[wp-meta] [Making WordPress.org] #6851: Consider adding a visual representation of Rating trends for plugins.

[Making WordPress.org]

#6851: Consider adding a visual representation of Rating trends for plugins.
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 Reporter:  dufresnesteven    |       Owner:  (none)
     Type:  enhancement       |      Status:  new
 Priority:  normal            |   Milestone:
Component:  Plugin Directory  |  Resolution:
 Keywords:                    |
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Comment (by Presskopp):

 I recently built a small browser extension that visualizes plugin rating
 trends over time and ran into some data limitations that might be relevant
 for this ticket.

 The current Plugins API
 ({{{plugins/info/1.2/?action=plugin_information}}}) only exposes
 cumulative rating data. It does not provide time-based information, such
 as when ratings were submitted or how the distribution changed over time.

 To reconstruct rating trends I currently fetch and parse the paginated
 review pages, extracting the star rating and review timestamp from each
 entry and aggregating them locally. This works, but requires crawling all
 review pages and is slower and more brittle than using structured API
 data.

 If wordpress.org internally tracks rating events or review timestamps,
 exposing something like rating counts per day/week or review-based rating
 history via the API would make trend analysis much easier.

 As a small experiment around this idea I built a browser extension that
 visualizes rating trends directly on plugin pages with a more detailed
 visual representation of rating changes over time, which can help
 highlight shifts in rating patterns more clearly:

 https://github.com/Presskopp/wp-plugin-rating-trend

 It shows what can currently be derived from the publicly available data,
 although the implementation requires significantly more effort due to the
 lack of historical rating data in the API.

 Example visualization (BackWPup plugin):

 The chart shows the monthly average rating based on individual review
 timestamps over the last 12 months. In this example, ratings dropped
 significantly after the introduction of a new version and later recovered
 again as issues were addressed.

 This kind of visualization makes it easier to see when rating changes
 occurred, something that cannot currently be derived from the API since it
 only exposes the current cumulative rating distribution.

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Ticket URL: <https://meta.trac.wordpress.org/ticket/6851#comment:6>
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