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Predicting Hacker News article success with neural networks and TensorFlow

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21–30 of 32 posts

Re: Predicting Hacker News article success with neural networks and TensorFlow

#21
I upvoted this story because the title scored well in the model.

Show/Ask HN seem to do pretty well. I suppose I'd have expected that, given the community feel of the site. I'd say HN is a pretty good place to test a prototype or ask for guidance, and overall people do seem to constructively and thoughtfully try to help each other out.

Re: Predicting Hacker News article success with neural networks and TensorFlow

#22
post #15
post #14

Earlier quoted context omitted.

"YC YC YC YC YC" gets you 99.9%!! edit: "YC YC YC YC YC YC" -> 100%

"YC YC YC Rust Rust" is also 100% :). Gotta have some Rust.

"YYYYYYYYYYYYYYYYYY YC YC YC YC YCCCCCCCCCCCCCCCCCCCCCCCCCCCC RUST" is also 100% with 0% flag probability :)

Re: Predicting Hacker News article success with neural networks and TensorFlow

#23
I did something similar ("50 terms most predictive of a submission making it to the front page") some time ago: https://news.ycombinator.com/item?id=10893677 .

One realization was that it was easier to predict if a title/keyword would NOT make it to the front page than if it would. That is, it's clearer what to avoid (startup, app, business, product, mobile, marketing, etc) than what to do.

Re: Predicting Hacker News article success with neural networks and TensorFlow

#24
post #18

Predicting the success of comments is way easier. Just lean left-wing for positive points and right-wing for negative points. I have been testing this myself for a while.

I agree that HN is more likely filled with Libertarians than liberals. Also, as many are INTJ, saying just the garbage INTJs like hearing will get you more love.

Re: Predicting Hacker News article success with neural networks and TensorFlow

#25
Why do people assume posts on the front-page are driven fully automatic + some secret juicy for points and what have you?

Why wouldn't YC just use a human (or two) to bump/nudge posts they would like to climb or expose according to their agenda/internal policy?

It you monitor the hot page, there is a clear political bias, topical bias, as well as temporal peaks of movements/ranking indicating that would be the case - too complex for any ML currently to predict. Just my 2 cents.. keep it simple.

Re: Predicting Hacker News article success with neural networks and TensorFlow

#26
post #25

Why do people assume posts on the front-page are driven fully automatic + some secret juicy for points and what have you? Why wouldn't YC just use a human (or two) to bump/nudge posts they would like to climb or expose according to their agenda/internal policy? It you monitor the hot page, there is a clear political bias, topical bias, as well as temporal peaks of movements/ranking indicating that would be the case -…

HN is a hive mind that has some complex transfer function. This project is experimentally (using past data) determining that transfer function.

Re: Predicting Hacker News article success with neural networks and TensorFlow

#28
post #6

Pretty interesting : The highest score I've been able to find right now, apart from the extreme examples, is 49.6% success, 5.3% flag ... with the title "Predicting Hacker News article success with neural networks and TensorFlow". Did the author chose that title on purpose? :) It's fun to try a title, and then add "Ask HN:" or "Show HN:" in front of it and see the probability change dramatically, or remove the (YC ..…

Try 'YC has died'.

Re: Predicting Hacker News article success with neural networks and TensorFlow

#30
post #22
post #15

Earlier quoted context omitted.

"YC YC YC Rust Rust" is also 100% :). Gotta have some Rust.

"YYYYYYYYYYYYYYYYYY YC YC YC YC YCCCCCCCCCCCCCCCCCCCCCCCCCCCC RUST" is also 100% with 0% flag probability :)

YC YC YC YC YC YC golang is better thanRust

100% :)

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