Below is a network diagram showing some of the extremist groups and ideologies in my data set, and how they overlap in membership.
Two of the key anti-Muslim groups in this network – each scoring very high on betweenness centrality measures – are Infidel Brotherhood International and Stop the Islamization of America. Each of their ego graphs are shown below:
Anti-Muslim groups attract the same audiences as other extremist ideologies, including secessionist neo-Confederates, militant anti-government conspiracy theorists, and racist white nationalists. In addition, groups like IBI and SOIA can serve as a convenient lingua franca: their brand of hate is a common denominator that ties extremists of disparate ideologies together.
I’ve updated the Facebook co-membership graphs (see original post) for my upcoming talk at the International Conference on Computational Social Science (IC2S2) to be held at Northwestern University in July. (extended abstract – PDF)
This talk will include data through the end of March, 2018.
Once again, larger nodes = more people. Closer placement between nodes on the graph mean more folks in common.
What do we learn? There are some ideologies that are woven much more naturally into the fabric of a “united” far-right, as opposed to other ideologies, which will be harder to integrate.
Upcoming work will look at groups with nativist ideologies, including anti-Muslim, anti-Immigrant, and how those correspond to Anti-Government/Patriot/Militia and White Nationalist beliefs.
Each map “pin” shows which group did the flyering, as well as the link for where I learned of it. These links are usually tweets from the groups themselves or tweets from students who found the flyers. Sometimes I also use news articles, for example from Vice, Inside Higher Ed, USA Today, NPR, and I also have Google News alerts set up for the alt-right groups so when the flyering incidents are picked up in local media, I get those as well.
On January 18, 2017 the US Department of Homeland Security discontinued its Daily Open Source Infrastructure Report service which it had run since October 2006. To enable researchers to study the content of these reports, I collected as many as I could find (2,151 PDF files) and released them to the Internet Archive. You can find them here: DHS Daily Open Source Infrastructure Reports 2006-2017
Nearly 12,000 professors have used the AAUP’s “Add My Name” feature in order to be added to Turning Point USA’s Professor Watchlist, and large groups of faculty from Trinity University and University of Notre Dame, among others, have also requested to join. The Professor Watchlist was created in order to expose professors who “advance a radical agenda in lecture halls” and inclusion on the list is supposed to be based on “incidents that have already been reported by a credible source.”
How has the list changed?
The Professor Watchlist debuted in November with 146 names, and has grown to 166 names as of January 3, 2016. I was curious who has been added (obviously not all 12,000 who requested to be added!), and even more curious about who has been taken off the list.
Unfortunately, the Wayback Machine does not have the original PW pages indexed for each individual professor, so I can’t go back and see what they wrote for each, but based on what I was able to find online, the rationales for these seven seem very flimsy.
Anyway, at a rate of only 35 changes in a month, Turning Point is going to hire some more interns to enter all these 12,000 names! Good luck with that.
I was playing around with some code today from Mastering Social Media Mining with Python (by Marco Bonzanini, and published by the same company that published my last twobooks), and I came up with this snazzy set of scripts (postGetter.py, fileParser.py) that mines the last X posts from any public Facebook page, creates a clickable FB url for each, sorts them in order of most interactions (shares + likes), and creates a spreadsheet with the results.
Here are the results when run for the last 1000 posts by the Times-News of Burlington, our local newspaper: timesNews.csv.
Not that surprising or shocking, but here goes. The last 1000 only goes back to August or so (modify the params at the top of the code to make it scrape more), but the top five posts for August-December based on interactions seem to be: