Hi folks... As usual (but not taken for granted), AoIR folks have provided a variety of insightful and useful comments and resources about my two prior requests. One was about if there was a Twitter directory where you could enter a list of official organization names and get their main twitter account listing, and the second was the same about YouTube. Also as usual, what appeared to be a simple question can involve a lot of issues. - Twitter Username Extractor: https://zackproser.com/software/username-extractor/ This splashpage explains how this uses quite detailed (and dark humorish) procedures. You log in with a Twitter account. We tried it, but it seems like it’s no longer working. - Maurice RM Vergeer: “yes it can be done, using R and the package rtweet. As for the YouTube question in the other, a similar approach could be done with R and the package Tuber. It probably needs a "do for" loop. Not sure rtweet (beware, technical lingo ahead) is vectorized for this problem. A loop will take some time though, given the large number of organizations. Furthermore, because one query will return multiple results, some semi-manual evaluation needs to take place to asses which account is the actual account. But, anyone with some experience with R could do it” - Nicole Lemire Garlic: As Maurice mentioned, the R tuber package can easily pull the lists of videos from YouTube. There are vectorized and loop-based methods for this. Feel free to email me if you’d like some sample R scripts. - Stu Shulman: “I would add that Maurice points to the non-trivial task of disambiguation when an organization name overlaps terms in common usage. For example, United Airlines is an organization, but it is most commonly referred to as United. Manchester United is a very popular football organization, most often referred to as United. The list of other widespread uses of this common organization name sums up the disambiguation problem. It can be done with training and machine-learning, but not for 2000 terms unless you have an army of workers and lots of money. That suggests a second point, essentially that the practical steps required to gather data for 2000 organizations over time and remain compliant with rate and query limits would be daunting. You might consider trying the task with 5 organizations to assess the challenge of performing the task at scale. Finally, from the view of qualitative research, depending on your end goals, you may not need such a huge number of organizations to reach saturation during analysis. That is, say you looked at 50 organizations and then noticed on 51-60 that you were not learning much you had not already learned. That is saturation. “ - Ed Summers: Based on our several email discussions, he developed a simple program (called luckysocial) that can take a list of official organization names, and either find their website through Google and then go to the website, or, if you have their website enter that directly, to identify their Facebook, Twitter, Instagram, YouTube, and RSS accounts. We ran that using 2000+ nonprofit organization names and it worked great! https://github.com/edsu/luckysocial#readme - Muira McCammon: Muira has done a lot of work identifying and tracking US Federal and state government Twitter accounts. She also writes: If you do want to get technical and/or exhaustive about this, it may be worth using Wayback to double check that the social media accounts currently associated with an org weren't preceded/predated by others. Many orgs these days will have one primary Twitter account but then will launch smaller accounts related to specific initiatives/campaigns/etc. A lot of orgs these days aren't updating their homepages webpages to reflect the full extent of their social media presence. - Peter Timusk: Web sites tend to be heterogeneous and canned CMS sites with very little CSS or HTML5 exposed to gather. - Jakob Jünger: Interestingly, though identifying accounts is a common task, no common protocols or methods have evolved so far. Cross-checking web links, directories, social media search and so on is definitely necessary to get high quality lists. Each of the source brings its own bias. Both of your questions can be solved using Facepager, see the getting started tutorials on the wiki: https://github.com/strohne/Facepager. I (Ron) checked this out and it’s quite a capable program, with lots of good documentation, more of a general interface for using APIs to collect data from various social media and YouTube. Learning this could be a good investment of time and effort. Thanks to all! -- Ronald E. Rice Arthur N. Rupe Professor in the Social Effects of Mass Communication Department of Communication 4127 SS&MS Bldg Santa Barbara, CA 93106-4020 805-893-8696; rrice@comm.ucsb.edu https://www.comm.ucsb.edu/people/ronald-e-rice [image: UC Santa Barbara]