Drop me a line if you'd like to discuss this / share w/ reports. username at Protonmail.

I'm sort of a Can Haz All The Tables sort of guy, and I'm largely processing via awk (and a few other shell tools). So pasting that here would get a bit tedious...

It's also been interesting to look at how HN has, and hasn't, changed over the years. Your categorical analysis would be an interesting filter to look at over time, especially regarding accusations that HN is drifting in various directions.

The other bit that stands out to me is how constrained a set the front page is (30 slots per day, 10,950 per year, 10,980 in a leap year), as well as how thin submission titles are for gleaning meaning and context (I'm ... somewhat frustrated by this). Though there is clearly signal that gets through.

I don't have time-of-day granularity, but can look at day-of-week (and have) and month-of-year (not yet) looking for seasonality. DoW has been interesting (usually peaks Tue/Wed, starts trailing off on Fri, Sat & Sun are low points, based on votes/comments, but give higher odds of a given submission landing).

You might want to look at Whaly's work as well (I'd edited it into my larger top-level comment above: <https://whaly.io/posts/hacker-news-2021-retrospective>).

I should mention that I clicked on every single link to see the contents before classifying it, which is part of what made this so tedious

There's a thin line between dedication and mania.

For those interested in the Ongoing Saga of HN Front Page Analyticcs, I've been posting occasional updates to the above site-based classification (~60% of posts now classified) to the Fediverse: <https://toot.cat/@dredmorbius/tagged/HackerNewsAnalytics>

(It's a bit much to dump massive tables to HN, I'm trying to keep that to a bearable minimum.)

Having played with classifying sites for much of the past day, I've assigned a classification to just under 30% of them, which classifies just under 64% of all posts.

The remaining unclassified sites average about 1.7 posts each (there are a few with as many as 20 posts), but there are minimal gains for additional classification.

I'm starting now with running an analysis over the full archive to come up with trends-by-classification over years.

The top-20 classifications (by story) are:

     1  64777  36.21%  UNCLASSIFIED
     2  22481  12.57%  blog
     3  15106   8.44%  general news
     4  13769   7.70%  tech news
     5  12709   7.10%  programming
     6   8459   4.73%  academic / science
     7   8200   4.58%  corporate comm.
     8   7294   4.08%  n/a
     9   5311   2.97%  business news
    10   3798   2.12%  general interest
    11   2151   1.20%  social media
    12   2048   1.14%  software
    13   1613   0.90%  technology
    14   1432   0.80%  video
    15   1144   0.64%  general information (wiki)
    16   1006   0.56%  government
    17    724   0.40%  misc documents
    18    720   0.40%  law
    19    702   0.39%  tech discussion
    20    620   0.35%  science news

I've got a total of 60 classifications which ... seems a bit high, and I'm looking at ways of slimming that down. It's also a bit confused, as some is classified by topic ("programming", "networking" "database", "cryptocurrency", "crowdfunding"), some by source ("corporate comm." is any post that originates from an identifiable company communicating as that company), and general format ("blog" includes 5,306 sites, and spans a wide range of topics). The distinction between, say, "tech news" and "blog" is somewhat ambiguous, and there are a few blogs which should be classified as "corporate comms.". But in all there's a rough sense of what types of content are being posted, and I'd really like to see the change over time.

So, some further thoughts on your methodology:

- It's comprehensive. That's ... admirable, but not necessarily efficient in data analysis. There's a lot to be said for both random sampling and inference.

- You might get more mileage by looking at the top-n stories of a given day. I'd suggest 3--5 items. There's a considerable fall-off in activity from storypos 1 to storypos 30 (1st to 30th items on the front page archive), which is one of the dimensions I've looked at.

- The thought that's occurred to me over the past few days is that this seems like a natural area in which LLM / GPT techniques might be used to classify posts given training data.

- Tuple and ngram analysis can also turn up interesting patterns. Here it's useful to have a base corpus from which universal tendencies can be inferred, and to look at statistically improbably terms which occur both from the HN subject corpus to the universal corpus (terms and phrases which HN finds significant), as well as changing trends over time within the HN corpus.

- Day-of-week and month-of-year analysis can also show interesting patterns, and I've looked at a bit of the first. I'd really like to know if there's an HN "September" (on an annual basis).

- I took a look at your data and ... spreadsheets. Maybe I'm old-school, but flatfiles and gawk are really my style.