TY  - THES
A1  - Schrottenbacher, Patrick
T1  - Identifying toxic behaviour in online games
N2  - 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.
KW  - Graph Neural Networks
KW  - Toxicity
KW  - Classification
Y1  - 2024
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/81676
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-816761
CY  - Frankfurt am Main
ER  -