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REVIEW 2 major objections 2 minor 82 references

"bot lane noob" Towards Deployment of NLP-based Toxicity Detectors in Video Games

T0 review · 2 major / 2 minor · reviewed 2026-05-10 · grok-4.3

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Pith's one-line read A dataset of messages labeled by expert League of Legends players produces a toxicity detector that outperforms general-purpose NLP tools.

desk verdict The paper releases a new dataset of live League of Legends chat messages labeled by eight expert players and shows a detector trained on it beats general toxicity tools, but the labeling process has thin validation. read the letter →

arxiv 2604.10175 v1 submitted 2026-04-11 cs.CR cs.CYcs.LG

Jonas Ave , Irdin Pekaric , Matthias Frohner , Giovanni Apruzzese This is my paper · ORCID
classification cs.CRcs.CYcs.LG
keywords toxicitydetectionvideogamesLeagueofLegendsnaturallanguageprocessingharassmentdatasetsmachinelearningbrowserextension
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

The paper seeks to fill the gap in tools for detecting toxic messages sent during live video game matches, where most prior work has focused on broader social media rather than in-game chat. A literature review of over one thousand works confirms that few efforts have built and tested NLP-based detectors specifically for match-time harassment. The authors address this by recruiting eight expert players to label more than fifteen thousand League of Legends messages, creating the L2DTnH dataset of toxic and non-toxic examples. They train a detector on this data and show through experiments that it exceeds the performance of both general-purpose toxicity detectors and state-of-the-art NLP models on game content. They also test the detector on additional game-related texts, build a browser extension that flags toxic text locally without external servers, and release all resources for others to use.

What carries the argument

The L2DTnH dataset of expert-labeled in-game chat messages from League of Legends, used to train a specialized NLP toxicity detector that is then compared against general models.

What would settle it

Independent expert labeling of a new set of recent League of Legends match messages shows the detector performing no better than general-purpose NLP toxicity detectors.

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Extended reading notes

Core claim

Through a systematic review of 1,039 works, the paper finds limited prior efforts on ML/NLP detectors for toxicity in live matches. It introduces L2DTnH, a dataset of 1.4k toxic and 13.8k non-toxic messages labeled by eight expert LoL players. A detector trained on L2DTnH is shown to outperform general-purpose and state-of-the-art NLP toxicity detectors. The approach is validated on additional game-related data, and a web browser extension is developed to flag toxic content locally without third-party AI servers. All resources are released publicly to support further applied research against toxicity in video games.

Load-bearing premise

The labels assigned by eight expert players accurately capture what counts as toxicity in live matches and the detector generalizes to new game data without overfitting.

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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The paper conducts a systematic literature review (n=1,039) showing scarcity of NLP/ML-based toxicity detectors for live video-game matches. It introduces the L2DTnH dataset of 15.2k League of Legends chat messages (1.4k toxic) labeled by 8 expert players, trains an NLP detector claimed to outperform general-purpose and SOTA toxicity detectors, validates it on external game data, releases a local browser extension for flagging toxic content, and makes all resources public.

Significance. If the outperformance and generalization claims are supported by reliable labels and transparent evaluation, the work supplies a domain-specific dataset and practical deployment artifact for toxicity mitigation during active matches, directly addressing the gap identified in the literature review. The public release of the dataset, detector, and serverless extension is a clear strength enabling reproducibility.

major comments (2)
  1. [Dataset construction] Dataset construction section: the labeling procedure by the 8 expert players is described only at high level. No annotation protocol, definition of toxicity, independence of labels, majority-vote rule, or inter-annotator agreement statistic (Cohen/Fleiss kappa) is reported. Because the central outperformance claim rests on the quality of these 1.4k toxic labels, missing reliability metrics constitute a load-bearing gap.
  2. [Empirical results] Empirical results section: the abstract asserts that the L2DTnH-trained detector outperforms general-purpose and SOTA NLP toxicity detectors, yet no concrete metrics (precision, recall, F1, AUC), baseline models, train/test split details, or cross-validation procedure are supplied in the provided text. With only 1.4k positive examples, these omissions prevent assessment of whether the reported gains are robust or artifactual.
minor comments (2)
  1. [Title] The title's use of quotation marks around 'bot lane noob' is stylistically unclear and does not immediately convey the paper's focus on deployable detectors.
  2. [Literature review] The literature-review methodology would benefit from explicit search strings, databases queried, and inclusion/exclusion criteria to substantiate the n=1,039 count.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful and constructive review of our manuscript. The comments identify important gaps in transparency that we have addressed through targeted revisions. We respond to each major comment below.

read point-by-point responses

  1. Referee: [Dataset construction] Dataset construction section: the labeling procedure by the 8 expert players is described only at high level. No annotation protocol, definition of toxicity, independence of labels, majority-vote rule, or inter-annotator agreement statistic (Cohen/Fleiss kappa) is reported. Because the central outperformance claim rests on the quality of these 1.4k toxic labels, missing reliability metrics constitute a load-bearing gap.

    Authors: We agree that the original Dataset construction section provided only a high-level description and omitted key details on the labeling process. In the revised manuscript we have expanded this section to include: the complete annotation protocol followed by the eight expert players, the operational definition of toxicity used during labeling, confirmation that annotations were performed independently, the majority-vote aggregation rule applied to produce final labels, and the inter-annotator agreement statistic (Fleiss' kappa). These additions directly improve transparency and allow readers to assess the reliability of the 1.4k toxic labels that underpin our detector. revision: yes

  2. Referee: [Empirical results] Empirical results section: the abstract asserts that the L2DTnH-trained detector outperforms general-purpose and SOTA NLP toxicity detectors, yet no concrete metrics (precision, recall, F1, AUC), baseline models, train/test split details, or cross-validation procedure are supplied in the provided text. With only 1.4k positive examples, these omissions prevent assessment of whether the reported gains are robust or artifactual.

    Authors: We acknowledge that the Empirical results section lacked the concrete methodological details needed for proper evaluation. In the revised version we have added: the full set of performance metrics (precision, recall, F1, and AUC) for both our detector and all baselines, the identities of the baseline models (general-purpose and SOTA toxicity detectors), the train/test split procedure, and the cross-validation protocol employed. We also describe how class imbalance was handled. These changes enable readers to assess the robustness of the reported outperformance given the limited number of positive examples. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical dataset creation and supervised learning pipeline

full rationale

The paper contains no equations, derivations, or first-principles predictions. Its core contribution is the collection of a new labeled dataset (L2DTnH) via expert annotation followed by standard supervised training of an NLP toxicity classifier, with empirical comparisons to external baselines. No step reduces a claimed result to its own inputs by construction, self-definition, or load-bearing self-citation. The work is self-contained against external benchmarks and does not invoke uniqueness theorems, ansatzes, or renamings that would trigger circularity patterns.

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

Central claim depends on the representativeness of the expert-labeled dataset and the validity of comparisons to external toxicity detectors; no free parameters, axioms, or invented entities are explicitly introduced in the abstract.

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Cite this review

Pith. "Pith review of "bot lane noob" Towards Deployment of NLP-based Toxicity Detectors in Video Games." pith.science (2026). https://pith.science/paper/2604.10175

@misc{pith2026260410175,
  author       = {Pith},
  title        = {Pith review of: "bot lane noob" Towards Deployment of NLP-based Toxicity Detectors in Video Games},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2604.10175}},
  note         = {Machine review of arXiv:2604.10175}
}
read the original abstract

Toxicity and harassment are widespread in the video-gaming context. Especially in competitive online multiplayer scenarios, gamers oftentimes send harmful messages to other players (teammates or opponents) whose consequences span from mild annoyance to withdrawal and depression. Abundant prior work tackled these problems, e.g., pointing out the negative effects of toxic interactions. However, few works proposed countermeasures specifically developed and tested on textual messages sent during a match -- i.e., when the "harassment" actually occurs. We posit that such a scarcity stems from the lack of high-quality datasets that can be used to devise "automated" detectors based on natural-language processing (NLP) and machine learning (ML), and which can -- ideally -- mitigate the harm of toxic comments during a gaming session. This work provides a foundation for addressing the problem of toxicity and harassment in video games. First, through a systematic literature review (n=1,039), we provide evidence that only few works proposed ML/NLP-based detectors of toxicity/harassment during live matches. Then, we partner-up with 8 expert League of Legend (LoL) players and create a fine-grained labelled dataset, L2DTnH, containing 1.4k toxic and 13.8k non-toxic messages exchanged during LoL matches. We use L2DTnH to develop a detector that we then empirically show outperforms general-purpose and state-of-the-art toxicity detectors reliant on NLP. To further demonstrate the practicality of our resources, we test our detector on game-related data beyond that included in L2DTnH; and we develop a Web-browser extension that flags toxic content in Webpages -- without querying third-party servers owned by AI companies. We publicly release all of our resources. Our contributions pave the way for more applied research devoted to fighting the spread of toxicity and harassment in video games.

Figures

Figures reproduced from arXiv: 2604.10175 by the authors.

Figure 1
Figure 1. Overview of the creation process of L2DTnH. 3.1 Context and Challenges Our research builds upon the LoL “Tribunal” chatlogs dataset [86]. The dataset was created by RIOT Games’ “Tribunal” moderation system [12], an initiative where experienced players collectively review matches wherein an player had been reported to exhibit toxic behavior—with the purpose of verifying if such claims are true. Each case in the Tribu… view at source ↗
Figure 2
Figure 2. Exemplary application of our Browser Extension (website: [ [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. The graphical interface of our browser extension. The analysis can be stopped or resumed. [PITH_FULL_IMAGE:figures/full_fig_p020_3.png] view at source ↗

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Works this paper leans on

82 extracted references · 82 canonical work pages

  1. [1]

    Accessed on October 7th, 2025,https://www.reddit.com/r/leagueoflegends/c omments/1ngnf9b/league_is_making_me_dislike_my_friends/

    work page 2025

  2. [2]

    Reddit gaming,https://www.reddit.com/r/gaming/

    work page

  3. [3]

    Sensitive personal data.https://home.treasury.gov/taxonomy/term/7651

    work page

  4. [4]

    Forms of harassment in online video games in the united states in 2023. Tech. rep., Statista (2023),https://www.statista.com/statistics/1133182/harassment-o nline-video-games/

    work page arXiv 2023

  5. [5]

    Accessed on October 6th, 2025 (2025),https://leagueoflegends

    The tribunal. Accessed on October 6th, 2025 (2025),https://leagueoflegends. fandom.com/wiki/The_Tribunal

    work page 2025

  6. [6]

    Accessed on Oc- tober 6th, 2025 (2025),https://activeplayer.io/league-of-legends/

    ActivePlayer: League of legends live player count and statistics. Accessed on Oc- tober 6th, 2025 (2025),https://activeplayer.io/league-of-legends/

    work page 2025

  7. [7]

    Accessed on October 6th, 2025 (2025),https://activeplayer.io/world-of-tanks/

    ActivePlayer: World of tanks (wot) live player count and game statistics. Accessed on October 6th, 2025 (2025),https://activeplayer.io/world-of-tanks/

    work page 2025

  8. [8]

    ADL: Free to play? hate, harassment and positive social experience in online games

    work page

Show all 82 references
  1. [9]

    2025 (2020),https://adl.org/resources/report/free-p lay-hate-harassment-and-positive-social-experience-online-games-2020

    Accessed: 6 Oct. 2025 (2020),https://adl.org/resources/report/free-p lay-hate-harassment-and-positive-social-experience-online-games-2020

    2025

  2. [10]

    In: Companion Proceedings of TheWebConf (2019)

    Aroyo, L., Dixon, L., Thain, N., Redfield, O., Rosen, R.: Crowdsourcing subjective tasks: the case study of understanding toxicity in online discussions. In: Companion Proceedings of TheWebConf (2019)

    2019

  3. [11]

    IEEE Security & Privacy (2012)

    Bailey, M., Dittrich, D., Kenneally, E., Maughan, D.: The Menlo report. IEEE Security & Privacy (2012)

    2012

  4. [12]

    In: CHI (2021)

    Beres, N.A., Frommel, J., Reid, E., Mandryk, R.L., Klarkowski, M.: Don’t you know that you’re toxic: Normalization of toxicity in online gaming. In: CHI (2021)

    2021

  5. [13]

    In: WWW (2014)

    Blackburn, J., Kwak, H.: Stfu noob! predicting crowdsourced decisions on toxic behavior in online games. In: WWW (2014)

    2014

  6. [14]

    In: ICSCSS (2024)

    Bonthu, B., Abhay, P., Gottipati, L.S., Vamsi, G.K.: Civilitycheck: An integrated natural language processing and machine learning framework to detect hateful and offensive language. In: ICSCSS (2024)

    2024

  7. [15]

    In: Science and Information Conference

    Bowker, J., Ophoff, J.: Reducing exposure to hateful speech online. In: Science and Information Conference. Springer (2022)

    2022

  8. [16]

    In: ACM/SIGAPP SAC

    Braun, T., Pekaric, I., Apruzzese, G.: Understanding the process of data labeling in cybersecurity. In: ACM/SIGAPP SAC. pp. 1596–1605 (2024)

    2024

  9. [17]

    In: CHI-PLAY (2021)

    Canossa, A., Salimov, D., Azadvar, A., Harteveld, C., Yannakakis, G.: For honor, for toxicity: Detecting toxic behavior through gameplay. In: CHI-PLAY (2021)

    2021

  10. [18]

    In: CIPR

    Chakrabarty, N.: A machine learning approach to comment toxicity classification. In: CIPR. Springer (2019)

    2019

  11. [19]

    Commission, E.: Sensitive data.https://ec.europa.eu/info/law/law-topic/d ata-protection/reform/rules-business-and-organisations/legal-grounds -processing-data/sensitive-data_en

  12. [20]

    In: IEEE HNICEM (2019)

    Cornel, J.A., Pablo, C.C., Marzan, J.A., Mercado, V.J., Fabito, B., Rodriguez, R., Octaviano, M., Oco, N., De La Cruz, A.: Cyberbullying detection for online games chat logs using deep learning. In: IEEE HNICEM (2019)

    2019

  13. [21]

    Corrˆ ea, N.K.: Aira (2023).https://doi.org/10.5281/zenodo.6989727,https: //github.com/Nkluge-correa/Aira 18 Jonas Ave, Irdin Pekaric, Matthias Frohner, and Giovanni Apruzzese

    2023 doi

  14. [22]

    In: ACL (2024)

    Costa-juss` a, M., Meglioli, M., Andrews, P., Dale, D., Hansanti, P., Kalbassi, E., Mourachko, A., Ropers, C., Wood, C.: Mutox: Universal multilingual audio-based toxicity dataset and zero-shot detector. In: ACL (2024)

    2024

  15. [23]

    In: Inter- national Conference on Networking and Communications (2024)

    Deep, S., Singh, S., Yadav, S., Shedge, Y., Khade, A.: Creating an integrated browser plug-in for detecting and blocking obscene image/text content. In: Inter- national Conference on Networking and Communications (2024)

    2024

  16. [24]

    In: IEEE CoG (2023)

    Dreier, L., Pirker, J.: Toxicity in twitch live stream chats: Towards understanding the impact of gender, size of community and game genre. In: IEEE CoG (2023)

    2023

  17. [25]

    In: IEEE OCTA (2020)

    d’Sa, A.G., Illina, I., Fohr, D.: Bert and fasttext embeddings for automatic detec- tion of toxic speech. In: IEEE OCTA (2020)

    2020

  18. [26]

    In: ACDSA (2024)

    Du Toit, J.L., Kotz´ e, E.: The automatic detection of abusive language in dota 2 chat messages. In: ACDSA (2024)

    2024

  19. [27]

    In: DATE Friday Workshop on System-level Design Methods for Deep Learning on Heterogeneous Architectures (2021)

    Ducasse, Q., Cotret, P., Lagadec, L., Stewart, R.: Benchmarking quantized neural networks on fpgas with finn. In: DATE Friday Workshop on System-level Design Methods for Deep Learning on Heterogeneous Architectures (2021)

    2021

  20. [28]

    Accessed: 6 Oct

    ExplodingTopics: Amount of data created daily. Accessed: 6 Oct. 2025 (2025), https://explodingtopics.com/blog/data-generated-per-day

    2025

  21. [29]

    2025 (2025),ht tps://explodingtopics.com/blog/number-of-gamers

    ExplodingTopics: How many gamers are there? Accessed: 6 Oct. 2025 (2025),ht tps://explodingtopics.com/blog/number-of-gamers

    2025

  22. [30]

    Accessed: 6 Oct

    ExplodingTopics: Internet traffic from mobile devices. Accessed: 6 Oct. 2025 (2025),https://explodingtopics.com/blog/mobile-internet-traffic

    2025

  23. [31]

    Quality & Quan- tity (2015)

    Falotico, R., Quatto, P.: Fleiss’ kappa statistic without paradoxes. Quality & Quan- tity (2015)

    2015

  24. [32]

    In: CHI (2020)

    Frommel, J., Sagl, V., Depping, A.E., Johanson, C., Miller, M.K., Mandryk, R.L.: Recognizing affiliation: Using behavioural traces to predict the quality of social interactions in online games. In: CHI (2020)

    2020

  25. [33]

    In: Proc

    Ghosh, S., Lepcha, S., Sakshi, S., Shah, R.R., Umesh, S.: Detoxy: A large-scale multimodal dataset for toxicity classification in spoken utterances. In: Proc. Inter- speech (2022)

    2022

  26. [34]

    Hanu, L., Unitary team: Detoxify. Github. https://github.com/unitaryai/detoxify

  27. [35]

    In: Proc

    Hartvigsen, T., Gabriel, S., Palangi, H., Sap, M., Ray, D., Kamar, E.: Toxigen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection. In: Proc. ACL (2022)

    2022

  28. [36]

    In: Proc

    Hsieh, C.Y., Li, C.L., YEH, C.K., Nakhost, H., Fujii, Y., Ratner, A.J., Krishna, R., Lee, C.Y., Pfister, T.: Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes. In: Proc. ACL (2023)

    2023

  29. [37]

    Jahanbakhsh, F., Zhang, A.X., Karahalios, K., Karger, D.R.: Our browser exten- sion lets readers change the headlines on news articles, and you won’t believe what they did! Proceedings of the ACM on Human-Computer Interaction (2022)

    2022

  30. [38]

    In: SOICT (2023)

    Kazimierczak, M., Thanapattheerakul, T., Chan, J.H.: Enhancing security in what- sapp: a system for detecting malicious and inappropriate content. In: SOICT (2023)

    2023

  31. [39]

    Games and Culture (2024)

    Kilmer, E.D., Aslan, Z., Kowert, R.: Addressing toxicity and extremism in games: Conversations with the video game industry. Games and Culture (2024)

    2024

  32. [40]

    Online Social Networks and Media (2022)

    Kim, J., Wohn, D.Y., Cha, M.: Understanding and identifying the use of emotes in toxic chat on twitch. Online Social Networks and Media (2022)

    2022

  33. [41]

    Kordyaka, B., Karaosmanoglu, S., Laato, S.: Defining toxicity in multiplayer online games: A systematic literature review. Comp. Human Behavior Reports (2025)

    2025

  34. [42]

    In: CHI-PLAY (2020)

    Kou, Y.: Toxic behaviors in team-based competitive gaming: The case of league of legends. In: CHI-PLAY (2020)

    2020

  35. [43]

    In: CHI (2015) Towards Deployment of NLP-based Toxicity Detectors in Video Games 19

    Kwak, H., Blackburn, J., Han, S.: Exploring cyberbullying and other toxic behavior in team competition online games. In: CHI (2015) Towards Deployment of NLP-based Toxicity Detectors in Video Games 19

    2015

  36. [44]

    In: CSCSC (2018)

    Kwon, S., Liang, P., Tandon, S., Berman, J., Chang, P.j., Gilbert, E.: Tweety holmes: A browser extension for abusive twitter profile detection. In: CSCSC (2018)

    2018

  37. [45]

    In: CHI (2024)

    Laato, S., Kordyaka, B., Hamari, J.: Traumatizing or just annoying? unveiling the spectrum of gamer toxicity in the StarCraft II community. In: CHI (2024)

    2024

  38. [46]

    In: AAAI ICWSM (2022)

    Lobo, P.R., Daga, E., Alani, H.: Supporting online toxicity detection with knowl- edge graphs. In: AAAI ICWSM (2022)

    2022

  39. [47]

    In: ACL (2022)

    Logacheva, V., Dementieva, D., Ustyantsev, S., Moskovskiy, D., Dale, D., Krotova, I., Semenov, N., Panchenko, A.: ParaDetox: Detoxification with parallel data. In: ACL (2022)

    2022

  40. [48]

    In: Extended Abstracts of CHI (2023)

    Mandryk, R.L., Frommel, J., Goyal, N., Freeman, G., Lampe, C., Vieweg, S., Wohn, D.Y.: Combating toxicity, harassment, and abuse in online social spaces: A work- shop at chi 2023. In: Extended Abstracts of CHI (2023)

    2023

  41. [49]

    In: NetGames (2015)

    M¨ artens, M., Shen, S., Iosup, A., Kuipers, F.: Toxicity detection in multiplayer online games. In: NetGames (2015)

    2015

  42. [50]

    Merayo, N., Cotelo, R., Carratal´ a-S´ aez, R., And´ ujar, F.J.: Applying machine learn- ing to assess emotional reactions to video game content streamed on spanish twitch channels. Comp. Speech & Language (2024)

    2024

  43. [51]

    In: SLSP (2019)

    Miok, K., Nguyen-Doan, D., ˇSkrlj, B., Zaharie, D., Robnik-ˇSikonja, M.: Prediction uncertainty estimation for hate speech classification. In: SLSP (2019)

    2019

  44. [52]

    Computers & Security (2018)

    Murnion, S., Buchanan, W.J., Smales, A., Russell, G.: Machine learning and se- mantic analysis of in-game chat for cyberbullying. Computers & Security (2018)

    2018

  45. [53]

    In: ACL (2025)

    Naseem, U., Shiwakoti, S., Shah, S.B., Thapa, S., Zhang, Q.: Gametox: A com- prehensive dataset and analysis for enhanced toxicity detection in online gaming communities. In: ACL (2025)

    2025

  46. [54]

    In: WI-IAT (2017)

    Neto, J.A., Yokoyama, K.M., Becker, K.: Studying toxic behavior influence and player chat in an online video game. In: WI-IAT (2017)

    2017

  47. [55]

    In: SBP-BRiMS (2019)

    Obadimu, A., Mead, E., Hussain, M.N., Agarwal, N.: Identifying toxicity within youtube video comment. In: SBP-BRiMS (2019)

    2019

  48. [56]

    BMJ (2021)

    Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E., et al.: The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ (2021)

    2020

  49. [57]

    Journal of Gaming & Virtual Worlds (2015)

    Paul, H.L., Bowman, N.D., Banks, J.: The enjoyment of griefing in online games. Journal of Gaming & Virtual Worlds (2015)

    2015

  50. [58]

    In: ICETI4T (2025)

    Pawale, S., Pingat, V., Petkar, T., Patil, A., Waykar, S.: Toxiguard-a system that guides your vision towards toxic text. In: ICETI4T (2025)

    2025

  51. [59]

    In: CHI PLAY (2023)

    Poeller, S., Dechant, M.J., Klarkowski, M., Mandryk, R.L.: Suspecting sarcasm: how league of legends players dismiss positive communication in toxic environ- ments. In: CHI PLAY (2023)

    2023

  52. [60]

    bad vibra- tions

    Reid, E., Mandryk, R.L., Beres, N.A., Klarkowski, M., Frommel, J.: “bad vibra- tions”: Sensing toxicity from in-game audio features. IEEE ToG (2022)

    2022

  53. [61]

    In: 2025 EuroS&PW

    Schr¨ oer, S.L., Canevascini, N., Pekaric, I., Widmer, P., Laskov, P.: The dark side of the web: Towards understanding various data sources in cyber threat intelligence. In: 2025 EuroS&PW. pp. 79–89. IEEE (2025)

    2025

  54. [62]

    In: ACM CHI (2019)

    Seng¨ un, S., Salminen, J., Jung, S.g., Mawhorter, P., Jansen, B.J.: Analyzing hate speech toward players from the mena in league of legends. In: ACM CHI (2019)

    2019

  55. [63]

    In: CHI (2017)

    Shaer, O., Westendorf, L., Knouf, N.A., Pederson, C.: Understanding gaming per- ceptions and experiences in a women’s college community. In: CHI (2017)

    2017

  56. [64]

    IEEE Access (2022) 20 Jonas Ave, Irdin Pekaric, Matthias Frohner, and Giovanni Apruzzese

    Shannaq, F., Hammo, B., Faris, H., Castillo-Valdivieso, P.A.: Offensive language detection in arabic social networks using evolutionary-based classifiers learned from fine-tuned embeddings. IEEE Access (2022) 20 Jonas Ave, Irdin Pekaric, Matthias Frohner, and Giovanni Apruzzese

    2022

  57. [65]

    Sikiandani, N.M.D., Suarjaya, I.M.A.D., Putra, Y.P.: Browser-based detection of harmful content with deep learning model. J. Appl. Inf. Comp. (2025)

    2025

  58. [66]

    In: CHI PLAY

    Sparrow, L.A., Galwey, R., Jovic, D., Hardwick, T., Butt, M.A.: Towards ethical ai moderation in multiplayer games. In: CHI PLAY

  59. [67]

    Accessed October 6th 2025 (2024),https://www

    Statista: Esports - worldwide. Accessed October 6th 2025 (2024),https://www. statista.com/outlook/amo/esports/worldwide

    2025

  60. [68]

    Accessed: 6 Oct

    Statista: Video game worldwide - statistics & facts. Accessed: 6 Oct. 2025 (2025), https://www.statista.com/topics/1680/gaming/

    2025

  61. [69]

    Steam: Community discussion,https://steamcommunity.com/discussions/

  62. [70]

    In: PETRA (2021)

    Stepanova, N., Muthemba, W., Todrzak, R., Cross, M., Ames, N., Raiti, J.: Nat- ural language processing and sentiment analysis for verbal aggression detection; a solution for cyberbullying during live video gaming. In: PETRA (2021)

    2021

  63. [71]

    Accessed: 6 Oct

    StreamHatchet: Most watched games on youtube gaming. Accessed: 6 Oct. 2025 (2025),https://streamhatchet.com/rankings/youtube/games/

    2025

  64. [72]

    Aggressive Behav

    Tang, W.Y., Reer, F., Quandt, T.: Investigating sexual harassment in online video games: How personality and context factors are related to toxic sexual behaviors against fellow players. Aggressive Behav. (2020)

    2020

  65. [73]

    In: CODASPY (2023)

    Tricomi, P.P., Facciolo, L., Apruzzese, G., Conti, M.: Attribute inference attacks in online multiplayer video games: A case study on dota2. In: CODASPY (2023)

    2023

  66. [74]

    Accessed: 6 Oct

    TwitchTracker: Twitch top streamers. Accessed: 6 Oct. 2025 (2025),https://tw itchtracker.com/channels/ranking

    2025

  67. [75]

    Viggiato, M., Lin, D., Hindle, A., Bezemer, C.P.: What causes wrong sentiment classifications of game reviews? IEEE TOG (2021)

    2021

  68. [76]

    In: RIVF (2021)

    Vo, H.H.P., Tran, H.T., Luu, S.T.: Automatically detecting cyberbullying com- ments on online game forums. In: RIVF (2021)

    2021

  69. [77]

    arXiv preprint arXiv:2106.06213 (2021)

    Weld, H., Huang, G., Lee, J., Zhang, T., Wang, K., Guo, X., Long, S., Poon, J., Han, S.C.: Conda: a contextual dual-annotated dataset for in-game toxicity understanding and detection. arXiv preprint arXiv:2106.06213 (2021)

    2021

  70. [78]

    In: ICSI

    Wijesiriwardene, T., Inan, H., Kursuncu, U., Gaur, M., Shalin, V.L., Thirunarayan, K., Sheth, A., Arpinar, I.B.: Alone: A dataset for toxic behavior among adolescents on twitter. In: ICSI. Springer (2020)

    2020

  71. [79]

    In: CHI-PLAY (2024)

    Wijkstra, M., Rogers, K., Mandryk, R.L., Veltkamp, R.C., Frommel, J.: How to tame a toxic player? a systematic literature review on intervention systems for toxic behaviors in online video games. In: CHI-PLAY (2024)

    2024

  72. [80]

    Xue, S.S.: League of legends tribunal chatlogs (2020),https://www.kaggle.com /datasets/simshengxue/league-of-legends-tribunal-chatlogs

    2020

  73. [81]

    ACM Games: Research and Practice (2024)

    Yang, Z., Grenon-Godbout, N., Rabbany, R.: Game on, hate off: A study of toxicity in online multiplayer environments. ACM Games: Research and Practice (2024)

    2024

  74. [82]

    Aggressive Behavior (2022) Fig

    Zsila, ´A., Shabahang, R., Aruguete, M.S., Orosz, G.: Toxic behaviors in online multiplayer games: Prevalence, perception, risk factors of victimization, and psy- chological consequences. Aggressive Behavior (2022) Fig. 3: The graphical interface of our browser extension. The ...

    2022

Pith tools

Reviewed May 10, 2026 · model on record in the stance chip above.