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

TL;DR

This paper employs NLP techniques to analyze and distinguish review bombing in Metacritic PC game ratings, achieving high accuracy and providing insights to mitigate such phenomena.

Contribution

It introduces an NLP-based method to identify review bombing in user ratings, improving understanding and detection of malicious low reviews.

Findings

Achieved 0.88 accuracy in distinguishing review bombings from genuine low ratings

Identified key words and concepts associated with review bombing

Provided insights for mitigating review bombing effects

Abstract

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.