podbrushkin

Go ahead. Shoot me.

I wonder, how many people regret saying it because they've been actually shot. Ah, yes. None. Good line.

Nah, promote him instead.

If computing these tags is not expensive, they can be computed and stored internally in the app at client side. If this will work and will be useful, it can be moved to server-side in one of lemmy’s updates. Each post will have have probable tags in metadata with % of how sure an algorithm was about assigning this tag. Personally, I think affecting your feed by picking appropriate instance doesn’t work, and I do hope other instance-independent ways to browse lemmy will become available. But right now I haven’t found a time even to check Lemmy’s api to see what’s already available.

Is it only an idea? I can think of automatic tagging of all posts. If you have access to a post and all its comments, probably you can programmatically assign a tag to it. Based on “words cloud” or something like that. Annoying posts usually have a lot of comments which simplifies automatic tagging. It can make it possible to filter out specific topics, or, contrary, browse them specifically.

I think it’s a matter of sorting. Do you know how does it work? I don’t. Would be interesting to know. Why exactly those posts are shown in the feed? Is it “sort all by upvotes count descending”? Probably not, because this way you will get popular post from previous year. Is it “same, but filtered to those posted within last week”? Probably not. I think interacting with lemmy’s api can shed some light on this topic. Probably you can use whatever sorting you like.

Solutions should bring more freedom, not restrictions. Imagine not being able to upvote something you like.

Some software solutions exist, e.g. War and Peace by Tolstoy can be downloaded with metadata, ids are assigned to all characters and when one character tells something to another, this is highlighted as “x speaks to y”, and you can run a community detection algorithms on this data. I think in the paper they’ve been mentioning some proprietary software. I suspect detecting who speaks to whom is even harder.

Also, some form of crowd sourcing probably should be possible. At least collecting scans is possible on wikisource and wikimedia commons.

Probably AI language models should be pretty good in distinguishing between linguistic ambiguities.

I dream for a time when such reports as in OP post will be a matter of work for an hour or two — because data will be already collected and clean.

Like that? https://archive.org/search?query=subject%3A%22film+photography%22&page=2&sort=-downloads

It’s voyager for iOS https://mander.xyz/post/37815572

This is extremely interesting. How many magazines and newspapers are digitized in the way you can analyze them like that? This is a simple word-based analyze, also those texts can be enriched with metadata, e.g. mentions of people can be marked with their identifiers in Wikidata.

I was going to remind of how good “filtered keywords” function is, but suddenly it struck me I shouldn’t have seen this post in the first place. Apparently, I saw it because my block list had a flaw.

I thought this was a normal coding. Then how do you call those who heavily rely on google and SO?