A NLP Approach to "Review Bombing" in Metacritic PC Videogames User Ratings
This addresses the problem of misleading user ratings for video game developers and platforms, though it appears incremental as it applies existing NLP methods to a new domain.
The researchers tackled the problem of distinguishing genuine negative reviews from coordinated 'review bombing' attacks on Metacritic PC game ratings using NLP analysis of 50,000+ user scores, achieving 0.88 accuracy on a validation set.
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.