Source?
They may be using JavaScript to track that more accurately.
This is because when I did read more of them, they usually turned out to be a lot less interesting than the ensuing discussion on HN.
So now I use the HN discussion as a proxy for article quality. In the HN discussion I can often find a good summary of the article and get a sense of whether the article is likely to be worth reading or not.
Only maybe 1 out of 10 articles or less that I look interesting to me on HN wind up ones that I actually bother to click through. And of the ones I click through, only 1 out of 10 wind up deserving of being read rather than skimmed.
Some years back, there were a couple of "HN Full Feed" type RSS feeds, that would send the contents of the entire linked article, so I could read them without even bothering to go on to the web site.
I valued these services not only because they were more convenient in that it made clicking through and waiting for the aritcle to load no longer necessary, but also because there'd be less tracking of my interests this way.
I also have javascript disabled for 99% of the sites I visit, and am considering starting to use TOR for more of my browsing. It's really nobody's business what I'm reading, and it's a real pity the Internet wasn't built with more inherent privacy and anonymity features.
Btw how did you accumulate your '1 in 10' stats? Off the cuff? Can you think of a way to measure this? Because I don't think 1 in 10 articles 'deserve to be read'. Somebody DID read them, took the time to post them here. So for some audience at least they were meaningful.
Yep. Just a rough estimate based on my sense of how many articles I actually bother clicking through to. Having a more accurate estimate of my own click-through rate would not be valuable to me, so I never bothered to try to find out.
"Can you think of a way to measure this?"
If I was interested in gathering such stats for myself, I suppose I could use a browser add-on to measure my HN use.
Alternatively, HN could start using indirect links. But I'm not sure if I'd stay with HN if they started doing that. I hate being spied upon, which is one major reason I stopped using Google, and would probably drop HN as well if they started going down that road. Not that HN really needs to do that, since they already know which HN discussion pages I open and what I write (which are reasons for me to start making myself a bit more anonymous in my HN use).
"Because I don't think 1 in 10 articles 'deserve to be read'. Somebody DID read them, took the time to post them here. So for some audience at least they were meaningful."
It all depends on who the audience is, doesn't it? If you aim for the lowest common denominator, you'll probably get a bigger audience. This is a major reason for much of the mainstream media content being such utter garbage (from my perspective).
Also, just because someone clicked through on an HN link doesn't mean that they liked what they found when they got there. The same goes for tracking of people clicking on "Like" buttons or even sending links to their friends.
People could have all sorts of reasons for clicking "Like" buttons that have nothing to do with them enjoying or even reading the content. And I can't count the number of times I've forwarded unread articles to friends because I thought it might be something they might be interested in, but that I had no interest in myself.
"You read the article? You must be new here!", etc
If these numbers aren't accurate due to Google Analytics, I'd be interested to know a way to get the accurate numbers.
The other annoying thing was that, HTTPS never sends referrers. Hence, not a single one of my visits said it was from Hacker News.
I know, you don't want to leak the referrer in most circumstances when it's HTTPS, but it just seems so vital. The Internet was made and understood by referrals and links, lacking an ability to see referrers seems quite unfortunate, especially if all the Internet ends up HTTPS.
Google and Facebook are the only ones who would be able to stitch together significant portions of referral traffic due to Google Analytics or Facebook Like / Connect. Everyone else is just left stumbling around blind.
[1]: https://twitter.com/taybenlor/status/326622962377695232
This is probably minority behavior, but I will often use Instapaper to bookmark articles for later, and then read a batch together. For most in-depth articles, I probably spend less than ten seconds decide whether I should click "Read Later" and then leaving, even though I do in fact read later.
As an example of alternate HN clients, hckrnews.com had 124 referrals, ihackernews.com had 85, HackerWeb had 77 and PulseWeb had 48. That's a grand total of around 300 out of 13.5k.
I'd imagine Readability and Instapaper to be big but probably only some small multiple of that at best.
Unfortunately, my personal website is not their target market. Plans start at $9.95 per month: infinitely more than my hosting cost and also overkill for whenever I'm not front paged. It'd only really make sense for me if it had pricing based on usage, which I can almost guarantee they'll never launch.
I'd argue that Google Analytics is vague in defining or explaining their terms. In a perfect world, yes, they'd offer "pings" to see if a user is active, but even without that I'd at least hope they clarify their terms. Misunderstood terms lead to misunderstood statistics. The less misunderstood statistics, the better.
If I didn't know how to do that, I would probably use a scrollmap tool like CrazyEgg.
I got a extra 300 visitors that day and about 50 the next. The average visitors per day is around 25 so this is a big and noticeable spike.
I guess I am kinda shocked a random link in the middle of a dying thread generated that much traffic while something hitting the frontpage only generated about 53 times more traffic.
I could get your angst if I had linked to it again or actually linked to it in my profile. But I did neither. So now I just think you are a dick.
~18%
(See http://dl.dropboxusercontent.com/u/85192141/Analytics%20www.... )
First off, the metric by definition will always be skewed lower than reality. For multi-page visits, GA takes the time of the first hit and time of the second hit to calculate time on page (and will chain these together to get time on site). Since the page the user leaves on doesn't have a "second hit", that time is never included.[1]
For single page visits, as blog posts tend to be, the calculation is slightly different.[1]
Time on Page = (time of last “engagement hit” on page) – (time of first hit from page)
[1] http://cutroni.com/blog/2012/02/29/understanding-google-anal...
[2]http://www.analytics-ninja.com/blog/2012/06/google-analytics...
If we ignore the 0-second anomaly, it looks like we've got a nice bell curve peaking between 180-600 seconds, probably closer to 180 than to 600. That sounds about right for a 670-word article.
While I didn't track engagement time, I looked at number of comments (both here and on my posts, and shares on Twitter and Facebook) to try and figure out how much of it was "real" traffic.
http://syskall.com/how-to-roll-out-your-own-javascript-api-w...
3051 visits
00:00:16 average visit duration
98.9% less than 10 seconds
3920 visits
00:00:14 average visit duration
99.1% less than 10 seconds
(edit: according to lenazegher's comment the average visit duration stats might not be as bad as they look since my bounce rate was pretty high, ~95% on both posts)
So in the end, the only useful information you get from GA data, is the rate of change (which is useful for many things) -- but not, for instance, the actual number of visits to your pages -- because you have no idea what is counted and what isn't -- and what is considered a visit.
Which illustrates that Google analytics reports something, but what it reports is what it reports. To put it another way, Google Analytics records information useful to Google. What it reports back to the datapoint is designed to appear useful to the datapoint. The purpose of the information provided is solely to encourage the datapoint to keep using Google Analytics so that Google can keep using the datapoint's website to track people on the internet.
So, one common way to handle this on blogs is to use setTimeout in conjunction with an event. Basically you fire an event after 15 or so sections which will then count as an interaction.
It was a post on medium.com about Pac-Man. Medium tracks number of views and number of reads per day. I think they use a metric that's not just time-on-page to differentiate between views and reads. My post had about 26k views and about 15k reads the day it was on the front page.
Do this stats make any sense at all?
Rephrase: can anyone count positive effect of the articles mentioned on HN and further discussions to them?
It may be that I'm not the only person that does this kinda thing.
Using something like this for example: http://larsjung.de/fracs/