4 ms·

I'd be interested to learn how you think kmeans helps either diversity or hate speech detection. I don't think it helps with any of either, as people's behavio

I'd be interested to learn how you think kmeans helps either diversity or hate speech detection. I don't think it helps with any of either, as people's behavior, the context, the language used and sensibilities change across cultures and over time. What you call hate speech now may not be in the future and may not have been in the past. The best you could hope for is creating a fuzzy representation of the most visible problematic behavior and try to outrun model-world dealignment by constantly updating it.

pjkundert 4y ago
The clustering responds to which tweets are up- and down-voted by the agent. And, the agent is moved closer to the clusters they like tweets from, and further from the clusters they dislike tweets from. The more diverse your likes (likes tweets in many other clusters), the broader and less restrictive your cluster weighting is -- the more variety you see. The more restrictive your selection, the less variety. You decide how much of an "echo chamber" you're in. But, if you don't like $BAD_THING, and you downvote enough tweets of $BAD_THING, the less you'll see tweets by people in groups where they like $BAD_THING.
- uniqueuid 4y ago
  Recommender systems for news and social media have been tried, and I don't doubt that there may be niches where they succeed. But large platforms have two massive problems: (1) what people want is popular content, sometimes even content they would downvote. This destroys clustering by creating fuzzy centralized bridges and erodes the usefulness of recommenders (2) people over time have lost trust and interest in highly personalized feeds, see facebook or the revolt against instagram's feed sorting. Two cases that seem to work for now are music recommendation and tiktok. I'm not holding my breath though, because spotify might end up driving people away with too many monetized podcasts and tiktok could succumb to the generational migration. But we'll see!
  - concinds 4y ago
    Sentiment analysis can solve the first. If the algo notices that someone only engages negatively with another cluster, cut it off; they're not entitled to pollute that. And the second point was a mostly abstract intellectual debate in the early 2010s, but people have proven that they absolutely prefer to stick with their own, and have close-to-zero tolerance for dissent or disagreement (see, "the hivemind"). Twitter is far more prone to this than Facebook, since it's founded on communities (i.e. clusters) rather than just friends. Twitter just needs to stop putting junk into people's feeds; and silo them better to reduce harassment. That would also reduce polarization, since a yuge cause for polarization (far-right, antifa) is a knee-jerk reaction to the very worst content from the other side. Stop promoting that, and you'll stop the "Brainwashing Of My Dad" effect (the film), where a conservative from Texas gets upset because of bathroom laws in California, or where a liberal gets upset because of a few 4chan trolls. It's the psychological phenomenon of "enmeshment": remove the boundaries between people, and you make their relationship very toxic and conflictual. Enforce better boundaries, and their relationship will be far healthier. Twitter promoted the former; Musk can avoid most of the upcoming "hell" being predicted by doing the latter.
    - naet 4y ago
      I think polarization is likely to be much more extreme if you silo people off into like minded groups.
      - pjkundert 4y ago
        People will get bored, eventually, slumbering in a Pablum of like-mindedness.
- PuppyTailWags 4y ago
  How does this work with things like "ratio-ing" or "dunking" or libsoftiktok that intentionally take clusters of media from one group and shoving it into their group explicitly to hate them?
  - pjkundert 4y ago
    I'm not sure that shaking your head and laughing at someone's crazy-talk is "hating" them... Perhaps that's part of the problem. If you don't want people chuckling at you, don't talk crazy. If you really, really get annoyed at something and mash the downvote button, it'll eventually go away, and you'll be left in your warm, cozy bubble. Everyone wins!

pjkundert 4y ago

The clustering responds to which tweets are up- and down-voted by the agent. And, the agent is moved closer to the clusters they like tweets from, and further from the clusters they dislike tweets from. The more diverse your likes (likes tweets in many other clusters), the broader and less restrictive your cluster weighting is -- the more variety you see. The more restrictive your selection, the less variety. You decide how much of an "echo chamber" you're in. But, if you don't like $BAD_THING, and you downvote enough tweets of $BAD_THING, the less you'll see tweets by people in groups where they like $BAD_THING.

uniqueuid 4y ago
Recommender systems for news and social media have been tried, and I don't doubt that there may be niches where they succeed. But large platforms have two massive problems: (1) what people want is popular content, sometimes even content they would downvote. This destroys clustering by creating fuzzy centralized bridges and erodes the usefulness of recommenders (2) people over time have lost trust and interest in highly personalized feeds, see facebook or the revolt against instagram's feed sorting. Two cases that seem to work for now are music recommendation and tiktok. I'm not holding my breath though, because spotify might end up driving people away with too many monetized podcasts and tiktok could succumb to the generational migration. But we'll see!
- concinds 4y ago
  Sentiment analysis can solve the first. If the algo notices that someone only engages negatively with another cluster, cut it off; they're not entitled to pollute that. And the second point was a mostly abstract intellectual debate in the early 2010s, but people have proven that they absolutely prefer to stick with their own, and have close-to-zero tolerance for dissent or disagreement (see, "the hivemind"). Twitter is far more prone to this than Facebook, since it's founded on communities (i.e. clusters) rather than just friends. Twitter just needs to stop putting junk into people's feeds; and silo them better to reduce harassment. That would also reduce polarization, since a yuge cause for polarization (far-right, antifa) is a knee-jerk reaction to the very worst content from the other side. Stop promoting that, and you'll stop the "Brainwashing Of My Dad" effect (the film), where a conservative from Texas gets upset because of bathroom laws in California, or where a liberal gets upset because of a few 4chan trolls. It's the psychological phenomenon of "enmeshment": remove the boundaries between people, and you make their relationship very toxic and conflictual. Enforce better boundaries, and their relationship will be far healthier. Twitter promoted the former; Musk can avoid most of the upcoming "hell" being predicted by doing the latter.
  - naet 4y ago
    I think polarization is likely to be much more extreme if you silo people off into like minded groups.
    - pjkundert 4y ago
      People will get bored, eventually, slumbering in a Pablum of like-mindedness.
PuppyTailWags 4y ago
How does this work with things like "ratio-ing" or "dunking" or libsoftiktok that intentionally take clusters of media from one group and shoving it into their group explicitly to hate them?
- pjkundert 4y ago
  I'm not sure that shaking your head and laughing at someone's crazy-talk is "hating" them... Perhaps that's part of the problem. If you don't want people chuckling at you, don't talk crazy. If you really, really get annoyed at something and mash the downvote button, it'll eventually go away, and you'll be left in your warm, cozy bubble. Everyone wins!