I worked on the Irish version of an open source tool that looked into this somewhat.
Allowed you to pull in each article on the various news sites and stick them into git where you could then see the difference in the articles over time.
A Phd student founds some interesting insights particularly in the Irish media where content would often be changed sometimes weeks / months / years after the original story was placed online.
Often though; I found it showed how little time a journalist often had time to work on revisions to any one story....the world should lament the death of journalism.
Really interesting findings on clickbait. I worry about the viability of the second half, using machine learning to detect bias (which seemed admittedly inconclusive).
> finding bias in headlines is a more subjective exercise than finding it in Wikipedia articles...
Probably because bias from new Wikipedia users is less careful. And absent smoking guns, once you get into the gray areas, several studies have shown bias is often in the eye of the beholder:
What I'd really want is an adblock extension that automatically hides clickbait articles on, say, google news. Or maybe a crowd-sourced thing that converts titles into nonclickbait titles. People need to think of it as just another form of spam.
tldr.io might be close to your needs, but imo usually one can pretty accurately guess whether a title is intended clickbait or not (of course it helps that even if you are moderate about it in my guesstimation 90%+ of articles are easily fall in that category).
I'm on the other end of things though, I would really like a tool which can generate clickbait titles based on the article body, or even better, generate the whole fluff piece based on some keywords. Something similar to https://pdos.csail.mit.edu/archive/scigen/ maybe
[+] [-] jplasmeier|9 years ago|reply
"The classifier was trained by the developer using Buzzfeed headlines as clickbait and New York Times headlines as non-clickbait."
[+] [-] nerdponx|9 years ago|reply
[+] [-] beilabs|9 years ago|reply
Allowed you to pull in each article on the various news sites and stick them into git where you could then see the difference in the articles over time.
A Phd student founds some interesting insights particularly in the Irish media where content would often be changed sometimes weeks / months / years after the original story was placed online.
https://github.com/johnl/news-sniffer
Often though; I found it showed how little time a journalist often had time to work on revisions to any one story....the world should lament the death of journalism.
[+] [-] brownbat|9 years ago|reply
> finding bias in headlines is a more subjective exercise than finding it in Wikipedia articles...
Probably because bias from new Wikipedia users is less careful. And absent smoking guns, once you get into the gray areas, several studies have shown bias is often in the eye of the beholder:
https://en.wikipedia.org/wiki/Hostile_media_effect
[+] [-] cha-cho|9 years ago|reply
[+] [-] yakult|9 years ago|reply
[+] [-] a_imho|9 years ago|reply
I'm on the other end of things though, I would really like a tool which can generate clickbait titles based on the article body, or even better, generate the whole fluff piece based on some keywords. Something similar to https://pdos.csail.mit.edu/archive/scigen/ maybe
[+] [-] noir-york|9 years ago|reply
I think the research may need to take a second look at the algos used. Would have been interesting to have the Economist in there as well.