Using Deep Learning at Scale in Twitter’s Timelines
blog.twitter.com
blog.twitter.com
Maybe it's optimised for rage which drives more page refreshes? Twitter started to look like it's really dying, desperately trying to squeeze last ad juices from its userbase.
0. The fact is, I want a algo timeline. There are far too many posts in a day for me to read everything.
1. People made the same complaints about Facebook's algorithmic feed. Very few users complain about that anymore (outside of HN, I guess). If anything, without FB's algo feed, users would be overwhelmed with a flood of posts. FB will surface very granular posts like a friend liking something, so we are talking about multiple thousands of potential news feed posts per day. And if FB were to take out that granularity, that means potentially missing tens of highly relevant posts a day.
2. Obviously I don't have any inside info, but I guarantee you Twitter split tested the algorithmic timelines and is also measuring user engagement and they wouldn't have kept this highly complex feature (with probably a whole engineering team dedicated to it) unless there was a big boost.
3. Q1 of 2017, the first quarter they rolled out the algo timeline, user growth accelerated.
4. I don't know a single person with experience growing/measuring consumer web products who thought the algo timeline was a bad idea. It is such an obvious piece of low-hanging fruit.
Not a Twitter or FB user, but do visit G+ once a week or so for a few minutes. Or did. Now everything is out of order and a jumble of crazy including surfacing my own long past contributions over more recent ones. I doubt I'll be back. I can't speak directly to Twitter timelines but if it's anything similar I wouldn't use it.
There may be a point to reducing noise and promoting relevant feeds. Maybe it has to happen to keep the view-able volume to a manageable level. Maybe. Or maybe the strategy was get everyone in the door and hooked, then start controlling what they see. And that is hugely valuable to both commerce and government.
A/B testing aside, my prediction is that removing user control over what is seen backfires hugely and may provide an opening for competing services. Until they manage to lock the internet down to 4 approved services anyway. Which probably has at least been considered.
"Impact on people using Twitter is typically measured by running one or more A/B tests and comparing the results between experiment buckets. The set of metrics we use here usually relate more directly to usage and enjoyment of Twitter."
People have accepted these timelines as a fait accompli but that's all.
You are deeply incorrect about that, and the attachment to chronological timelines is probably what doomed Twitter for so long.
I absolutely want a relevant timeline. So do most people. Most people don't want to spend all day glued to Twitter to keep up with ongoing conversations. They want to be able to quickly open it and find content which intrigues them and doesn't confuse them.
Twitter listening too much to power users for years and years is what kept them from building products the masses could love.
It's insane to me that people on HN think chronological timelines are good. Should I have to read hundreds or thousands of posts a day (which is definitely how much content my friends produce) just to find the 5 or 6 which matter to me. Why wouldn't I want a machine to do that sorting for me?
I want that
Seems like they could please the "I want to see relevant" and the "I want to see most recent" camps.
Does that not work?
Very curious to know what this metric would be. Maybe some combination of likes, retweets, and viewing time? Or maybe including a general liking term (i.e. liking a person's tweet gives a small boost to all their tweets).