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Can Yılmaz
Can Yılmaz

Posted on • Originally published at apify.com

Scraping Steam Game & Reviews for community managers: what data is available and how to use it

#webscraping #apify #socialmedia #productivity

If you are working in the community managers space and you have ever needed Steam Game & Reviews as a structured feed, you know the gap between "the data exists on a website" and "the data is in my notebook" can swallow a whole sprint. Here is what the dataset actually contains and the workflow I would build around it.

Why this data matters for community managers

The short version: social listening, sentiment tracking, brand monitoring and content research. Steam Game & Reviews Scraper Steam Store Data & User Reviews to JSON/CSV Scrape game metadata and user reviews from the Steam Store using Steam's public JSON API. For community managers, trend researchers and brand-monitoring teams, the value is having a normalised, queryable representation of a source that ordinarily fights structured access.

Fields available

The dataset comes back with these fields per record:

  • type -- type
  • appId -- app id
  • name -- name
  • url -- url
  • gameType -- game type
  • shortDescription -- short description
  • isFree -- is free
  • priceCurrent -- price current
  • priceOriginal -- price original
  • discountPercent -- discount percent
  • developers -- developers
  • publishers -- publishers
  • genres -- genres
  • categories -- categories
  • releaseDate -- release date
  • comingSoon -- coming soon
  • platforms -- platforms
  • metacriticScore -- metacritic score
  • metacriticUrl -- metacritic url
  • requiredAge -- required age
  • headerImage -- header image
  • website -- website
  • supportedLanguages -- supported languages
  • scrapedAt -- scraped at

The mix is decent. You get enough identifying information to deduplicate across runs, enough content to actually answer questions, and enough timestamps to do time-series work.

Two example records

Trimmed for readability:

{
  "type": "game",
  "appId": "570",
  "name": "Dota 2",
  "url": "https://store.steampowered.com/app/570",
  "gameType": "game",
  "shortDescription": "Every day, millions of players worldwide enter battle as one of over a hundred Dota heroes. And no matter if it's their 10th hour of play...",
  "isFree": true,
  "priceCurrent": null,
  "priceOriginal": null,
  "discountPercent": null
}
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{
  "type": "review",
  "appId": "570"
}
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A community manager could start asking real questions on day one with this shape: aggregate counts across categorical fields, distributions on numeric fields, simple text analysis on the long-form content.

A workflow that works

If I were dropping this into an existing community managers stack:

  1. Schedule a recurring scrape. Daily or every few hours depending on how fast the source updates.
  2. Land it raw. Object storage, partitioned by date. Cheap, replayable, future-proof against schema changes.
  3. Curate. Dedup on the natural key, type-cast the columns, surface the curated view to your dashboard or notebook layer.
  4. Layer enrichment. Most community managers workflows need a second source -- reference data, internal CRM, third-party signal -- to extract real value. Build that join early.

Honest trade-offs

This is not a magic dataset. Things to know up-front:

  • The source can rate-limit you. Plan for retries and back-off.
  • Free-text fields are noisy. Budget for cleaning.
  • Schema can drift if the source redesigns. Wire up assertions on record counts and key presence.

Concrete questions you could answer day one

A community manager working with this dataset could, on the first day:

  • Rank entities by any numeric field, broken down by a categorical field, to find leaders and laggards.
  • Build a time-series of new entries per day from the timestamp columns to see growth or decline.
  • Pull the long-form text into a quick TF-IDF or topic-model to surface what the dataset is actually about under the hood.
  • Spot duplicates and near-duplicates as a data-quality exercise, which often surfaces interesting structural anomalies in the source.

None of those questions require a finished pipeline. A notebook, the JSON file, and an afternoon are enough.

Verdict

For community managers, this is a useful input -- not a finished answer, but a strong starting point that saves you from writing a brittle HTML parser of your own. The marginal cost of trying it on a real project is a few hours; the marginal value if the dataset clicks with your workflow is open-ended.

For live, customizable extractions of this data, the actor that produced the dataset shown above is published on the Apify Store: logiover/steam-game-reviews-scraper. It supports JSON, CSV and Excel exports and runs on a schedule.

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