First, using Pushshift, I found all 33764 posts on /r/nosurf up until the end of 2023.
Then I filtered them down to "success posts," which I considered to be posts where the person significantly reduced their overall screen time for at least a month.
I started by filtering posts using heuristics. For example, if the title ended with a "?", I figured it was a question and not a success post. After applying these filters, I manually reviewed the remaining 17013 titles and selected those that seemed like they could possibly be successes. That gave me 678 candidate posts. After reading through each candidate post, I found 160 posts that met the criteria (not including multiple posts from the same author).
I then read all of the success posts and manually tagged information that I wanted to get numbers on. I was looking for techniques used, apps people were addicted to, the benefits of quitting, withdrawal symptoms, among other things.
This selection process almost certainly missed some success posts, but as a sanity check I compared my list to previously compiled success stories and found that I had caught them all or explicitly rejected those that didn't meet my specific criteria.
Manually tagging data is extremely fraught. For one thing, I'm sure I missed a lot of potential tags (like not counting as benefits all the benefits listed in a post). Also, deciding how to categorize things is tricky and I'm sure I wasn't perfectly consistent. And there is often more than one way to categorize things, and the somewhat arbitrary decisions I made probably affected the counts significantly.
Still, hopefully we can learn something here despite these limitations.