Years: 16
Start year: 2007
End year: 2023
Total Stories: 178882
Distinct Sites: 52642
Distinct Submitters: 43648
Year Stories Points ( mean ) Comments ( mean ) Sites Submitters
---- ------- ------ --------- -------- --------- ----- ----------
2007 9382 92264 ( 9.83 ) 61207 ( 6.52 ) 2644 1163
2008 10980 294775 ( 26.85 ) 186339 ( 16.97 ) 3458 2021
2009 10950 608603 ( 55.58 ) 303962 ( 27.76 ) 4157 3017
2010 10950 1062763 ( 97.06 ) 491718 ( 44.91 ) 4397 3987
2011 10949 1657004 ( 151.34 ) 632724 ( 57.79 ) 4830 4889
2012 10980 1829402 ( 166.61 ) 778634 ( 70.91 ) 5047 5537
2013 10950 2132819 ( 194.78 ) 998387 ( 91.18 ) 5250 5975
2014 10905 2057628 ( 188.69 ) 916438 ( 84.04 ) 5338 5920
2015 10950 2001269 ( 182.76 ) 845719 ( 77.23 ) 5301 5313
2016 10977 2521394 ( 229.70 ) 1137575 ( 103.63 ) 5238 5341
2017 10950 2776064 ( 253.52 ) 1259899 ( 115.06 ) 5236 5274
2018 10950 2762928 ( 252.32 ) 1262654 ( 115.31 ) 4986 5038
2019 10950 3051011 ( 278.63 ) 1447141 ( 132.16 ) 5302 5123
2020 10980 3338150 ( 304.02 ) 1734703 ( 157.99 ) 5938 5564
2021 10950 3376829 ( 308.39 ) 1859933 ( 169.86 ) 6178 5339
2022 10950 3308025 ( 302.10 ) 1986265 ( 181.39 ) 6446 5443
2023 5160 1555335 ( 301.42 ) 879401 ( 170.43 ) 3253 2851
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>).
(It's a bit much to dump massive tables to HN, I'm trying to keep that to a bearable minimum.)
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
- 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.
A couple of tips:
- It's possible to crawl the page using wget, given a reasonable delay. The full collection from 2007 to present (I'd done my first crawl in late May of this year) took a couple of days. Updates to that happen in seconds.
- I break down data by date, story position (e.g., rank 1--30), submitted site (if present), points (votes), comments, and submitter, as well as title.
- I'm working on classifying titles. The original question prompting my analysis was what US states get the most love from HN (NY, CA, WA*, TX, and CO are the top 5). I'd expanded that US and globally-significant cities, and been doing some tuple-based ngram analysis, though that gets pretty hairy.
For 2022 (most recent complete year), the top 40 submitted front-page sites are:
TechCrunch, BTW, lands at #41: (The "mean" values are the arithmetic mean of points (votes) and comments by domain.)For 2023, there've only been 10 TechCrunch items (through 21-6-2023), well below trend:
I'll note that breaking stories down by site will tend to obscure categories, as frequently-submitted sites (say, NY Times) will crowd out many individual blogs. I could probably do some manual classification based on sites, including, say, all categories of Twitter (currently broken out by user/account), and might look into that.One of the most surprising facts to jump out to me is how much nytimes.com has fallen since 2019. It had previously been in the top-4 submitted sites pretty consistently, and single top for 2014--2019, but fell to 7th in 2020 and 9th in 2021, recovering to 5 in 2022.
I've also paired my own analysis with a 2022 study published by Whaly.io based on the HN API and all content submitted: <https://whaly.io/posts/hacker-news-2021-retrospective>
I've been somewhat live-bloogging my analysis on the Fediverse under the #HackerNewsAnalytics hashtag:
<https://toot.cat/@dredmorbius/tagged/HackerNewsAnalytics>
That includes a number of findings (and testing/debugging notes), including: mentions of Reddit by year, mentions of the FP-500 companies (top-10: Apple, Microsoft, Amazon, Intel, Tesla, Netflix, IBM, Adobe, Oracle, and AT&T, though Google under various terms (Google, Alphabet, YouTube, Android) nearly doubles the top-ranked Apple, and no, adding in iPhone, iPad, MacBook, etc., doesn't help), trends in votes and comments by story position (interesting IMO), overall submission success rate (a hair under 3%), mentions of the FP Top 100 Global Thinkers in titles (reprising an old study of mine of numerous online sites), a look at the Leaders characteristics, what HN cares about being down, and, well, ... things: <https://toot.cat/@dredmorbius/110454128168815763>
________________________________
Notes:
* "Washington" can of course designate both a city and a state, amongst other things, and it turns out that the string is dominated by references to the Washington Post, much as "New York" is by the New York Times. But the list gives the naive ranking. Adding in "Silicon Valley" and "San Francisco" put California well on top.
Edits: Some in situ updates as I think of things. Sorry!