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@benwerd @benwerd@mastodon.social It would be cool if they could easily expose numbers of interactions (reads, replies, bookmarks, etc.) as a signal in a way such that social readers could filter using this data along with tags/categories for prioritizing what we might want to read.

Selfishly they could use these signals internally for better measuring engagement with articles and particular writers. Is it high quality engagement (useful comments, reads) versus lower quality engagement (bookmarks which might indicate "I read the headline and might be interested").

Highly enterprising publications, and especially "local" publications/newspapers, might consider offering IndieWeb as a Service to allow their readers the ability to have their "own platform" within the publisher's platform/stack. This could be done on a co-op basis or potentially even bundled into subscription prices. Something along the lines of Kinja perhaps, but with more ownership/control/ability to move. Or perhaps a white-labeled version of something like micro.blog, but run/managed by the NYT, WSJ, other?

A well tummeled version of the Hometown fork of Mastodon with "local only posting" could be an engaging thing for a sophisticated newspaper or magazine to create. The publication could have closer control/moderation of the local posting for article related conversations, but people could still communicate with others outside of that "home" server. Alternately, in the standard Mastodon model, the "public timeline" could be filtered for posts about or commenting on the outlet's own content and all other content goes into the federated timeline.

Publications offering their own microsub social reader interfaces could be fun and clever. It could be an interesting way to have a more streamlined reading experience for paid subscribers among other potential options. This could be an interesting interface for helping people build a truly custom reading experience specifically for them, particularly for larger newspapers with large amounts of content that could be better filtered and personalized to individuals.