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A NLP Approach to "Review Bombing" in Metacritic PC Videogames User Ratings

arXiv 2405.06306 ↗First posted 10 May 2024cs.CL cs.LGcited by 0

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Abstract (arXiv)

Many videogames suffer "review bombing" -a large volume of unusually low scores that in many cases do not reflect the real quality of the product- when rated by users. By taking Metacritic's 50,000+ user score aggregations for PC games in English language, we use a Natural Language Processing (NLP) approach to try to understand the main words and concepts appearing in such cases, reaching a 0.88 accuracy on a validation set when distinguishing between just bad ratings and review bombings. By uncovering and analyzing the patterns driving this phenomenon, these results could be used to further mitigate these situations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 52 references that are in the map. First posted on arXiv 10 May 2024.

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References · 2

  1. 2019Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detectioncited by 52
  2. 2021Ideology-driven polarisation in online ratings: the review bombing of The Last of Us Part IIcited by 1

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