024) appears in the conclusion as a side remark that these subreddits are often welcoming; it is not load-bearing for the main results. The paper itself flags the one measurement-overlap concern: in the Limitations section, 'There is also almost certainly collinearity between potentially problematic language and negative sentiment. That is, the same lexical features (profanity, hostile language, slurs) that are found in lexicons or trigger toxicity detection are also likely to influence sentiment scores. This overlap means the correlation between negative sentiment and problematic content may be partly methodological.' This is an acknowledged construct-overlap confound, not a derived result passed off as a prediction, and it does not propagate to the match-aligned or cross-community claims. The skeptic's time-of-day matching concern about Tables 6-7 is a validity threat, not circularity, because it questions whether the comparison groups differ on confounders; it does not show that the effect is an input by construction.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central result is empirical and does not depend on fitted constants. The main burdens are measurement assumptions: sentiment scores are treated as direct estimates of emotional state, paired posts within 10 minutes are treated as evidence of spillover, and the final match result is used as the label for the entire 120-minute window. The author candidly acknowledges several of these in the Limitations section.

free parameters (4)
  • Paired-post time window = 10 minutes
    Chosen by hand to define cross-community pairs; no sensitivity analysis is reported, and the spillover estimate may depend on this threshold.
  • Match window for post alignment = 120 minutes
    Posts within 120 minutes of kick-off are labeled with the final result even though the live score differs; the author notes this can weaken correlations.
  • Top-poster threshold = 1,000
    The paired corpus includes only users who are top-1,000 posters in a club subreddit, overrepresenting heavy users.
  • ToxicityModel sample fraction = 10% of corpus
    Only a tenth of posts were scored with ToxicityModel due to compute limits; the sampling method is not described.
assumptions (6)
  • domain assumption Reddit post text is a valid measure of user emotional state.
    The entire pipeline scores posts for sentiment and treats distributions as emotional states of fans; bots, sarcasm, in-group humor, and performative identity posts are not filtered out. Invoked throughout Section 3 and Section 5.
  • domain assumption TweetNLP sentiment scores are comparable across club and non-club subreddits.
    The model is applied to all subreddits with the same normalization, but no validation is reported on football-specific jargon or cross-community language shift. Invoked in Section 3.
  • domain assumption Same-user posts within 10 minutes capture emotional spillover rather than stable individual style or shared time-of-day mood.
    The causal interpretation of Tables 6-7 requires the non-FC post to be influenced by the match-induced state; the outside-match correlation (tau=0.059) shows a baseline that may already include these confounds. Invoked in Section 6.
  • domain assumption Match final outcome is the correct label for all posts in the 120-minute window.
    The live score differs from the final score during most of the window, but posts are aligned with the final result; the author acknowledges this mismatch in Limitations and says it likely weakens correlations.
  • standard math Statistical significance tests assume independent observations.
    ANOVA, chi-square, and negative binomial models are applied to posts clustered by user and subreddit; no clustered standard errors or mixed-effects controls are reported, so p-values are likely anti-conservative. Invoked in Section 5.2.
  • domain assumption Football match outcomes are exogenous to Reddit posting behavior.
    The causal pathway argument needs match results to be independent of online discourse; plausible because matches are scheduled real-world events, but no test or discussion of reverse causality is provided. Invoked in Section 5.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Catching Stray Balls: Football, fandom, and the impact on digital discourse." pith.science (2026). https://pith.science/paper/AJL25DYF

@misc{pith2026250601642,
  author       = {Pith},
  title        = {Pith review of: Catching Stray Balls: Football, fandom, and the impact on digital discourse},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AJL25DYF}},
  note         = {Machine review of arXiv:2506.01642}
}
read the original 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.

Figures

Figures reproduced from arXiv: 2506.01642 by the authors.

Figure 1
Figure 1. Sentiment score distributions comparing FC [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Change in poster sentiment over 48-hour pe [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Aggregated sentiment change per-minute by [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Post sentiment during Arsenal-Bournemouth [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Post count (log) and sentiment relative to [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

52 extracted references · 29 canonical work pages

  1. [1]

    Hunt Allcott, Luca Braghieri, Sarah Eichmeyer, and Matthew Gentzkow. 2020. https://doi.org/10.1257/aer.20190658 The Welfare Effects of Social Media . American Economic Review, 110(3):629--676

    doi:10.1257/aer.20190658 2020

  2. [2]

    Anderson and Heidi E

    Ashley A. Anderson and Heidi E. Huntington. 2017. https://doi.org/10.1177/1075547017735113 Social Media , Science , and Attack Discourse : How Twitter Discussions of Climate Change Use Sarcasm and Incivility . Science Communication, 39(5):598--620

    work page doi:10.1177/1075547017735113 2017

  3. [3]

    Angelo Antoci, Alexia Delfino, Fabio Paglieri, Fabrizio Panebianco, and Fabio Sabatini. 2016. https://doi.org/10.1371/journal.pone.0164286 Civility vs. Incivility in Online Social Interactions : An Evolutionary Approach . PLOS ONE, 11(11):e0164286

    work page doi:10.1371/journal.pone.0164286 2016

  4. [4]

    Imran Awan and Irene Zempi, editors. 2023. https://doi.org/10.46692/9781529227208 Hate Crime in Football . Bristol University Press

    work page doi:10.46692/9781529227208 2023

  5. [5]

    Femi Emmanuel Ayo, Olusegun Folorunso, Friday Thomas Ibharalu, and Idowu Ademola Osinuga. 2020. https://doi.org/10.1016/j.cosrev.2020.100311 Machine learning techniques for hate speech classification of twitter data: State -of-the-art, future challenges and research directions . Computer Science Review, 38:100311

    arXiv 2020

  6. [6]

    Baumeister, Ellen Bratslavsky, Catrin Finkenauer, and Kathleen D

    Roy F. Baumeister, Ellen Bratslavsky, Catrin Finkenauer, and Kathleen D. Vohs. 2001. https://doi.org/10.1037/1089-2680.5.4.323 Bad is stronger than good . Review of General Psychology, 5(4):323--370

    doi:10.1037/1089-2680.5.4.323 2001

  7. [7]

    Brady, Julian A

    William J. Brady, Julian A. Wills, John T. Jost, Joshua A. Tucker, and Jay J. Van Bavel. 2017. https://doi.org/10.1073/pnas.1618923114 Emotion shapes the diffusion of moralized content in social networks . Proceedings of the National Academy of Sciences, 114(28):7313--7318

    doi:10.1073/pnas.1618923114 2017

  8. [8]

    Luca Braghieri, Ro'ee Levy, and Alexey Makarin. 2022. https://doi.org/10.1257/aer.20211218 Social Media and Mental Health . American Economic Review, 112(11):3660--3693

    doi:10.1257/aer.20211218 2022

Show all 52 references
  1. [9]

    Jose Camacho-collados, Kiamehr Rezaee, Talayeh Riahi, Asahi Ushio, Daniel Loureiro, Dimosthenis Antypas, Joanne Boisson, Luis Espinosa Anke, Fangyu Liu, and Eugenio Martínez Cámara. 2022. https://doi.org/10.18653/v1/2022.emnlp-demos.5 TweetNLP : Cutting - Edge Natural Language...

    2022 doi

  2. [10]

    Jamie Cleland, Mark Doidge, Peter Millward, and Paul Widdop. 2018. https://doi.org/10.1007/978-3-319-73141-4 Collective Action and Football Fandom . Springer International Publishing, Cham

    2018 doi

  3. [11]

    Nicholas Kluge Corrêa. 2023. https://doi.org/10.5281/zenodo.6989727 Aira

    2023 doi

  4. [12]

    Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017. https://doi.org/10.1609/icwsm.v11i1.14955 Automated Hate Speech Detection and the Problem of Offensive Language . Proceedings of the International AAAI Conference on Web and Social Media, 11(1):512--515

    2017 doi

  5. [13]

    Alex Fenton, Wasim Ahmed, Mariann (Maz) Hardey, Rosy Boardman, and Emma Kavanagh. 2024. https://doi.org/10.1080/16184742.2023.2270566 Women’s football subculture of misogyny: the escalation to online gender-based violence . European Sport Management Quarterly, 24(6):1215--1237

    2024

  6. [14]

    Emilio Ferrara and Zeyao Yang. 2015. https://doi.org/10.1371/journal.pone.0142390 Measuring Emotional Contagion in Social Media . PLOS ONE, 10(11):e0142390

    2015 doi

  7. [15]

    Amit Goldenberg and James J. Gross. 2020. https://doi.org/10.1016/j.tics.2020.01.009 Digital Emotion Contagion . Trends in Cognitive Sciences, 24(4):316--328

    2020 doi

  8. [16]

    Rhett Hanscom, Tamara Silbergleit Lehman, Qin Lv, and Shivakant Mishra. 2024. https://doi.org/10.48550/arXiv.2410.21589 The Toxicity Phenomenon Across Social Media . arXiv preprint. ArXiv:2410.21589 [cs] version: 1

    2024 doi arXiv

  9. [17]

    Hill and Rafal Zaborowski

    Mark J. Hill and Rafal Zaborowski. 2024. Mass events and collective sentiment on digital platforms: Football's impact on online fan discourse. In International Conference on Social Media & Society, London, UK. Social Media Lab, Toronto Metropolitan University and Digital Cultu...

    2024

  10. [18]

    Md Saroar Jahan and Mourad Oussalah. 2023. https://doi.org/10.1016/j.neucom.2023.126232 A systematic review of hate speech automatic detection using natural language processing . Neurocomputing, 546:126232

    2023

  11. [19]

    I don’t watch that anymore

    Martin Kaden, Christian Brandt, S. B. Bauers, Jonathan Bunar, and Gregor Hovemann. 2023. https://doi.org/10.3389/fpsyg.2023.1289436 “ I don’t watch that anymore”: an analysis of online comments by German soccer fans on their relationship to their favored clubs . Frontiers in P...

    2023

  12. [20]

    Ashifa Kassam. 2024. https://www.theguardian.com/football/2024/oct/24/four-men-arrested-over-online-racist-hate-campaign-against-vinicius-junior Four men arrested over online racist hate campaign against Vinícius Júnior . The Guardian

    2024

  13. [21]

    M. G. Kendall. 1938. https://doi.org/10.1093/biomet/30.1-2.81 A new measure of rank correlation . Biometrika, 30(1-2):81--93

    1938 doi

  14. [22]

    Kerr, George V

    John H. Kerr, George V. Wilson, Isamu Nakamura, and Yoshiko Sudo. 2005. https://doi.org/10.1016/j.paid.2004.10.002 Emotional dynamics of soccer fans at winning and losing games . Personality and Individual Differences, 38(8):1855--1866

    2005 doi

  15. [23]

    Adam D. I. Kramer, Jamie E. Guillory, and Jeffrey T. Hancock. 2014. https://doi.org/10.1073/pnas.1320040111 Experimental evidence of massive-scale emotional contagion through social networks . Proceedings of the National Academy of Sciences of the United States of America, 111...

    2014 doi

  16. [24]

    LDNOOBW . 2025. https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words List of dirty, naughty, obscene, and otherwise bad words . GitHub repository

    2025

  17. [25]

    Tran, Yi Tay, Jeffrey Sorensen, Jai Gupta, Donald Metzler, and Lucy Vasserman

    Alyssa Lees, Vinh Q. Tran, Yi Tay, Jeffrey Sorensen, Jai Gupta, Donald Metzler, and Lucy Vasserman. 2022. https://doi.org/10.48550/arXiv.2202.11176 A New Generation of Perspective API : Efficient Multilingual Character -level Transformers . arXiv preprint. ArXiv:2202.11176 [cs]

    2022 doi arXiv

  18. [26]

    London Assembly . 2023. https://www.london.gov.uk/who-we-are/what-london-assembly-does/london-assembly-members/unmesh-desai/footballhatecrime2023 Police record almost 100 football-related hate crimes in a year

    2023

  19. [27]

    Tiago Marques, Helena Nobre, and David Gordon. 2018. https://doi.org/10.1504/IJDCET.2018.10010163 Sports fan relationships with soccer teams . International Journal of Digital Culture and Electronic Tourism, 2

    2018

  20. [28]

    Ariadna Matamoros-Fernández and Johan Farkas. 2021. https://doi.org/10.1177/1527476420982230 Racism, Hate Speech , and Social Media : A Systematic Review and Critique . Television & New Media, 22(2):205--224

    2021 doi

  21. [29]

    Smitha Milli, Micah Carroll, Yike Wang, Sashrika Pandey, Sebastian Zhao, and Anca D Dragan. 2025. https://doi.org/10.1093/pnasnexus/pgaf062 Engagement, user satisfaction, and the amplification of divisive content on social media . PNAS Nexus, 4(3):pgaf062

    2025 doi

  22. [30]

    Sandra Miranda, C \'e lia Gouveia, Branco Di F \'a tima, and Ana Cristina Antunes. 2024. https://doi.org/10.1080/14660970.2023.2230452 Hate speech on social media: behaviour of Portuguese football fans on Facebook . Soccer & Society, 25(1):76--91

    2024

  23. [31]

    Jessica Murray. 2021. https://www.theguardian.com/uk-news/2021/feb/15/uk-first-football-hate-officer-social-media-racist-abuse-stuart-ward UK 's first football hate crime officer turns focus on social media . The Guardian

    2021

  24. [32]

    Newson, Michael D

    M. Newson, Michael D. Buhrmester, and H. Whitehouse. 2021. https://doi.org/10.1080/23750472.2020.1866650 United in defeat: shared suffering and group bonding among football fans . Managing Sport and Leisure, 28:164--181

    2021

  25. [33]

    Daniel Nuttall. 2018. https://www.uclan.ac.uk/research/index.php Online Fandom : exploring community and identity formation within football fan forums . doctoral, University of Central Lancashire

    2018

  26. [34]

    John Obinna Onwe. 2016. Involuntary emotions: Exploring the experiences of winning and losing on sport fans. International Journal of Sport Studies, 6:136--146

    2016

  27. [35]

    Zizi Papacharissi. 2014. https://doi.org/10.1093/acprof:oso/9780199999736.001.0001 Affective Publics : Sentiment , Technology , and Politics . Oxford University Press

    2014

  28. [36]

    Keith D. Parry. 2012. https://doi.org/10.1108/14439881211248374 Game of two passions: a football fan's autoethnography . Qualitative Research Journal, 12(2):238--250

    2012 doi

  29. [37]

    John Pavlopoulos, Jeffrey Sorensen, Lucas Dixon, Nithum Thain, and Ion Androutsopoulos. 2020. https://doi.org/10.48550/arXiv.2006.00998 Toxicity Detection : Does Context Really Matter ? arXiv preprint. ArXiv:2006.00998 [cs]

    2020 doi arXiv

  30. [38]

    Amir Ben Porat. 2010. https://doi.org/10.1080/14660971003619594 Football fandom: A bounded identification . Soccer and Society, 11(3):277--290

    2010 doi

  31. [39]

    Raquel Recuero. 2024. https://doi.org/10.1177/20563051231224264 The Platformization of Violence : Toward a Concept of Discursive Toxicity on Social Media . Social Media + Society, 10(1):20563051231224264

    2024 doi

  32. [40]

    David Rowe, Andy Ruddock, and Brett Hutchins. 2010. https://doi.org/10.1177/1354856510367622 Cultures of Complaint : Online Fan Message Boards and Networked Digital Media Sport Communities . Convergence, 16(3):298--315

    2010 doi

  33. [41]

    Dillon, Susan Benesch, and Derek Ruths

    Haji Mohammad Saleem, Kelly P. Dillon, Susan Benesch, and Derek Ruths. 2017. https://doi.org/10.48550/arXiv.1709.10159 A Web of Hate : Tackling Hateful Speech in Online Social Spaces . arXiv preprint. ArXiv:1709.10159 [cs]

    2017 doi arXiv

  34. [42]

    Cornel Sandvoss. 2004. https://doi.org/10.4324/9780203561393 A Game of Two Halves : Football Fandom , Television and Globalisation . Routledge, London

    2004 doi

  35. [43]

    Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, and Noah A. Smith. 2019. https://doi.org/10.18653/v1/P19-1163 The Risk of Racial Bias in Hate Speech Detection . In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages 1668--1678,...

    2019 doi

  36. [44]

    Anna Schmidt and Michael Wiegand. 2017. https://doi.org/10.18653/v1/W17-1101 A Survey on Hate Speech Detection using Natural Language Processing . In Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media , pages 1--10, Valencia, Spain....

    2017 doi

  37. [45]

    Javier Torregrosa, Gema Bello-Orgaz, Eugenio Martínez-Cámara, Javier Del Ser, and David Camacho. 2023. https://doi.org/10.1007/s12652-021-03658-z A survey on extremism analysis using natural language processing: definitions, literature review, trends and challenges . Journal o...

    2023 doi

  38. [46]

    Sho Tsugawa and Hiroyuki Ohsaki. 2015. https://doi.org/10.1145/2817946.2817962 Negative Messages Spread Rapidly and Widely on Social Media . In Proceedings of the 2015 ACM on Conference on Online Social Networks , COSN '15, pages 151--160, New York, NY, USA. Association for Co...

    2015

  39. [47]

    Bertie Vidgen and Leon Derczynski. 2020. https://doi.org/10.1371/journal.pone.0243300 Directions in abusive language training data, a systematic review: Garbage in, garbage out . PLOS ONE, 15(12):e0243300

    2020 doi

  40. [48]

    Yucheng Wang and Zhicong Lu. 2023. https://doi.org/10.1145/3544548.3581310 Making Sense of Post -match Fan Behaviors in the Online Football Communities . In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems , CHI '23, pages 1--17, New York, NY, USA. ...

    2023

  41. [49]

    James Woods and Jan Andre Lee Ludvigsen. 2022. https://doi.org/10.1080/17430437.2021.1904902 The changing faces of fandom? exploring emerging ‘online’ and ‘offline’ fandom spaces in the english premier league . Sport in Society, 25(11):2234--2249

    2022

  42. [50]

    Ellery Wulczyn, Nithum Thain, and Lucas Dixon. 2017. https://doi.org/10.48550/arXiv.1610.08914 Ex Machina : Personal Attacks Seen at Scale . arXiv preprint. ArXiv:1610.08914 [cs]

    2017 doi arXiv

  43. [51]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...

  44. [52]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.