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

arXiv: https://arxiv.org/abs/2405.06306 · HTML: https://mlsanity.com/paper/2405.06306/

## 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.

## Builds on (most cited first, 2 of 2)

- [Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection](https://mlsanity.com/paper/1907.09177/) (2019)
- [Ideology-driven polarisation in online ratings: the review bombing of The Last of Us Part II](https://mlsanity.com/paper/2104.01140/) (2021)

## 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.