A Descriptive and Correlational Analysis of Popularity Patterns Among Trending Google Chrome Extensions

Abstract

This study presents a descriptive and correlational analysis of a publicly available dataset of trending Google Chrome extensions (N = 50). Browser extensions constitute an increasingly important layer of user-facing software, yet empirical characterizations of what distinguishes popular extensions from the broader marketplace remain scarce relative to the volume of work on extension security and privacy. Using a dataset of extensions flagged as “trending” on the Chrome Web Store, we characterize the distribution of extensions across functional categories, describe the central tendency and dispersion of user ratings and review counts, and examine the association between review volume, category membership, and user ratings through non-parametric statistical tests. Extensions were overwhelmingly concentrated in the Productivity category (58.0% of the sample). Ratings and review counts both departed significantly from normality (Shapiro-Wilk p < .001 in both cases), justifying the use of non-parametric methods throughout. Spearman's rank correlation revealed a strong, statistically significant positive association between review count and rating (ρ = .71, p < .001), indicating that more heavily reviewed extensions in this sample also tended to be more favorably rated. A Kruskal-Wallis test found no statistically significant difference in rating distributions across functional categories (H(7) = 5.21, p = .63), suggesting that, within this sample, category membership alone was not a strong determinant of user rating. These findings provide a baseline empirical profile of popularity patterns in the Chrome Web Store ecosystem and offer a methodological and substantive reference point for future studies on browser extension adoption, marketplace design, and user trust signals.