Steam User Review Data – 1,000 Most Recent Reviews from 500 Popular Games

This dataset contains 1,000 of the most recent Steam user reviews from 500 games, ordered by organic visibility / relevance at the time of collection – December 6th 2023. The dataset includes review text and associated metadata to support research on player feedback, platform cultures, and user experience evaluation.<b>Included fields:</b><br>recommendationid; author; language; review; timestamp_created; timestamp_updated; voted_up; votes_up; votes_funny; weighted_vote_score; comment_count; steam_purchase; received_for_free; written_during_early_access; hidden_in_steam_china; steam_china_location; timestamp_dev_responded; developer_response.<br>This dataset provides a broader sampling frame for examining how users evaluate games through public reviews. It is suitable for qualitative coding, descriptive analysis of review metadata, and computational text analysis.<b>Relationship to associated publication:</b><br>The OzCHI ’25 paper <i>Evaluating Time in Play and Temporal Satisfaction: Time-Centric Language in Video Game User Reviews on Steam</i> used this dataset to examine how players evaluate video games through time-centric language in Steam user reviews.<br>From the first 100 games ordered by relevance at the time of collection, a time-centric keyword filter was applied, identifying 12,337 reviews containing time-related terms. From these, 5,423 reviews that explicitly addressed player time were thematically analysed, leading to six Temporal Priorities and the construct of Temporal Satisfaction.This larger repository is designed to complement that study by extending the sampling frame beyond the 100-game analytic subset and enabling future qualitative and mixed-method work that builds on player-led temporal evaluation – offering a user-centred lens that can sit alongside (and enrich) existing quantitative approaches to playtime and review analysis.