"Rust Rust Rust Rust Rust" seems to be the optimal number of Rusts with 97.6% success probability.<p>EDIT: This beats it with 99.4%:<p><pre><code> " Rust Rust Rust Rust Rust "</code></pre>
Interesting article.<p>Ignoring the outlier, stories have at best a 1 in 3 chance of succeeding on HN. This means 2/3 of the interesting stories passing through HN are lost, and I could potentially triple my high quality reading material. It is amazing how much good content there is out there that I will never find.
I see what you did there with your own title: <a href="https://imgur.com/a/SthQV" rel="nofollow">https://imgur.com/a/SthQV</a>
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".<p>Did the author chose that title on purpose? :)<p>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 ...) from the extreme examples and see the prediction change.
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 did something similar ("50 terms most predictive of a submission making it to the front page") some time ago: <a href="https://news.ycombinator.com/item?id=10893677" rel="nofollow">https://news.ycombinator.com/item?id=10893677</a> .<p>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.
Why do people assume posts on the front-page are driven fully automatic + some secret juicy for points and what have you?<p>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?<p>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.
I upvoted this story because the title scored well in the model.<p>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.
I absolutely love that the author includes both a live demo and an explanation of how they did it! Bravo.<p>Although, something seems odd when whitespace effects the score. It may have been a good idea to normalize the whitespace.
This was a very interesting read though most of the article simply went over my head (because I'm just a lowly web dev). What would be the shortest path to learning the things described in this article?
"Predicting Hacker News article success with neural networks and TensorFlow" has success probability of 49.6%. I am wondering if OP tried several options to come up with this title.
This is a neat tool! I had too much fun identifying minimal titles, particularly my coming post "(YC S0000009)" with 99.3% probability of success and a 0% flag probability.
I love the way putting in a title gives a score, and then putting a space on the end sometimes changes it. Sometimes for the better, sometimes for the worse.<p>So: Consider the trailing space ...