65% of Hacker News posts have negative sentiment, and they outperform

OP here :) On skepticism being lumped with negativity: partially true. The SST-2 training task treats critical evaluation as negative sentiment. I should clarify that "negative" here means evaluative or critical, not hostile. HN's culture of substantive critique registers as negative by these metrics, but that's arguably a feature of technical discourse rather than toxicity.

On the neutral category: the model outputs continuous scores from 0 to 1, so neutrality does exist around 0.5. The bimodal distribution with peaks at roughly 0.0 and 0.95 reflects how HN users tend toward strong evaluative positions. Three-class models could provide additional perspective, and that's worth exploring in future work.

Also love your meta-observation. Imo your comment is critical, substantive, and engaging. By sentiment metrics it's "negative," but functionally it's high-quality discourse. But that's exactly how I read the data: HN's negativity is constructive critique that drives engagement, not hostility.

There's a lot of legitimate criticism, but they're also a noticeable amount of "reply guy"-ism (pedantry that can sometimes derail the conversation and bring nothing of substance) and "pet peeve syndrome" (people who'll always repeat the same criticisms of a product or company even if completely off-topic for a submission). It would probably be hard to classify them. And whenever I go on reddit I see that both are enormously worse on there.

I was about to make a comment about skepticism, thank you for adding it. Its likely that its all bunched in together. Looking at material with a critical eye is a positive feature of HN not a negative - thats a very very nuanced thing to evaluate though and likely we do not have the technology

I’m not sure if this is the exact model used by OP, but it appears close, and it classifies your comment as positive at 99.9%.

https://huggingface.co/distilbert/distilbert-base-uncased-fi...

There also is some conflict of interests. VC investors and marketeers obviously want to nurture optimism. And that is entirely understandable and very likely necessary for good ideas to be spread.

While engineers, especially those that like to share knowledge and open source solutions are far more critical of monetizing products.

Overall HN doesn't lack criticism, since there many technically minded people around. But I like the mix to be honest and agree that skepticism is often seen as simple negativity. Sure, you probably don't want to advertise your product as "pretty decent, but there are numerous better theoretical solutions".

HN is not the rest, it is not the majority. It's for a specific tech-savvy social category. This category does want skepticism and criticism because they tend to be perfectionists. This is not "negative sentiment" anything but very positive "evrika!" sentiment for members of the aforementioned category.

Would one say: nice attempt trying to tell people how they are supposed to feel around here?

You comment is very interesting observation. Its made me reconsider some things about sentiment analysis. You are right its not really that HN is negative its that sentiment analysis doesn't really have any way I can think of offhand to measure meaningful discourse rather than GOOD/BAD/NEUTRAL

I've never done any sentiment analysis outside of hobby tinkering. Maybe there is some HN experts that will chime in on how to deal with it?

> The comment I'm writing right now is critical, but is it "negative?" I certainly don't mean it that way.

It's a matter of perspective. The OP is a negative post. You are negative about it. Therefore, you have made a positive post.

Hi, appreciate your comment. The sampling is from all posts / comments over the past 35 days, accessed via the API (https://github.com/philippdubach/hn-archiver). There might be a skew to sample higher voted posts first (i.e. if there is high volume posts and comments with zero upvotes don't make it into the database) so that would explain the high ration. I will definitely look into it before publishing the paper - this is exactly the feedback I was hoping for publishing the preprint. Thanks for pointing this out! Would love to see the mentioned classifier. If you find the time please reach out to the email on the page or on bluesky.

> "most likely to get upvoted" for which "Richard Stallman is Dead" is the optimal title

This is extremely funny, and reminds me of the famous newspaper headline "Generalissimo Francisco Franco Is Still Dead". Of course, at time of writing, RMS is still alive and the optimal headline is a falsehood..

Sorry to get both meta and personal, but I'm kind of curious because you're one of the few here whose name I instantly recognize, probably because I'm fairly interested in science and my impression is you mostly post scientific papers or articles discussing them. I'm looking at your profile of submissions now and the first page is 30 submissions all made in the last 24 hours. Most of them are indeed scientific papers. My own experience reading material like this is it generally takes at minimum 5-6 hours to read a paper and meaningfully digest any of it, and that's only true of subjects I'm somewhat familiar with. For subjects I'm not familiar with, there is rarely any point in reading direct research at all. Given you can't possibly be reading all of this, what is your motivation for submitting all of it to Hacker News? What is your process for finding this material and identifying it as interesting?

Sent you a message to the mail in your bio but I thought I also leave this here: Again, thank you for your comments. I went down a rabbit hole reading your essays Hacker News For Hackers and Classifying Hacker News Titles.

I've also been building an RSS reader with integrated HN success prediction – essentially trying to surface which articles from my feed are most likely to resonate on HN before I submit them. Your research directly informed several decisions, so I wanted to share what I've learned and ask for your insights.

V1 DistilBERT baseline: Started with a fine-tuned DistilBERT on ~15k HN titles. Achieved ROC AUC of 0.77, which felt promising. Clean architecture, simple training loop. V2 Added complexity: Switched to RoBERTa + TF-IDF ensemble, thinking more features = better. ROC AUC dropped to 0.70. Precision suffered. Calibration was poor (ECE 0.11). V3 Stacking meta-learner: Added LightGBM to combine RoBERTa + TF-IDF + 30 engineered features (domain hit rates, author patterns, temporal encoding, word-level stats). The model was "cheating" by memorizing historical domain/author success rates from training data: - domain_hit_rate: 79.9% importance - roberta_prob: only 18.6% V4 – Return to simplicity: Pure RoBERTa, no stacking. Added isotonic calibration (your probability clustering problem, solved!). Current performance: - ROC AUC: 0.692 - ECE: 0.009 (excellent calibration) - Optimal threshold: 0.295 (not 0.5 – exactly as you documented)

What worked for me: Isotonic calibration: Your observation that most predictions cluster below 0.2 was spot-on. Isotonic regression produces well-distributed, meaningful probabilities. Aggressive threshold lowering: At ~10% base rate (posts hitting 100+ points), 0.5 threshold catches almost nothing useful. Pure transformer, no feature engineering: Contrary to intuition, adding TF-IDF and engineered features mostly added noise. The transformer handles the semantics well on its own.

What didn't worked for me: Focal Loss: Made the model too conservative (760 FN vs 219 FP) Domain/author features: Feature leakage, didn't generaliz Stacking: Added complexity without improving generalization

Your essay mentions achieving similar ROC AUC with logistic regression on bag-of-words features. A few things I'm curious about: Do you still maintain this system? Has your approach evolved since 2017? What was your experience with full-content vs title-only classification? I'm title-only currently, which has obvious limits. Any insights on the non-stationarity problem? Topic drift (Apple launches, security panics) seems like a persistent challenge. What made you choose logistic regression over neural approaches at the time? The simplicity seems to have served you well.

I have 104872 karma on HN. You may find https://news.ycombinator.com/leaders and https://news.ycombinator.com/bestcomments interesting. However, there doesn't seem to be an easy way to sort one's own comments by ranking. One of these days I'm going to scrape mine and see if I can write the "rules of HN" for highly upvoted comments.

One is: HN does not like jokes, unless you put an explanation in the comment as well.

I’ve felt the same way with social media in general. It’s about managing your resources. In this case it’s your time.

Something I’ve been experimenting with here is writing smaller comments that serve as an invitation for someone to write an equally lengthy or longer comment in reply.

If the accept the implicit invitation then we can have a longer conversation. It has had moderate success.

Longer content isn’t always better. There is something to brevity. Anyone can make a point with 2,000 words, but it takes writing and editing skill to make that same point and have the same impact with 20 words.

I've certainly noticed the same. I have two accounts here, a main one, and one that I use as a throwaway for occasional personal/emotional, off-topic, or snarky comments. The latter has roughly 4x the comment-per-karma ratio at the moment.

Though interestingly that's largely due to a few specific comments 'blowing up' -- it's typically either 0 upvotes or 100+. I believe the median is actually lower despite a significantly higher average.

This is spot on and has really reduced my willpower to post, tbh.

Like begets like. A glib and snarky comment gets an emotional response, leading to quick, emotional votes. A nuanced, thoughtful comment gets the reader to think, but that's rarely conducive to upvotes unless they were already in agreement.

Over time everyone is Pavlovian-conditioned by the dopamine hit of upvotes to stick with the glib and snarky comments.

The whole upvote system is just a slow-acting poison that inevitably destroys any online community. HN has fared better than most, thanks to great moderation, but it won't resist forever.

>It's frustrating that better content is not always rewarded

It could be. Maybe we just fail to create better content, despite the effort put in. Maybe your frustration comes from lack of engagement, maybe your effort was lost in the ether and no one noticed... But getting noticed could be one criteria to evaluate how good content is. You perform better while not creating the content you consider better. Or captivating an audience to appreciate the better. You see, they don't.

Do you have a blog? It sounds like you would enjoy that.

It could be that your longer and more thoughtful comments get a lot of upvotes, but also a lot of down votes from angry hackers who were oppressed by your writing. Resulting in a tiny negative or positive number. Impossible to tell.

Thank you for pointing that out.

I'm recalibrating my own behavior to upvote more.

Is it the desired behavior of HN that silent upvotes are for agreement? (Instead of a positive comment that doesn't add substantially to the discourse?)

> I read the pre published PDF but don't really see stand what we were supposed to take from this blog post in particular.

I'd argue it's a good thing that they just report the data and then you can draw your own conclusions about whether this is good or bad.

> Also am I basically fullfiling the blog post prophecy?

Yes. I think the post does well to make the point that "negativity" comes in two forms, critical and toxic. Lumping the two seems like an oversight, to me.

The site design also seems to discourage making a simple positive comment. Just click the up vote button.

Not only that, but a positive comment that adds nothing is frowned upon ("that's what the upvote button is for!") which negatively selects such comments.

Thus comments are mostly neutral, objective facts that add upon the original comment, or negative comments of disagreement.

I learn a lot more from informative posts that add something than critical posts that tear something down, largely because the critical posts are basic and repetitive while the informative ones are often novel and offer insider/professional observations.

I definitely think you're onto something. Also, we're inherently psychologically biased toward negative content because all the monkeys who ignored the scary things died.

We're naturally wired to engage with negative content - and that's a must-use recipe for success in an economy that increasingly relies on grabbing your attention.

It's no wonder that depression and anxiety rates are higher than ever, despite our world being much, much safer than it was 100-200 years ago.

Even being aware of this doesn't help all that much.

Trump did a new, unbelievably dumb thing that's going to ruin people's lives? Instant click from me.

Malaria rates down 20% over the past 10 years in the DRC?* I'm still scrolling.

*Fake example, but you get the gist.

You're right! Oh, wait....

The cynical doomerism of reddit is like an infectious disease that ensnares you in their pit of misery with it's initial blast of catharsis. People whose lives bring them out of that swamp leave reddit and stop contributing, so it's mainly populated with miserable cynical doomers all jerking each other off about how screwed they are. Most of them are teenage/college kids working bottom rung jobs/entry level work/unemployed, with all the naivete that comes with it. Stay away from it.

Controversial content is discussed more than positive one, that's a well known phenomenon from gossiping with friends to discussing politics online to whatever.

I always bring the same example: if one of your best friends has troubles with it's partner you'll hear for hours. But when things go smooth they have nothing to say and you have little to add.

I personally only really noticed that I did not like the "after dark" style reddits. But I would generally try to ignore anything political, and focus on like craft/hobby content, media (but not tabloid style), and things not a commentary.

Reddit (or socially generated sites) are really a mixed bag.

I feel like this goes back to the "trick" of getting your questions about Linux answered. Basically, if you just asked your question "How do I do X on Linux?", you'd get no response. But if you said "Windows is so much better than Linux because I can't even do X on Linux", you'd get 5 different ways to accomplish your task before the end of the day.

Nothing gets people engaged more than making them angry.

A 45/65 balance feels like it's at the optimal balance for interesting. Users are expected to continually upvote more and more boring posts if the user pool grows with noise. If the system stabilizes to 50/50, the content would trend toward mediocre but harmless.. Ergo, HN really is a cut above social media.

Unrelated but I still use rif daily. You can patch the apk using Revanced to use your own API key rather than the original developer's key. With the rise of AI, I've block a bunch of subreddits that have become infected with obvious engagement bait posts all with similar structures, writing styles, and tropes.

"Am I the asshole for leaving my spouse because they pushed me down the stairs and murdered my dog? He's also a member of an ultra-nationalist terror organization and doesn't put his cart away at the grocery store.

My friends and family have chimed in with mixed sentiments on social media. Some are praising me and others are telling me I'm wrong."

The account will of course be brand new and all of the top comments will be accounts that solely respond to similar bait posts on similar subreddits. It reminds me of subreddit simulator, it's bots talking to bots. My personal conspiracy theory is that reddit encourages this AI bait slop because it drives engagement and gets people to see more ads. The stories are like the soap operas I sometimes watched with my mom growing up.

Same, and what made me finally quit reddit for good was realizing that on a given r/all page I was blocking 98+% of the content, to the point where it made me question why I am even bothering.

I went through a similar process recently mostly by hand and found the same result. After blocking negative vibes, my only "subs" were intentionally "wholesome" subs like animals/feel good news etc.

>also revealed how boring it is without the kind of algorithmic reaction seeking content.

I also found this but realized this is a good thing(!) if your goal is to reduce Reddit usage.

That being said, a little negativity might be warranted in order to be a part of the discussion. Otherwise you're just opting out completely.

You're making a distinction the paper should address more directly. The classifier can't tell the difference between "this API design is fundamentally flawed because X" and "this company is terrible" (as noted in an earlier reply). Both register as negative by models trained on reviews and social media.

You're also right that HN's moderation probably removes hostile content quickly (which is why I prefer this platform to other roptions tbh). So the negativity we observe is mostly substantive critique rather than personal attacks.

That said, I'd push back a bit on whether this makes the finding less interesting. If anything, the opposite seems true. The fact that HN's "negativity" is constructive criticism, and that this criticism correlates with 27% higher engagement, tells us something about how technical communities value critical analysis over promotional framing. The classifier limitation is real (also see my other replies), but the engagement correlation holds whether we call it "negative sentiment" or "evaluative critique."

I'll add a limitations section to make the terminology clearer: "negative sentiment" as used here means evaluative criticism detected by SST-2-trained models, not personal attacks or toxic comments. Thanks for your feedback!

Cynicism is not a truth-telling philosophy, it cannot be. Telling you a fact is not being critical.

ESL?

This also explains how early performance can be predictive despite the lack of preferential attachment.

No one wants to spend time writing "I agree", mostly they move on or give an upvote. Doesn't look like TFA counts upvotes as we don't see them.

From an evolutionary standpoint, which circumstances should a thinking being prioritize to best ensure its safety and survival? Should it seek out "positive sentiment" and seek to avoid "negative sentiment" (even though this likely doesn't mean evading negative circumstances merely avoiding the sentiment until it is too late)?

Negative bias is probably inevitable in cognition itself.

If i find an article online, ill sometimes pass it through a HN search to see any issues with it.

There are plenty of articles or news ive red that made me think "that's pretty clever" only for HN to point out background i missed and tradeoffs making a solution worse.

Sometimes criticism is shallow or pedantic, but thats easy to dismiss if irrelevant.