Web Decay Graph
tbray.org
tbray.org
"How did you detect "decay"? Just based on HTTP codes, or by actually looking at the linked content? On my own blog, I found more often than I like that old links still "work" per HTTP, but now refer to something rather different from the content that I originally intended to refer to."
What would be some good way to detect such spam sites in an automated way? Looking for the link's title in the remote HTML? Check for common domain placeholder page contents and spam words? Maybe Google has some API one could use?
(Someone with far too much time on their hands could probably write a script to attempt to retrieve a copy of the page from the Wayback Machine from around the time the link was posted, then calculate the percentage change compared to the current version. Not really reliable, but worth a try.)
Yea I know it won't work for all links.
* make a copy of all linked resources when each post is published
* regularly compare the linked resources to the copies to make sure the links still work as expected
* when a link dies, automatically replace it with a link to the copy
I now use 'save page as' (in Firefox I actually use https://addons.mozilla.org/en-US/firefox/addon/mozilla-archi...) all the time.
Unfortunately, JS-heavy pages might still perform delayed loading of resources which MAFF doesn't handle, which is one of the many reasons I truly hate how "modern web technologies" are abused.
Seriously, this approach will only shift the data from one unreliable online service to another one. Having decentralized offline copies of relevant information is much better. And maybe we will be able to come up with a mechanism to coordinate these decentralized backups, a la freenet [0]
Disclosure: I use the bookmarking there, not the archiving.
One example on good URL's are HN: https://news.ycombinator.com/item?id=9637215
It will not help ranking on search engines, but the URL will hopefully never change.
Isn't it kind of weird to use a percentage for a line graph? With percentages, the goal is to provide an obvious fractional breakdown, that viewers can readily sum to 100%, visually.
I have no idea how to sum a curve and reconcile back to the original universe.
If the complete set of links in this data is a count of 12,373 links, then how many have decayed? Based on that graph, I have no idea.
Bret Victor posted some interesting thoughts on this subject a few days ago:
http://worrydream.com/TheWebOfAlexandria/
http://worrydream.com/TheWebOfAlexandria/2.html
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