Abstract-Level Reporting Practices and Methodological Composition in 245 Studies Using User-Generated Video Game Reviews

Studies of user-generated video game reviews make sampling decisions that limit how far their findings can be generalized. How often these decisions are reported has not been measured. This abstract-level systematic mapping study screened 868 records from Scopus and Web of Science and coded the 245 eligible studies on eight dimensions from titles, abstracts and author keywords. Agreement with independent manual coding gave a median per-column Cohen’s kappa of 0.92 and a screening kappa of 0.93, and a second coder agreed with the final coding at a median of 0.89. Language scope was absent from 93.1% of abstracts, corpus size from 49.8% and the review platform from 41.2%; only 4.1% reported all three. Platform disclosure improved over time; language disclosure did not. In the 56 accessible full texts of a random sample of 75, nearly all missing platforms and corpus sizes were stated, but language scope remained unstated in 36. Among studies naming a source, 72.2% used Steam. Transformer-based methods rose (odds ratio 1.56 per year, p < 0.001), while dictionary-based sentiment analysis did not decline in absolute use. Syntheses in this area need full-text extraction, review-based findings largely describe one storefront, and language scope is underreported in abstracts and full texts alike.