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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 mass

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.

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.