Identifying toxic behaviour in online games
- In online video games toxic interactions are very prevalent and often even considered an imperative part of gaming. Most studies analyse the toxicity in video games by analysing the messages that are sent during a match, while only a few focus on other interactions. We focus specifically on the in-game events to try to identify toxic matches, by constructing a framework that takes a list of time-based events and projects them into a graph structure which we can then analyse with current methods in the field of graph representation learning. Specifically we use a Graph Neural Network and Principal Neighbour- hood Aggregation to analyse the graph structure to predict the toxicity of a match. We also discuss the subjectivity behind the term toxicity and why the process of only analysing in-game messages with current state-of-the-art NLP methods isn’t capable to infer if a match is perceived as toxic or not.
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| Author: | Patrick SchrottenbacherORCiD |
|---|---|
| URN: | urn:nbn:de:hebis:30:3-816761 |
| Place of publication: | Frankfurt am Main |
| Document Type: | Bachelor Thesis |
| Language: | English |
| Date of Publication (online): | 2024/01/10 |
| Year of first Publication: | 2023 |
| Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
| Granting Institution: | Johann Wolfgang Goethe-Universität |
| Release Date: | 2024/04/29 |
| Tag: | Classification; Graph Neural Networks; Toxicity |
| Page Number: | 35 |
| HeBIS-PPN: | 51765251X |
| Institutes: | Informatik und Mathematik |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Sammlungen: | Universitätspublikationen |
| Licence (German): |  Creative Commons - Namensnennung-Keine kommerzielle Nutzung-Weitergabe unter gleichen Bedingungen 4.0 |
