Logo des Repositoriums
 

Effective Toxicity Prediction in Online Multiplayer Gaming: Four Obstacles to Making Approaches Usable

Kurzanzeige
dc.contributor.authorFrommel, Julian
dc.contributor.authorMandryk, Regan
dc.contributor.editorMarky, Karola
dc.contributor.editorGrünefeld, Uwe
dc.contributor.editorKosch, Thomas
dc.date.accessioned2022-08-30T10:27:43Z
dc.date.available2022-08-30T10:27:43Z
dc.date.issued2022
dc.description.abstractToxicity represents a threat to the safety and health of online multiplayer gaming communities. This has been recognized by industry, academia, and players and led to efforts for combating toxicity, including different approaches for predicting toxicity from behaviour. Despite promising results, such approaches have not yet been able to meaningfully combat toxicity at scale. In this position paper, we describe four obstacles that impede usable applied toxicity prediction in multiplayer games that could help to combat harm.We want to foster a discussion about how user-centered artificial intelligence approaches may help solve these obstacles.en
dc.identifier.doi10.18420/muc2022-mci-ws12-315
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39102
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofMensch und Computer 2022 - Workshopband
dc.relation.ispartofseriesMensch und Computer
dc.subjecttoxicity
dc.subjectreporting
dc.subjectprediction
dc.subjectclassification
dc.subjectmultiplayer
dc.subjectesports
dc.subjectcompetitive
dc.subjectgame
dc.subjectgaming
dc.titleEffective Toxicity Prediction in Online Multiplayer Gaming: Four Obstacles to Making Approaches Usableen
dc.typeText/Workshop Paper
gi.citation.publisherPlaceBonn
gi.conference.date4.-7. September 2022
gi.conference.locationDarmstadt
gi.conference.sessiontitleMCI-WS12: UCAI 2022: Workshop on User-Centered Artificial Intelligence
gi.document.qualitydigidoc

Dateien

Originalbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
Name:
WS-12-4_Effective Toxicity Prediction in Online Multiplayer Gaming.pdf
Größe:
417.03 KB
Format:
Adobe Portable Document Format
Herunterladen

Sammlungen

Workshopband MuC 2022