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Predicting Popularity of Posts on Hacker News (2016) [pdf]

45 points by adgasf 9y ago

deleted 9y ago
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hanoz 9y ago
I predict this post would be a lot more popular if its title ended with "with machine learning".
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patmcguire 9y ago
It isn't too particular to hacker news, although I did like the tables, definitely recognized the topics. SELECTED TOPICS EXTRACTED BY NMF Topic 1 js javascript node react component python config browser Topic 2 pple iphone ipad fbi mac cook sir headphone watch steve Topic 3 invest startup fund venture capital entrepreneur tech raise Topic 4 game pokemon vr nintendo virtual oculus headset steam Topic 5 email clinton website inbox gmail slack spam campaign Topic 7 tesla vehicle musk autopilot elect autonomous battery road Topic 8 facebook twitter advertise zuckerberg messenger snapchat Topic 11 docker deploy cloud kubernet swarm cluster microservices Topic 12 ai intelligence algorithm neurual deep alphago deepmind Topic 14 uber ride city lyft tax vehicle hail self did transport kalanick
AndrewKemendo 9y ago
The content of a news post is the most powerful source for predicting its popularity. I would like to believe this is true, and I would guess that it's probably more true on HN than elsewhere, but I would really like to see them dive more into how they came to this conclusion. I think the moderator led title editing makes this comparison more difficult (not bad necessarily) but it would be worth comparing popularity:content to popularity:title to have a more rigorous understanding. Overall I applaud the approach they took. It seems however that the conclusion/output doesn't give better insight into the parameters which drive post popularity on HN, over previous approaches which seek to understand the underlying voting mechanism [1]. While different they are trying to basically understand the question "what makes for a popular post on HN." Modeling the sentiment of a community under a formal mechanism (vote algo) is incredibly difficult to do correctly, so again kudos for the work and it seems like it's something that could be built on. [1]http://sangaline.com/post/reverse-engineering-the-hacker-news-ranking-algorithm/ http://sangaline.com/post/reverse-engineering-the-hacker-new...

45 points by adgasf 9y ago

deleted 9y ago

[deleted]

hanoz 9y ago

I predict this post would be a lot more popular if its title ended with "with machine learning".

deleted 9y ago
[deleted]

patmcguire 9y ago

It isn't too particular to hacker news, although I did like the tables, definitely recognized the topics. SELECTED TOPICS EXTRACTED BY NMF Topic 1 js javascript node react component python config browser Topic 2 pple iphone ipad fbi mac cook sir headphone watch steve Topic 3 invest startup fund venture capital entrepreneur tech raise Topic 4 game pokemon vr nintendo virtual oculus headset steam Topic 5 email clinton website inbox gmail slack spam campaign Topic 7 tesla vehicle musk autopilot elect autonomous battery road Topic 8 facebook twitter advertise zuckerberg messenger snapchat Topic 11 docker deploy cloud kubernet swarm cluster microservices Topic 12 ai intelligence algorithm neurual deep alphago deepmind Topic 14 uber ride city lyft tax vehicle hail self did transport kalanick

AndrewKemendo 9y ago

The content of a news post is the most powerful source for predicting its popularity. I would like to believe this is true, and I would guess that it's probably more true on HN than elsewhere, but I would really like to see them dive more into how they came to this conclusion. I think the moderator led title editing makes this comparison more difficult (not bad necessarily) but it would be worth comparing popularity:content to popularity:title to have a more rigorous understanding. Overall I applaud the approach they took. It seems however that the conclusion/output doesn't give better insight into the parameters which drive post popularity on HN, over previous approaches which seek to understand the underlying voting mechanism [1]. While different they are trying to basically understand the question "what makes for a popular post on HN." Modeling the sentiment of a community under a formal mechanism (vote algo) is incredibly difficult to do correctly, so again kudos for the work and it seems like it's something that could be built on. [1]http://sangaline.com/post/reverse-engineering-the-hacker-news-ranking-algorithm/ http://sangaline.com/post/reverse-engineering-the-hacker-new...