HN front page, 16000 visitors in a day, how many actually read the article?

I rarely read even the articles with interesting titles that make it to HN's front page.

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.

These data are not being interpreted correctly. Analytics calculates time-on-page based on the time between loads of the google JS embedded in your pages. Any visitor who 'bounces' - that is, only visits a single page - only loads the JS once, so their time-on-page is recorded as 0 seconds, regardless of how long they actually spent on the page.

There seems to be a lot of confusion about how GA tracks user engagement, which is understandable as even the Support article linked in another comment doesn't accurately explain what happens with single page visits.

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)

If you set up Event Tracking to trigger as a user scrolls to predetermined lengths of your article, it'll trigger these 'engagement hits' and give you a better approximation of time on site. If you just throw in a standard tracking code that fires off a _trackPageview() event on page load, then GA will never see a second engagement and will not be able to calculate any approximation of time on page/site, so it'll default into the "less than 10 seconds" bucket. Depending on what blogging platform you're using, there are some add-ins that provide such functionality.[2]

[1] http://cutroni.com/blog/2012/02/29/understanding-google-anal...

[2]http://www.analytics-ninja.com/blog/2012/06/google-analytics...

Analytics calculates the time on page by the time difference _between_ page hits. One hit: 0 seconds on site. Because of this, it isn't an accurate metric to measure engagement for a single blog post.

Most of long articles are just a waste of time, the actual information can be condensed into a single paragraph or less and the rest is just redundancy or useless information.

As other commenters have mentioned, 0 seconds doesn't mean anything. Meanwhile, those 18 visitors who spent more than 1800+ seconds on your page? Probably they just opened the page in a new tab and only got around to reading it a few hours (or days) later. So data at both extremes are useless.

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.

Interesting, that ties in with my observations of around 700 visitors per hour on the front page - http://shkspr.mobi/blog/2012/11/whats-the-front-page-of-hack...

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.

Here are my stats for two of my blog posts that made the front page:

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

http://syskall.com/yc-w12-startups-hosting-decisions/index.h...

    3920 visits
    00:00:14 average visit duration
    99.1% less than 10 seconds

Somewhat depressing...

(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)

Interesting. I posted a link to my shithole of a site in a comment a hundred deep in a post that had reached the top of the frontpage and had fallen to pretty much the bottom when I posted in the thread.

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.

This discussion highlights exactly why I don't consider GA a very useful tool - there is no real transparency as to what and how data is collected/measured/filtered [That I've been able to find, anyway].

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.

If the article is long, I usually add it to instapaper or pocket to read later. The time spent on the actual site is low but I still engage with the content.

I was just discussing this with a friend today. We both had front page stories on HN recently. He reported 6k of 6.8k[1] leaving within 10 seconds and I saw that 11k of 13.5k[2] left within 10 seconds.

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

[2]: https://twitter.com/Smerity/status/333534743670951936

I suspect that if you only make one page view, Google will assume your visit was 0 seconds. Most HN visitors will read the article and close the tab.

Here's a bit of data: last week my page http://www.gwern.net/Google%20shutdowns was submitted to Hacker News and hit the front page for a while, racking up thousands of visitors. As it happens, I was running an A/B test on fonts, where a JavaScript timer sleeps 40 seconds and then fires, telling Google Analytics that a reader has 'converted'. (This hopefully avoids the bouncing distortion of the 'time on page' metric.) So, what percentage of readers stayed on the page long enough for the timer to fire after 40 seconds? (The Markdown source is somewhere around 12k words, so it's not the quickest read in the world.)

~18%

(See http://dl.dropboxusercontent.com/u/85192141/Analytics%20www.... )

at this rate, I'm now hoping that I can do an analysis of the analysis of a viral post that also have gone viral. how awesome would that be? :)

If I read your page on my desktop, JavaScript was off - unless I had forgotten to revoke temporary permissions after whitelidting Google analytics on another page.

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.

A post that estimated how many HN visitors block Google Analytics would be useful.

For me (echoing some of what gnosis has stated), the real value that HN brings is the discussion. Frequently, I find the comments, insights, opinions, and tangents elicited by HN submissions to be more interesting and thought-provoking than the submissions themselves. I typically browse through the discussion a good bit before ever clicking through to the article/site which initially drove the discussion. There are plenty of "show-and-tell" mechanisms on the Web. What sets HN apart, in my mind, is the round-table that develops in response to a lot of those submissions.

Like has been mentioned by many, this is incorrect interpretation... and quite common to see on blogs or single page sites. Although technically it's not dependent pageviews, but interactions (so pageviews or events).

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.

As another data point, I recently had a 60% read rate on a post that was on HN's front page for a while.

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.

Paul, you may want to compare raw web server logs to the numbers you get from GA. I wouldn't be surprised if there's a big discrepancy, especially when there's HN in the mix. Moreover, those who are nerdy enough to surf with tracking scripts blocked might be the ones who actually read the article ;)

Can anyone count those who read translations without link to the original article or to the HN discussion?...

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?

I tend to quickly go through the front page and bookmark the articles that I'm interested in. Then, when I have the time and I'm on my tablet - I read them.

It may be that I'm not the only person that does this kinda thing.

Here's another way to measure engagement for longer content, scroll depth: http://robflaherty.github.io/jquery-scrolldepth/

A tool to record which portion of the screen was visible for how long would be interesting.

Using something like this for example: http://larsjung.de/fracs/

The most traffic a domain controlled by me ever received from HN was about 3000 uniques: 2000 the first day, 1000 the next.

I add a lot of these articles to my Pocket and read them later. I wonder how GA reflects that.

Anything to do with preloading by Chrome?

16K is not the real number

tl;dr