Has anybody built an "in case you missed it" tool for #Mastodon? I'm thinking that once a day it reads everything in your feed from the last 24 hours, finds the toots with the most engagement, then emails you a summary.
Could be a useful #algorithm now that I've used #followgraph to boost my feed up to levels where reading everything is now impossible!
@harlanh yup!
👉🏼 https://github.com/hodgesmr/mastodon_digest
by @MattHodges
Background: https://mastodon.social/@MattHodges/109451111674479285
#fedilytics (I am trying to make this hashtag happen, intent is all things Fediverse data science)
@harlanh I haven’t dug in yet, but I believe it’s based exclusively on post data. Adding social network features could be useful.
@bentomn has been thinking about using social networks to predict the information value of boosting posts. That is, has your network already seen this?
A stand-alone tool like Mastodon Digest might be a good place to experiment with this.
Discussion here: https://hachyderm.io/@bentomn/109547838770700017
When I boost posts, what I miss is awareness of how many people I follow have already boosted the same story. While I might use a rule like boost only stories that are 4 hours old or less, all it takes is a little scrolling sometimes to find a much larger press account has boosted the same story in less than 1 or 2 hours. As others have mentioned, servers and clients could step up and help to manage the number of times duplicate boosts of the same post are shown with merged summary headings.
@harlanh Honestly people are quick to hate on algorithms and #fedilytics in general, but it’s because they’re used to being exploited by them.
Responsible data scientists can build respectful tools that empower principals* with affordances for thoughtful, constructive, and kind participation in authentic communities.
*Like a “User”, but not exploited
@PeterBronez @harlanh I agree in principle with this sentiment; on the other hand "I'll believe it when I see it".
So many responsible data scientists I know have very questionable funding sources (both in private and public sectors), and when push comes to shove, it's the funder's interests that make the call (even if to the scientists' discontent).
I also fear well-intentioned approaches will reproduce bad patterns ("let's start with popular" → prioritizes controversy → more addicting)
@PeterBronez @harlanh (I hope I didn't come across as being quick to hate on analytics; I do believe a thoughtful combination of data science techniques and HCI work _can_ produce healthy results, but these concerns need to be there every step of the way.
After all, it feels like the well-trodden paths in this area have been explored mostly to our detriment... as you said, we got used to being exploited.)
@hisham_hm @harlanh not at all, thanks for the thoughtful comment.
Perhaps it would be productive to write a #fedilytics code of conduct? A #fedilyticscharter?
This would provide a nexus for debate about constitutes respectful data science on #Fediverse. Then practitioners can follow the code and we can call out companies that violate it.
There are already a few proposals like this, see https://writer.oliphant.social/oliphant/mastodon-handy-links-page#proposals-and-policies
One option is to fork the #SlocanStatement and extend it with a section on analytic applications (contrasting with the current focus on transactional applications) to create a #fedilyticscharter
Alternatively, we could anchor a #fedilyticscharter on the consent-based communication model that @cwebber wrote for @spritelyinst
@hisham_hm @harlanh do either of those options feel viable to you?
What principles would you want to see in a #fedilyticscharter ?
Are there any good analytics focused code of conduct documents we could reference?
Harlan Harris
Peter Bronez
Hisham
