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Hacking Hacker News Headlines (metamarketsgroup.com)
275 points by th0ma5 on May 5, 2011 | hide | past | web | favorite | 34 comments



So the optimal hacker news headline is as follows:

Why showing the future is essential to acquiring data

Noted.


Showing why acquiring data is essential to the future

I have a feeling that headline wouldn't do all that well, but it does seem to be keeping with google's culture.


Testing your hypothesis here - http://news.ycombinator.com/item?id=2520098


I'm not sure if I'm being naive, but does the '|' and the '-' have some sort of NLP significance?

From the article, what's the difference between "data |" and "data -" ?


I think '|' denotes the beginning or the end of the title, and '_' denotes a wildcard.


I feel like you should work in absolute points-space, rather than rank-space.

Also, no clue if the factors you pulled out are orthogonal.


True. But credit where credit is due. Very cool analysis for a throwaway blog post specifically manufactured to garner karma.

Only thing I'll add as a data critique, the negative factors are reported as things to avoid. But, in fact, all of the reported on titles actually made it onto the Hacker News front page (1). There are an awful lot of submissions that never make it that far. In fact, the significance of the findings indicate that those terms make it onto the front page A LOT (2). I don't think the negatively correlated terms should necessarily be viewed as failures. Just less successful. My own suspicion is that those titles do draw eye-balls, but someone using titles like those is also likely to be kind of a bad writer, preventing those stories from getting upvotes. It would be very hard to prove a correlation between quality of title and quality of writing, though.

(1) I believe. Hard to tell from the post.

(2) Otherwise there wouldn't be enough data for them to be significant.


It would be very hard to prove a correlation between quality of title and quality of writing, though

Especially since the author of the linked article isn't always the one who submits the article and therefore gets to choose the HN title.


The point of the article was as I understand it, which influence the title has independent of the data. Stuff like quality of writing will just be noise that does not matter anymore as long as you have enough data.


They're not exactly in rank-space .. they discretize to the binary variable whether or not an article made it into the top-20, then use logistic regression to model that. so the coefficients are in log-odds space of that indicator


Yeah, but instead of looking at if something got into the top-20 (binary 1/0), he's saying that they should have modelled the absolute score of an article. This would give you a way of weeding out the cruft that hits the front page and disappears quickly.

Another way of looking at it would be the amount of time that a post spends on the front page.


We spent a little time modeling various transforms of absolute score. The top features are essentially the same, but the coefficient variance is a lot higher. We're also interested in modeling rank or mindshare "stickiness" -- some articles remain in higher spots longer than others.


And, the best headline for Hacker News is a headline about hacking Hacker News Headlines :) way to put the research to good use...


The submission should have been titled:

"Essential Lessons Showing How to Hack Hacker News with Data Visualization"

and it will be ranked #1 in no tim... never mind :D


According to itself, it would have been better if it was shorter: "Hacking Hacker News" or even yet, "Hacking HN"


As a counter-point...

This headline uses none of the hacks described in the article, yet it is ranking quite well.

Perhaps people should focus on letting the content speak for itself rather then using tricks like this?


It does, however, use the trick of being alliterative—which an N-gram analysis of terms will miss.


it's short, which has one of the hacks


64% isn't the greatest accuracy, but you guys were transparent about everything and the numbers look legit. Awesome job putting this together!


Very nice, but the analysis seems to assume that HN rank is determined by the headline and not by the content. (More precisely: for the analysis to give useful guidance to would-be HN headline writers, it needs not to be the case that content features correlated with headline features make a big difference to HN rank.)

My proposal for a good headline according to the numbers in this article: Showing why impossible future controversy survived the problem could hire data. Score: 1.3 (could) + 1.2 (problem) + 1.3 (survived the) + 1.0 (controversy) + 0.9 (impossible) + 0.7 (why ___ future) - 3.3 (11 words) + 2.6 (showing) + 0.5 (hire) + 1.9 (data [END]) = 8.1. For comparison, Why showing the future is essential to acquiring data gets 1.4 (essential) + 0.7 (why ___ future) - 2.7 (9 words) + 2.6 (showing) + 1.7 (acquiring) + 1.9 (data) = 5.6 -- except that it doesn't really get the points for "essential" (not at start) or "why ___ future" (two words in between) or "acquiring" (not in second place, word isn't quite right). Of course my headline has the little drawback of being total nonsense.


Great -- I'm hoping L1-regularized logistic regression will become the standard first thing to try in these quick-n-dirty "predict response variable from text" experiments. That's our approach too. (I assume this is L1 or similar since you mention regularization causing feature selection.)

[[ Edit: deleted question about what 'k' is for the discretized 1{ rank <= k } response. It's mentioned in the article ]]


yeah pretty strong l1--most features were 0. we binarized rank on I_{rank<=20}. it turns out there are tons of articles beyond the first page that stay low forever. check out the interactive viz vad made: http://hn.metamx.com (warning 2.6MB compressed js ahead)


Another question, how are standard errors calculated? I assume they're not from the bootstrapping since the p-values clearly aren't from the standard errors ( +/- 1.96*se is crossing coef=0 for several cases but with small p-values). The other way I would think to get p-values would be the percentage of bootstrap replicates that have (coef==0). But for only 20 replicates you're stuck with p=0 or p=0.05.

I'm genuinely curious how to do coef significance testing for L1-regularized models. I once saw someone ask this at a Tibshirani talk and he said "oh we have no idea, we've resorted to the bootstrap before".


to be honest we just recorded the coeff values for each replicate and did the bootstrap variance calculation.

% of replicates with (coef==0) is potentially much more clever, especially since that's the test we want to perform anyway. i'll run that over the data and see what changes.


I think the question is these don't look like NormalCDF(coef/se) p-values given the coef and se you report. They tend to be too small.

From a frequentist perspective, counting zeroes don't make much sense because under the null of coef=0 there is still a chance you don't estimate coef=0, even after regularization.


    I think the question is these don't look like NormalCDF(coef/se) p-values given the coef and se you report.  They tend to be too small.
right that's my question


interesting yeah some of them definitely don't look right. the output is from scipy's stats.ttest_1samp


It'd be interesting to see how the domain shown next to the title factors into this too. Seems like everything from GitHub always does very well.


I though this problem was with Digg, but I've experienced same with my submissions. It's funny that people judge content by headlines, we need a better way.


There is: delegate.

Find someone who reads Hacker News [1] and blogs. Subscribe to their RSS feed. There's a trick to [1], no doubt. But for most people the time savings far outweigh the occasional mismatch between your interests and the delegate's.

[1] For the same types of articles you do, ideally.


That's exactly what I do, though I don't read HN every day I have a script off in the cloud looking at the comments RSS feed, showing me every article that certain people comment on. As a quality filter, I find it better than the front page.


Is that script on GitHub, by any chance? Would love to have a look at it.


Curious behavior arises from HN's Feed inside Google Reader with "Sort by Magic" turned on... it seems to keep the good stuff towards the top, but anything really spammy and sensational occasionally gets the top place (so watch out for anything hitting #1 in there suddenly), but then you tend to miss some of the more obscure goodies, which arguably I miss from time to time anyway. Still, it is a curious different ranking, probably mostly driven by Google Reader "likes" and sharing.


also cleaning up the graph somehow would be great




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