Steam Games Metadata and Player Reviews (2020–2024)

This dataset presents a comprehensive and structured collection of video game metadata and user reviews from the Steam platform, covering the period between January 2020 and December 2024. It was compiled to support research into how various game attributes influence user satisfaction, engagement, and review behavior. The central research hypothesis behind this work suggests that specific characteristics of video games, such as genre, pricing, and supported platform, are closely associated with trends in user sentiment and review volume. Understanding these patterns can contribute to predictive models of game reception and improve design and marketing strategies for future releases.

To explore this hypothesis, data was gathered in two phases. In the first phase, metadata for all games listed on Steam during the target period was collected using the official Steam API. Each game was identified by its unique AppID and evaluated to ensure data completeness. The scraper retrieved details including the game title, release date, genres, supported languages, age restrictions, and pricing information. Games that were unreleased or launched before 2020 were excluded from the dataset. This resulted in a refined metadata file, stored as games.json, containing detailed information on 23,107 Steam games released from 2020 onward.

In the second phase, a dedicated script was used to collect user reviews for each game in the metadata file. The review collection process filtered out games with fewer than 25 reviews to avoid bias due to insufficient data. For the remaining games, reviews were gathered in all available languages to ensure a culturally diverse and inclusive dataset. Reviews were saved in individual CSV files named using the game’s AppID and the number of reviews it contains. Each file includes structured rows with fields such as review text, language, rating, and vote counts. This resulted in over 31 million reviews across more than 23,000 games, forming a robust basis for textual and quantitative analysis.

The data reveals several meaningful trends. Free-to-play games tend to attract higher review volumes, although not necessarily higher user ratings. Games within specific genres, such as role-playing, simulation, and survival, often have longer and more detailed reviews, indicating deeper user engagement.

By releasing both the metadata and reviews together, this dataset offers a multidimensional view of the Steam game landscape from 2020 to 2024. It captures user engagement in digital gaming during and after the COVID-19 period and provides a foundation for future research in user behavior, content personalization, and the evolving dynamics of online platforms.