Online reviews provide users with the opportunity to rate various types of items such as movies, music, and video games using a combination of numeric scores and textual comments. The study proposes a novel method that applies statistical matching on network-based covariates, with the aim to improve the estimation of the association between words and highly controversial items in online reviews. 

The application of this method on a sample of 40,665 items from the website Metacritic detects 218 highly controversial items. The application supports the theory that controversies on Metacritic are driven with a sense of self-awareness of participating of an online controversy (‘review bombing’). Typical controversial topics (sexual identities, religious morality, politics) are associated with controversial reviews, too.