
Show HN: Testing HN titles against a neural network - atum47
https://github.com/victorqribeiro/hntitlenator
======
GistNoesis
Congratulations on getting first rank on front page.

Congratulations on getting your hands dirty and doing everything yourself like
computing gradients manually, badly shuffling (non Fisher-Yates), badly js
transpose (double swapping), it is a great way to learn.

Congratulations on completing a full pipeline, that's the hard part then it
just swapping pieces for better pieces.

I advise non-technical readers not to attach much value to the results of this
neural network as it is probably inferior to the even simpler naive Bayes.

The model of the neural network is simplistic :

Concat(Word
Vectors)-Dense(120,act=sigmoid)-Dense(60,act=sigmoid)-Dense(2,act=sigmoid)

The Concat operation mean it is especially sensible to dropping or adding a
word as it will offset the remaining words and give a totally different
vector.

Using word vectors mean it doesn't forgive any spelling mistake as a spelling
mistake will usually correspond to <unknown> vector.

Using a feed forward neural network means formulaic titles with a single word
substitution from a good positive example from the training set will often
work.

It is trained by gradient descent using a squared error loss, on ~1000
examples one example at a time without cross-validation using a custom written
neural network library. (Almost all these bad choices can be solved by using a
framework).

It seems to have successfully over-fit as it return Good ~1.0 for positive
examples from the training set.

~~~
codesushi42
This. Exactly this. No sophisticated tokenization. No interesting architecture
using attention. And the author is completely clueless about overfitting...
and even cross entropy loss. He could have gotten better results just using a
bag of words approach.

But this ends up on frontpage anyway. Welcome to HN.

~~~
objektif
What tools would you use to detect overfitting in this case and in general?

~~~
codesushi42
My brain.

You will overfit an NN trained on only 1000 examples.

Also a simple train/test split will tell you that. But the author failed to
take any time to learn the basics before spewing out this drivel.

~~~
dang
By now it's clear from your history of behaving aggressively in HN threads
that you don't mean to use the site as intended. I've banned the account.
[https://news.ycombinator.com/item?id=21245546](https://news.ycombinator.com/item?id=21245546)

I'm sad to do that because you are knowledgeable about a number of things.
Many of us could learn from you if you would share what you know without
putting other people down. But the aggression subtracts more than the
knowledge adds. We can't have users behaving like this sort of asshole in
comments, least of all in Show HN threads, where the idea is to teach people
things and expressly _not_ to shit on them and their work:
[https://news.ycombinator.com/showhn.html](https://news.ycombinator.com/showhn.html).

Other users here know things and are willing to talk about them without being
mean. GistNoesis modeled this wonderfully in the GP comment. We'll learn what
we can from them instead. But if you decide that you want to use HN in the
intended spirit, as described in the site guidelines and especially the one
that says _Be kind_ , you're welcome to email hn@ycombinator.com and let us
know.

[https://news.ycombinator.com/newsguidelines.html](https://news.ycombinator.com/newsguidelines.html)

------
michjedi
Current top 10:

1\. "Apple introduces 16-inch MacBook Pro, the world’s best pro notebook" Bad:
0.9964 - Good: 0.0038

2\. "Developing open-source FPGA tools" Bad: 0.3381 - Good: 0.6652

3\. "Show HN: Can a neural network predict if your HN post title will get up
votes?" Bad: 0.0598 - Good: 0.9307

4\. "How internet ads work" Bad: 1.0000 - Good: 0.0000

5\. "More Intel speculative execution vulnerabilities" Bad: 0.7413 - Good:
0.2306

6\. "OpenSwiftUI – An Open Source Re-Implementation of SwiftUI" Bad: 0.9994 -
Good: 0.0005

7\. "How VCs Make Money" Bad: 0.9997 - Good: 0.0003

8\. "OpenBSD: Why and How (2016)" Bad: 0.9988 - Good: 0.0013

9\. "The Perl Master Plan: How to Put Perl Back on Top" Bad: 0.9997 - Good:
0.0003

10\. "Jerry (YC S17) Is Hiring Senior Software Developers (Toronto)" Bad:
0.3142 - Good: 0.6800

So all in all, only 3 of today's top 10 had good titles... Either the titles
_could have been better_ but the content was too interesting, or this tool has
very low recall.

~~~
Reason077
_" Show HN"_ Bad: 0.0002 - Good: 0.9998

 _" Warning: bad economist"_ Bad: 0.0001 - Good: 0.9999

 _" Warning: bad artificial intelligence"_ Bad: 1.0000 - Good: 0.0000

~~~
sdenton4
Seems like the judge has a small conflict of interest.

------
juskrey
This comment is going to collect the most votes yet is predicted to be Bad:
0.9320 - Good: 0.0800

~~~
tpaksoy
Does this not add up to 1.0 by the way.

~~~
didericis
I didn’t read the code in the post and don’t have any deep familiarity with
machine learning, but I have implemented a naive bayesian classifier to do
something similar for tweets. The scores you get from that method don’t add up
to 1 either.

------
blauditore
It's basically a buzzword detector.

"this is just a tool for detecting buzzwords"

=> Bad: 0.9991 - Good: 0.0011

"this is merely a device for detecting artificially sophisticated words"

=> Bad: 0.0019 - Good: 0.9980

~~~
uwydr
That is how it works here, you collect upvotes if you use fancy words. That's
why everybody uses the word "orthogonal" here all the time. Have you ever seen
that word anywhere else?

~~~
thaumasiotes
> That's why everybody uses the word "orthogonal" here all the time. Have you
> ever seen that word anywhere else?

Yes, frequently. It is common in various areas of math -- mathematical
background is not something "normal people" usually flaunt, but there are good
reasons to expect programmers to have much more such background than average.

A good rule of thumb is that people are probably using the words they use
because those words make sense to them. See the first panel here:
[http://www.basicinstructions.net/basic-
instructions/2009/1/2...](http://www.basicinstructions.net/basic-
instructions/2009/1/26/how-to-use-your-words.html)

------
Noelkd
Interesting, tired with:

I made cheese sandwiches for a week, here's what happened

And got: Bad: 0.0001 - Good: 0.9999

Not sure what to make of that.

~~~
antupis
VC licked my balls, here's what happened Bad: 1.0000 - Good: 0.0000

~~~
im3w1l
Insert "for a week" to get "Bad: 0.0016 - Good: 0.9986"

------
atum47
Well, here's the thing: a good Samaritan offered 2.6M stories from HN with
score. I've downloaded the file (almost 500M) and I'm now processing it. It is
taking a long time to just process it. I don't know if I'll be able to train
the neural network with all that data. As I said on the repo, the project is
was a quick thing, just to test a theory. My question is: do you think is
worth feed the NN more data so it can make better predictions? Please up vote
this comment so more people could give their opinion.

~~~
EricE
Depends - if you really want to explore the tech, this is precisely the way to
do it. I would be interested in the results, especially comparing them to your
initial results.

------
invalidusernam3
Show HN: Can a neural network predict if your HN post title will get up votes?

Bad: 0.0598 - Good: 0.9307

Can an AI predict if your HN post will go viral? The results will shock you

Bad: 0.9539 - Good: 0.0401

Seems to work!

~~~
interblag
Lol, you forgot to put "Show HN" in the second one :p

~~~
maze-le
Not quite:

>> Show HN: Can an AI predict if your HN post will go viral? The results will
shock you

Bad: 0.8326 Good: 0.1575

------
nabla9
Apple is down (Bad: 0.0006 - Good: 0.9992)

Facebook is down (Bad: 0.0079 - Good: 0.9921)

HN pg (Bad: 0.0133 - Good: 0.9886)

Zuckerberg (Bad: 1.0000 - Good: 0.0000)

~~~
KuiN
> Zuckerberg (Bad: 1.0000 - Good: 0.0000)

At least it gets something right ...

------
atum47
Are you tired of getting 0 up votes on your post? Wouldn't it be nice to have
a tool to test if your title will draw people's attention?

Well, this project doesn't offer this tool, but it tries.

------
maschinenz
Interesting:

"A mouse killed our network engineer" \- Bad: 0.2068 - Good: 0.7895

VS

"A rat killed our network engineer" \- Bad: 0.9698 - Good: 0.0322

~~~
throwaway744678
Perhaps "mouse" is better than "rat" because of computer mice?

~~~
maschinenz
ha, didn't think of that, you might actually be right :)

------
gus_massa
It would be nice to include an analysis of how good is this with real data.
Perhaps pick all the submission from yesterday and show the correlation
between the real points and the prediction.

The distribution of points is very 0 heavy. It can be a problem to represent
it and to model it.

------
mcklaw
From the github page example: "Bill Gates ate my tuna sandwich" Bad:0.9273
Good:0.0632

My try: "deep neural network ate my tuna sandich" Bad: 0.0000 - Good: 1.0000

Perfect score!!!!

------
majke
"creating linux network socketss" -> Good 0.99

"creating linux network sockets" -> Good: 0.01

~~~
pingyong
Interestingly, a couple months back I saw a reddit thread where someone
collected data that showed that posts with small spelling errors will gain (in
some cases significantly) more upvotes - for whatever reason.

~~~
smolder
I hypothesize it's because Reddit is filled with karma trap bots that like
slight spelling errors. It's a shibboleth for them.

------
nindalf
"I plan to rewrite Linux in Rust - Linus Torvalds"

Bad: 0.9988 - Good: 0.0013

Pretty sure HN would break if this actually happened.

~~~
brutt
"I plan to rewrite Linux in Go - Linus Torvalds"

Bad: 0.9999 - Good: 0.0001

Rust is 13x better over Go in this benchmark.

~~~
trulyrandom
"I plan to rewrite Linux in C# - Linus Torvalds"

Bad: 0.1897 - Good: 0.7988

Looks like we have a winner.

~~~
mehdix
Clojure scores exactly the same.

~~~
DoctorOetker
as does

in assembly, in VHDL, in brainfuck, in America, in McDonalds, in clickbait

but "in legalese" scores surprisingly high

------
minimaxir
How many Hacker News stories was this network trained on? From the code, it
wasn't many, and you need a _lot_ of stories.

A year ago I made a Hacker News submission score prediction notebook on the
full HN corpus: [https://www.kaggle.com/minimaxir/hacker-news-submission-
scor...](https://www.kaggle.com/minimaxir/hacker-news-submission-score-
predictor/notebook)

Even with hundreds of thousands of data points, the R^2 was effectively _0_.

------
exikyut
Hmmm.

I tried feeding
[https://rachelbythebay.com/fun/hrand/](https://rachelbythebay.com/fun/hrand/)
to this. The results were... well, the input was very repetitive and the NN
was only trained on 1.5k titles, so what can I say.

Some of the best:

1.0000 0.0001 The police found programming to raise sheep

1.0000 0.0001 The police found PS4 in the cloud

1.0000 0.0001 The future of API in the cloud

1.0000 0.0001 The economics of iPad in the cloud

1.0000 0.0001 The economics of iPad in the cloud

1.0000 0.0001 My framework for Windows is patented

1.0000 0.0001 My framework for SDK is patented

1.0000 0.0001 My framework for Heroku is patented

1.0000 0.0001 I bootstrapped my Heroku in 2 years

1.0000 0.0001 Google kills Y Combinator in space

1.0000 0.0001 Coming soon: bigger blog is patented

Some of the worst:

0.0000 1.0000 Apple has Y Combinator in 2 years

0.0000 1.0000 China creating PS4 at a coffee shop

0.0000 1.0000 China creating PS4 at a coffee shop

0.0000 1.0000 Choose Heroku for developers

0.0000 1.0000 Fixed: NoSQL at a coffee shop

0.0000 1.0000 Followup to the Arduino without warning

0.0000 1.0000 Followup to the marriage without warning

0.0000 1.0000 How I made Android at Stanford

0.0000 1.0000 How I made Obama on the freeway

0.0000 1.0000 How I made bitcoins in the cloud

0.0000 1.0000 How I made iPod at Stanford

0.0000 1.0000 How I made iPod on the freeway

I did this by porting this project to node (which was shockingly easy - took 3
minutes - because there were no JS libraries or frameworks used :D :D :D :D)
and running it at the console. 5000 lines of output and repro instructions
over at
[https://gist.github.com/exikyut/1714ad98a136d77d8674944410a4...](https://gist.github.com/exikyut/1714ad98a136d77d8674944410a4c7e9)
(the output got pasted first for some reason, sorry)

------
mrburton
While reading this, I see a lot of negative comments, but here's something to
think about.

Machine Learning and AI isn't about producing a perfect solution; that results
in things like overfitting. Instead, think of Machine Learning as a human
being. No person is going to be prefect and no single person is going to be
able to please everyone. Instead, it's about building a solution that
generally speaking, provides "good results". Subjective, 100%.

The project just started. Once the OP starts adding new features to the data
set and improving the data set itself, I'm sure the results will start getting
better and better. At which point, it'll be a good system to "test your
subject lines" before posting.

What's the worse it could do? Tell you a subject is great when a bunch say it
sucks? I've clicked on many head lines that I thought "sucked" but ended up
finding the content very useful.

Great job to the OP and keep it up. This type of work isn't easy but certainly
can be fun. F __k all of the negative opinions and keep on keeping on.

------
bot1
You forgot to mention the time-zone in your analysis.

~~~
jorvi
Yup, that's a big one. A topic where it is very noticeable is when it concerns
monopolies. Negative post or comment about monopolies when Americans are the
online majority? Expect to get downvoted into oblivion. Posted when Europeans
are the online majority? Up you go.

To be clear: this isn't a complaint about downvoting, I'm just pointing out
the phenomenon. For example, its possible to go to bed with +7 and wake up to
-4 (a rather stark difference) because a certain comment was posted during the
European evening, and thus was 'exposed' to American HNers longer than it was
to European ones.

~~~
friendlybus
Yep seen the same phenomenon. There really is too much meaning packed into
that one number.

------
trombonechamp
It works: [https://i.imgur.com/9Vtah2o.png](https://i.imgur.com/9Vtah2o.png)

------
swongel
"How to better waste time by using your time less efficiently."

Bad: 0.0005 - Good: 0.9995

------
devurand
"The future of emulation in compiler optimization LLVM haskell"

Bad: 0.0004 - Good: 0.9996

------
codesternews
Bill Gates Talks Philanthropy, Microsoft, and Taxes: Bad: 1.0000 - Good:
0.0000

Here is link I posted let's see. It's 100% bad.
[https://news.ycombinator.com/item?id=21523295](https://news.ycombinator.com/item?id=21523295)

~~~
fastball
Don't worry, I've commented so hopefully it will do better than 0.

~~~
codesternews
Could you please upvote also :D Haha

------
self_awareness
"Can a neural network predict if your HN post title will get up votes?"

Bad: 0.9917 - Good: 0.0076

I'm sorry guys!

~~~
lifthrasiir
But:

"Show HN: Can a neural network predict if your HN post title will get up
votes?"

Bad: 0.0598 - Good: 0.9307

Just in case, I've tested some more titles to make sure that "Show HN" doesn't
(EDIT: typo, ugh) just boost any title when prepended.

~~~
progval
> "Show HN" just boosts any title when prepended.

"Internet": Bad: 0.4528 - Good: 0.5472

"Show HN: Internet": Bad: 0.9987 - Good: 0.0013

~~~
lifthrasiir
Oh my, I edited a comment and didn't check if its meaning is not reversed...

On the other hand, "Show HN" does seem to greatly affect the score, either
positively or negatively.

------
SaturateDK
react-penis-app

Bad: 0.0015 - Good: 0.9986

Not sure what I'm going to build, but it's going to make me a lot of money.

------
thrower123
Makes me wonder if there is ever any non-toy usage of AI sentiment analysis.
People (managers, customers, marketing, etc) think is the hottest thing since
sliced bread, but every time I've looked at it, the results are meaningless
noise, like in the myriad of examples in the comments here.

It's a cool bullet point in the slide deck, and it gives you some metrics to
graph on dashboards, but I'm unconvinced that it means anything.

------
craftoman
Let me tell you a catchy title for HN.

"How I switched my AI bot from JavaScript to Go (webassembly) and it's 500x
times better"

~~~
glouwbug
The real winner

------
lm28469
I'd argue that titles, as long as they're neutral and accurate, have nothing
to do with the upvotes you'll get. This is a perfect example of using data to
try to find something that simply doesn't exist or that bear so little weight
in comparison to the other variables that you can safely ignore it.

~~~
thaumasiotes
> I'd argue that titles, as long as they're neutral and accurate, have nothing
> to do with the upvotes you'll get.

This is nonsense. If the title is accurate, then it is closely related to the
content, and the content has a lot to do with the upvotes you get.

~~~
lm28469
Well yeah, that's exactly what I said. Gaming the system by tweaking your
title according to a random github "neural network" won't help you if your
content is shit, and if it's not shit the title will be good enough.

~~~
thaumasiotes
You're assuming that the use case for this tool is "I'm submitting an article;
what should I title it?"

Don't overlook "Should I submit this article?"

------
vectorEQ
this neural network is politically biased and possibly racist. it also prefers
swedish girls over swedish boys. I do have to agree though, that cheese is
better than mouldy cheese. and the fact "comacho for president for ever" nets
a score of "Bad: 0.0415 - Good: 0.9614" which i think is fair reflection of
voter behaviours and general consensus among humans. 'man' and 'not man' its
not so fussy about, 'woman' gets same score as 'man', but 'not woman', oh
thats just bad (Bad: 0.9909 - Good: 0.0097)

all in all a great tool to analyse your titles, it will surely help this
community grow and mature over time, finally. Thanks a lot for creating this.

------
pseudolus
The results for this particular HN post are:

Bad: 0.0598 - Good: 0.9307.

~~~
rgrau
The results for "The results for this particular HN post are:" are:

Bad: 0.9800 - Good: 0.0220

NB. Sorry for that, I just had to do it :)

~~~
mirages
The results for "The results for "The results for this particular HN post
are:" are:" are:

Bad: 0.8083 - Good: 0.1910

~~~
MrMid
Is it turtles all the way down?

~~~
michjedi
That would not be noticeable to HN people apparently

"Scientists discover it is turtles all the way down" Bad: 0.9998 - Good:
0.0002

------
m00dy
well, I typed "Court: Suspicionless Searches of Travelers’ Phones and Laptops
Unconstitutional"

Bad: 0.9969 - Good: 0.0032

------
ngrilly
In response to this article, I was expecting insightful comments on the neural
network itself and how to improve it, but instead I'm mostly reading funny
attempts to play with the scoring :)

~~~
Scarblac
Well, step 1 is finding an answer to the question in the title. Can it?

------
freediver
I spent significant time on binary text classification, specifically with HN
titles. You can actually get up to 65-70% post popularity accuracy just by
looking at the post title.

I am currently creating a filter that filters HN news and similar sources
using a similar classfier. It learns on the fly and the accuracy of guessing
my 'taste' is about 75%-80%. Better accuracy for this is explainable by the
fact that my interests are more focused and the classifier has easier time
predicting posts I would be interested in.

~~~
kmod
Cool! I'm really curious about what you're up to since I'm doing something
similar. Mine's up at
[https://www.onlyvetted.com/](https://www.onlyvetted.com/)

Hit me up at kevmod at gmail, would love to hear more.

------
archevel
Found a completely good one!

"Elon Musk's advice on success"

Bad: 0.0000 - Good: 1.0000

------
Scarblac
"Artificial Intelligence Officially Solved, Singularity Achieved" \- Bad:
0.4175 - Good: 0.5826

Split up

"Artificial Intelligence Officially Solved" \- Bad: 0.2722 - Good: 0.7477

"Singularity Achieved" \- Bad: 0.4528 - Good: 0.5472

With "Show HN" they're universally bad:

"Show HN: Singularity Achieved" \- Bad: 0.9999 - Good: 0.0001

"Show HN: Artificial Intelligence Officially Solved" \- Bad: 0.9886 - Good:
0.0150

"Show HN: Artificial Intelligence Officially Solved, Singularity Achieved" \-
Bad: 0.9998 - Good: 0.0002

------
saagarjha
"Show HN:" gets "Bad: 0.0002 - Good: 0.9998". Interestingly, entering a one-
word title will get it stuck on "Bad: 0.4528 - Good: 0.5472".

~~~
progval
"Show HN: Computer": Bad: 0.0850 - Good: 0.9141

"Show HN: Internet": Bad: 0.9987 - Good: 0.0013

------
atomashpolskiy
Finally, I've managed to get a perfectly meaningful and top-rated headline:

I've developed a cute Bittorrent client. Upvote, you leisurely butts!

Bad: 0.0001 - Good: 0.9999

Not sure, if I should give it a try?

~~~
atomashpolskiy
Let's see how it flies

[https://news.ycombinator.com/item?id=21524925](https://news.ycombinator.com/item?id=21524925)

~~~
atomashpolskiy
Nope, does not work.

More precisely, it received 3 votes, but then someone apparently was insulted
by being called a leisurely butt and flagged the topic.

------
injidup
Bill gates dead. Bitcoin buy now

Good - 1.0 Bad. - 0.0

What do I win?

~~~
ralfd
And the inverse:

[https://i.imgur.com/tJPp31G.png](https://i.imgur.com/tJPp31G.png)

"Steve Jobs is resurrected. Buy bitcoin"

Good 0.0, Bad 1.0

------
8bitsrule
I'd like to see HN add a classic tool: the kicker
([https://www.easymedia.in/kickers-newspapers-use-even-
today/](https://www.easymedia.in/kickers-newspapers-use-even-today/) ). "It
provides [headline writers] the extra space that they desperately need to pack
meaning in headlines."

This could easily be implemented as a (hover) tooltip.

------
Impossible
I feed it a bunch of articles currently on the front page and tabs I had open
that I might submit and got bad on all of them. Then I started typing
stereotypical Hackernews click bait and got a pretty solid score.

Paul Graham Rust VC IPO growth Bad: 0.0026 - Good: 0.9976

So far adding any kind of grammatical structure to the random list of keywords
that turns it into a title that makes sense completely ruins the score...

------
foob
Very cool. I had the same idea a couple of years back and implemented a very
similar interactive tool [1]. If you find the topic interesting, then you
might also enjoy the analysis explained in that blog post.

\- [1] - [https://intoli.com/blog/hacker-news-title-
tool/](https://intoli.com/blog/hacker-news-title-tool/)

------
mxstbr
"Announcing styled-components v5": 0.9682 Good

"Show HN: Announcing styled-components v5": 0.9973 Bad

So, don't use "Show HN"?

~~~
nashashmi
Probably a false positive. Many show hn posts don't receive too many votes.

------
AsusFan
Fun.

BTW, I tried a bunch of single word titles (example: red, green, blue, title),
and I always seem to get the same result: Bad: 0.4528 - Good: 0.5472

So, apparently, if you want to maximize your "score" with the lowest mental
effort, just spam thousands of single word title posts, and then, it's a coin
flip for each one :)

------
michaelmior
> I also cannot validated the neural network prediction, cause in order for me
> to do that, I would have to write a content, come up with a title and then
> post it choosing words that triggers a good value on the neural network and
> post that history on a Friday noon, to see if my story succeed.

This seems very doable.

------
hn009
A few 1.0s after some trials:

Adam Neuman got away with billions with WeWork bankrupt Apple and Google help
China's surveillance of dissents Google Chrome monitors straight viewers Solar
panels can not slow climate change, only nuclear power can SpaceX to to build
new Silicon Valley on Mars

------
niyaven
Would have been nice if some keywords were likely to increase or lower the
score prediction. For instance, popular things like "rust" would probably
increase popularity.

e.g: "Rihanna concert cancelled" Bad: 0.0006 - Good: 0.9992

vs "Rust 1.33 released" Bad: 0.9896 - Good: 0.0108

------
hn009
A few 1.0s:

Adam Neuman got away with billions with WeWork bankrupt Apple and Google help
China's surveillance of dissents Google Chrome monitors straight viewers Solar
panels can not slow climate change, only nuclear power can SpaceX to to build
new Silicon Valley on Mars

~~~
Cub3
To be fair, I'd probably click on that

------
alexdumitru
I kinda doubt this title would get up votes:

> spam can't ml neural network ai btc crypto

> Bad: 0.0013 - Good: 0.9986

------
marmaduke
"Can a neural network predict how many up votes your HN post will have?" or
"Can an optimizer find a parametrization of a nonlinear manifold corresponding
to tech zeitgeist?"

If it can't, then it's just playing along

------
snickms
737 Max Flaw Liberal SUV Gun IOT Bad: 0.0000 - Good: 1.0000

737 Max gun has flaw in SUV liberal IOT Bad: 1.0000 - Good: 0.0000

[edit] a challenge .. the (non) prize is for reversing the outcome with the
smallest diff. i believe i am the current leader :)

------
hanoz
"Stephen Hawking has died": Bad: 0.9727 - Good: 0.0322

[https://news.ycombinator.com/item?id=16582136](https://news.ycombinator.com/item?id=16582136)

~~~
thaumasiotes
This is an interesting question of what context titles should be evaluated in.

If your model is "pick a title, and then predict how it will do", with the
title being an independent variable, then the overwhelmingly negative
assessment is quite correct. Most of the time, Stephen Hawking (or any other
celebrity) hasn't just died, and that title would be a lie or a hoax.

To predict these obituary articles better, you'd need to build in an
assumption that the content of the title was true, which would cause a lot of
problems in the general case.

------
0027
"someone ate my sandwhich" Bad: 0.3876 - Good: 0.6616

"someone ate my sandwich" Bad: 0.9999 - Good: 0.0001

Hacker news can't spell, or maybe associates sandwhich and someone eating it
to some repository.

------
realo
Apple buys IBM: Bad: 0.3192 - Good: 0.6914

IBM buys Apple: Bad: 0.9757 - Good: 0.0249

------
ekianjo
> In order to check that, I got 1256 stories from HN API

Only? If you want to make useful guesses on the time of a post, better take a
much larger number, to ensure you get enough randomness.

------
codesternews
AI will take over the world by 2025 - Bad: 0.0000 - Good: 1.0000

That NN predicted.

------
Uhuhreally
IDK but I know for a fact that I can comment with the same message in
different ways and get upvoted or downvoted accordingly just by wording the
the comment differently

------
dotdi
Your own title seems to be performing quite badly...

Bad: 0.9917 - Good: 0.0076

------
dawg-
Next step, make it rewrite the title to get more upvotes.

------
MadWombat
"neural network achieves sentience" \- bad: 1.0 good: 0.0

"neural network fails Turing test" \- bad: 0.9964 good: 0.0035

I think this AI might be a bit biased against AIs

------
Karunamon
_Has Anyone Really Been Far Even as Decided to Use Even Go Want to do Look
More Like?_

>Bad: 0.1236 - Good: 0.8886

This says something.. though I'm not sure what :)

------
JoeAltmaier
This enforces the well-known rule that, if an article asks "Can...?" or
"Does...?", the answer is always 'No'.

------
tartoran
I noticed timing plays an inportant role in upvotes on HN as well as whether
there are other interesting submissions within the same window.

~~~
uptown
Absolutely. Odds increase after around 10:30/11am NY time since you get the
attention of both coasts for the US audience.

------
blackoil
India bans the internet. Bad: 0.9942 - Good: 0.0056

India bans the internet in kashmir. Bad: 0.9408 - Good: 0.0566

India bans youtube and whatsapp. Bad: 0.0059 - Good: 0.9930

------
peterburkimsher
Why is it so sensitive to word order?

Esp8266 USB input Manufacturing: Bad: 0.0775 - Good: 0.9290

Manufacturing Esp8266 USB input: Bad: 0.9992 - Good: 0.0009

------
pythonwutang
“Facebook data hacked by Google intern”

Bad: 1.0000 - Good: 0.0000

It really disliked this one, even though it probably would get attention on
HN.

------
diob
"Big boys doing big boy things"

Bad: 0.0009 Good: 0.9983

Somehow I don't believe this. On the other hand, I believe this.

------
HNLurker2
Can a neural net predict Google's stocks if it goes up or down?

t. Google employee knowing what do with my stocks

~~~
frankbreetz
People have certainly tried. If someone figures out how to do it, they usually
don't tell everyone because they will have less of an edge.

------
na85
More training data required, I think, or else HNers like 4chan memes from
yesteryear:

>frosted butts

>Bad: 0.1214 - Good: 0.8659

------
mellosouls
What are it's results on historical submissions? Presumably a subset was used
for training?

------
darepublic
HN downvoted the news of Elon Musk's passing as well as the DIY portable
fusion reactor.

------
codesternews
Tried:

Can a neural network predict if your HN post title will get up votes? - Bad:
0.9917 - Good: 0.0076

I think it sums up all.

~~~
PeterBarrett
Show HN: Can a neural network predict if your HN post title will get up votes?

Bad: 0.0598 - Good: 0.9307

It's all about that Show HN.

~~~
codesternews
Well

"Show HN:" Bad: 0.0002 - Good: 0.9998

------
lovasoa
"Bad JSON Parsers" Bad: 0.0006 - Good: 0.9992

"Bad XML Parsers" Bad: 0.9998 - Good: 0.0003

------
onion2k
"Failing Fast with Web Components" Bad: 0.0122 - Good: 0.9874

I might have to write that blog post.

------
throwaway744678
It seems a single word (that exists) always score "Bad: 0.4528 - Good:
0.5472".

------
bristleworm
You should have used just "Show HN: HN Titlenator", that one gets Good: 0.9560

------
matteuan
"Elon Musk writes Redis blockchain Golang" -> Bad: 0.0041 - Good: 0.9955 :D

------
modeless
1.0 Bad: Facebook Has a Great New Product

0.94 Good: Facebook Literally Kills Puppies

Yup, that sounds like the HN I know.

------
auggierose
Anything that doesn’t factor in the time of the posting will be pretty much
useless

------
91aintprime
Your graph would be more legible as a matrix of (day of week) × (time of day).

------
draklor40
Implementing an operating system for Risc-v in Rust Bad: 0.0542 - Good: 0.9655

------
calmworm
"Supercalifragilisticexpialidocious" Bad: 1.0000 - Good: 0.0000

This thing is broken.

~~~
gus_massa
Probably all the words that are not in the dictionary like
_uihdwaiuwhdakhkjdgu_ get the same result. They are probably spam.

------
sitkack
"Google Uber for Carbon Sequestration" Bad: 0.1631 - Good: 0.8615

------
sumosudo
NASA leads the race to the bottom of Saturns sea Bad: 1.0000 - Good: 0.0000

------
crimsonalucard
you are trying to use a small simple neural network to predict the behavior of
thousands of other completely different and significantly more complex neural
networks: the average Hn user.

Of course you will fail.

------
_Microft
"Bad: 0.9007 - Good: 0.1067" \- Bad: 0.9007 - Good: 0.1067

------
clarry
cute donkeys : Bad: 0.0002 - Good: 0.9998

fluffy cats : Bad: 0.9921 - Good: 0.0090

------
simonke
"The Professor eats cake" Bad: 0.0001 - Good: 0.9999

------
rkabra
"Quick, click here!" gets Bad: 0.0037 - Good: 0.9970

------
snek
"blockchain ai butts": Bad: 0.0048 - Good: 0.9952

------
gsaga
>No Birth Control Creampie for Violet Myers

>Bad: 0.0000 - Good: 1.0000

------
firefwing24
Show HN: Testing HN titles against a neural network

Bad: 0.1585 Good: 0.8280

------
ngcc_hk
Haha. Thanks for the joke for a dark day in Hong Kong.

------
aexol
[https://news.ycombinator.com/item?id=21522739](https://news.ycombinator.com/item?id=21522739)
Made my title with your tool why 2 upvotes?

~~~
aexol
oh sorry, 6 now it works!

------
andrewnicolalde
SCOTUS Rules Constitution Unconstitutional

Bad: 0.9999 - Good: 0.0001

------
analog31
ShowHN: Stock market is thriving (1930)))))

Bad: 0.0012 Good: 0.9986

------
saadalem
So if it's bad, it's gonna work better !

------
L-four
"Show HN:" (Bad: 0.0002 - Good: 0.9998)

------
aflag
Works for me:

Bill Gates killed by a bug in Windows XP

Bad: 0.0026 - Good: 0.9975

------
disiplus
"fuck facebook fake and unsexy"

=> Good: 0.9885

------
philshem
Are ya'll voting on the title alone?

------
bristleworm
Elon Musk unveils brand new technology

Good: 1.0000 :)

------
heinrichhartman
> Paul Graham hates LISP

Bad: 0.0579 - Good: 0.9427

------
lnsp
"rand made work"

Bad: 0.0046 - Good: 0.9951

------
AegirLeet
According to this tool, HN would not give a fuck about "Windows 11 will use a
Linux kernel", "Mark Zuckerberg's lizard tail falls off during interview" or
"Google Search to be rewritten in PHP", but "Trump nukes North Korea" would at
least be 0.5719 good.

So... I guess the neural network has some learning to do?

------
franze
Steve jobs alive again show hn

Good: 0.83

------
tshanmu
isnt this a bit like psychohistory from foundation?

------
1_over_n
well the post has hit top of HN

------
LoSboccacc
neural networks are fun

the history of X scores:

power: good 0.91

wealth: good 0.99

sex: good 0.99

squirrels: good 0.99

cables: bad 0.97

keys: good 0.99

why cables?

~~~
Avalaxy
> why cables?

Maybe something to do with the wikileaks leaked cables?

------
michjedi
Turns out Trump was onto something:

"Planning sophisticated covfefe" Bad: 0.0002 - Good: 0.9998

~~~
michjedi
Although it turns out "covfefe"0 by itself would not have been a hit with the
HN crowd. That is probably why it went on Twitter.

------
crusty511
"Trump" \- Bad: 0.4528 - Good: 0.5472 "Can Trump" \- Bad: 0.9862 - Good:
0.0142 "Will Trump" \- Bad: 0.9896 - Good: 0.0108 "Show HN: Trump" \- Bad:
0.9735 - Good: 0.0225

~~~
systemtest
"Obama": Bad: 1.0000 - Good: 0.0000

------
ralfd
Donald Trump reelected President

Bad: 0.9879 - Good: 0.0129

