Automated copy-and-paste detection under trial
One of the problems Wikipedia faces is users who add content copied and pasted verbatim from sources. When we follow up on a person's work, we often don't check for this, and a few editors have managed to make thousands of edits over many years before concerns are detected. In the past year, I've picked up three or four editors who have made many thousands of edits to medical topics in which their additions contain word-for-word copy from elsewhere. Most of those who only make a few edits of this nature are usually never detected.
After a user detects this kind of editing, clean-up involves going through all their edits and occasionally reverting dozens of articles. Unfortunately, sometimes it means going back to how an article was years back, resulting in the loss of the efforts of the many editors who came after them. Contingency reverts can end up harming overall article quality and frustrate the core editing community. What is the point of contributing to Wikipedia if it's simply a collection of copyright-infringed text cobbled together, and even your own original contributions disappear in the cleanup? Worse, the fallout can cause editors to retire. If we could have caught them early and explained the issues to them, we'd not only save a huge amount of work later on, but might retain editors who are willing to put in a great deal of time.
So what is the solution? In my opinion, we need near real-time automated analysis and detection of copyright concerns. I'd been trying to find someone to develop such a tool for more than two years; then, at Wikimania in London, I managed to corner a pywikibot programmer, ValHallASW, and convinced him to do a little work. This was followed by meeting a wonderful Israeli instructor from the Sackler School of Medicine Shani Evenstein who knew two incredibly able programmers, User:Eran and User:Ravid ziv. By the end of Wikimania our impromptu team had produced a basic bot – User:EranBot – that does what I'd envisioned. It works by taking all edits over a certain size and running them through Turnitin / iThenticate. Edits that come back positive are listed for human follow-up. Development of this idea began back in March of 2012 by User:Ocaasi and can be seen here.