After the horrible events in Christchurch, New Zealand last month, I spent some time tracking the terrorist’s manifesto and video as they traveled through the web. I wrote this article describing the technical aspects of how to track a file and why it is difficult, and some technologies -such as IPFS- that extremist groups will be using in the future to make removal of a file even more challenging.
Side note: I originally wrote the article for The Conversation, but since everything there is CC-licensed, the story was picked up and re-run by International Business Times, Raw Story, Alternet, International Policy Digest, and so on. I suppose that is an interesting meta-story about how my own article moved through the web.
I wrote this article to describe how 202 anti-Muslim groups (US-based) on Facebook during the period June 2017 – March 2018 use text and images to describe their cause.
The TLDR is that violence and “enemy” attitudes are the predominant text and visual indicators in these groups.
I wrote this little paper after being frustrated at the lack of data about precisely how many women were in far-right groups these days, especially online. I was hearing estimates ranging between 7% and 56%, and some of these didn’t take social media into account at all, but were just back-of-the-envelope guesstimates based on event attendance or interviews with group members.
In this work, I use a very large collection of data I collected about far-right extremist group members from Facebook’s API during the period June 20, 2017 – March 31, 2018. I then used two “genderizer” software packages to infer the gender of these 700,000 extremist group members. I then divide those into ten different ideologies and look for evidence of women’s auxiliaries, sometimes mockingly called “wheat fields”.
I find that wheat fields DO exist in five of the ten ideologies. The tall spikes at the left side of each graph below represent groups with a super-majority of women, and which are designed to be women-oriented groups.
I find that women’s leadership rates in the Facebook groups differ between ideologies. One smaller ideology, Neo-Nazi, tends to use women in leadership roles at a higher-than-expected rate. White nationalist and Proud Boys, both ideologies with wheat fields for women (a.k.a. women’s auxiliaries) don’t tend to have women in leadership roles in groups as a whole.
Finally, just like in the 1920s with the WKKK (Women’s KKK) and the Ladies’ Memorial Associations (LMAs) and United Daughters of the Confederacy (UDC) I find a systematic marginalization and oppression of women on the one hand comes into conflict with a practical need to leverage women’s networks and organizational abilities on the other hand.
This research was also recently covered in Buzzfeed News.
Below is a network diagram showing some of the extremist groups and ideologies in my data set, and how they overlap in membership.
Network of Anti-Muslim & other extremist groups on Facebook, with 10+ members in common (click to enlarge)
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.
As Wired recently reported, I have built a large dataset of far-right extremist groups in order to learn about their movement and figure out how they operate so that we can keep our communities safe.
Where does the data come from?
Much of my data comes from public, online sources. For example, despite Facebook claiming to be “no place for hate”, I have found that the Facebook social network is actually a very rich source of data about hate groups and the extreme far-right. For 500,000 members of 1,336 different far-right groups and events, Facebook is the perfect place for recruitment and community-building.
No place for hate?
In this blog post, I describe how I divided these 1,336 groups into 11 different far-right extremist ideologies (following the SPLC’s and ADL’s descriptions of these beliefs), and performed some social network data mining to answer questions about group co-membership.
What can we learn from this data?
Since the far-right was attempting to “unite” itself by holding events such as the August 12 Charlottesville rally (recall that this event was literally called “Unite the Right”), I wondered: Do groups from different ideologies share members in common? Which groups have the most members in common?
What are the different ideologies represented?
I originally had 9 different far-right ideologies in the classification system, and have since expanded to 11, as shown in the table below.
To classify each group or event, I evaluated the text and emojis used the group name and description, as well as text and symbols found in the group cover photo, and the public content if viewable (discussion comments, photos, and so on.)
Primarily, I use Facebook’s own recommendation system for finding new groups (“Suggested Groups”). I also use their search API for groups with keywords and concepts related to a particular ideology.
Special note since some right-wing conspiracy theory web sites are misreporting this fact: Keywords alone are NEVER sufficient to classify groups or to select them for inclusion. With Google now indexing over 620 million Facebook groups, and only 1,336 in my database, obviously I am not just randomly including every group that matches a keyword! That is a ridiculous assertion. For example: “rebel” is a very generic word, and there are many thousands of groups with that word in the title. As explained above, I follow the SPLC’s and ADL’s descriptions, and I manually evaluate every single group for a match. If a group doesn’t clearly qualify (by name, photo, content, and/or description), that group CANNOT be included.
Ideology
Count of groups
Concepts related to this ideology
Neo-Confederate**
347
Confederate, League of the South, rebel, confederate flag, secession, dixie, CSA (full description)
UPDATE: After getting feedback about the way I had classified Christian Identity and The Creativity Movements, I decided to collapse the category, moving Christian Identity into Anti-Semitic as SPLC recommends, and likewise, The Creativity Movement is moved into the Neo-Nazi parent category. This makes a lot more sense. Thank you, readers. I guess I’m back to 11 categories again! Numbers have been updated in the table above.
Social Network Analysis
Below is a diagram (created in Gephi using Fruchterman-Reingold layout) showing co-membership between groups from five of the ideologies: Alt-Right, Neo Confederate, White Nationalist, Anti-Government, and Neo-Nazi. Lines (“edges”) between groups (“nodes”) indicate that the two groups share at least 10 members in common. No group is included if it does not share at least 10 members with another group. Larger nodes mean the group has more members in it.
click to expand
In the center of the diagram, in purple, is the Unite the Right (UtR) event. Below is a close-up view of the groups and events that had the highest co-membership with UtR. Here I have highlighted the UtR node, and its connected nodes show up with their color (nodes that are disconnected from UtR in this diagram are “greyed out”).
click to expand
As the diagram shows, UtR not only had a very high number of nodes it shared members with, but the nodes it shared with were from a wide variety of groups from all the other ideologies: Neo-Confederate (red) groups, militias and Oath Keepers (blue), White Nationalist groups (green), Alt-Right groups (yellow), and even Neo-Nazi groups (black).
Which other groups are similarly well-connected? There are a few, but none cast as diverse a net as UtR. The figure below shows some of the largest nodes, all clustered in the busy center of the diagram. We seee a large neo-Confederate group, a very large militia group, a /pol/ (4chan) Alt-Right group, an “anti-SJW” (against “social justice warriors”) group, a “white culture” forum claiming to be the largest on the Internet, an Odinist/Folkish style group, and a forum for National Socialist ideas.
click to expand
More about classifying groups by primary ideology
The problem of multiple ideologies. Many far-right extremist groups could easily be classified into more than one ideology. Most of these groups are anti-Muslim, misogynist, anti-Semitic, and so on. Therefore, to come up with a group’s primary ideology, I will typically rank the name and description as of higher importance than the cover photo when trying to make the call. Still, there are gray areas. I once saw a group that listed 5 different ethnicities it disliked, used a militia symbol on the cover photo, and had a generic, non-descriptive name. Classifying that group was a challenge.
To handle the multiple-ideology problem early on in this project, I originally developed a tagging system where I could tag groups with multiple keywords. In that system, a group could be classified as “militia / anti-Muslim / folkish” or “neo-Confederate / militia / anti-immigration”. This system quickly became untenable, however. It was just too difficult to generate meaningful graphs when every group could be in multiple categories at once. At this point, I think such a tagging system should be built on top of – or in addition to – a primary classification system.
The problem of changing ideologies. Each time a group changes its name, description, or cover photo, it could change the perception of what the group’s primary ideology is. I have run into a few groups that went from neo-Confederate to militias, from anti-Muslim to anti-Government, from anti-Semitic to Alt-Right, etc.
Combining ideologies. Depending on the analysis type I want to do, I might sometimes combine some smaller groups into a larger group. For example, if I’m interested in studying “whiteness” groups, I might combine the large white nationalist category with the smaller Christian Identity and racist skinhead categories. However, if I’m specifically studying Christian Identity groups as a niche community, I can keep these ideologies separated.
Over-representation of certain ideologies. Since I live in the southern portion of the United States, I tend to find more Neo-Confederate groups than certain other types of groups that are more prevalent in other areas of the country.
In future blog posts, I’ll show some of my other data sources, as well as other ways to analyze the Facebook data.
There have been numerous media stories recounting all the hate group fliers found on university campuses recently. I decided to record these incidents on a Google Map.
UPDATE 1: We are now up to 130 incidents. I continue to update this map, so if I’m missing incidents, tweet them to me @MeganSquire0 or email them to me.
UPDATE 2: There are now 185 campuses on the map, all cited and color-coded.
UPDATE 3: 220 campuses now and the story was amplified this week with the “It’s ok to be white campaign” (Newsweek coverage)
UPDATE 4: I created a separate map to track 2018 incidents
The Map
Data sources
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.
Here is a presentation I gave to UNC students and community members last night about A Spotter’s Guide to Signs & Symbols of the Alt-Right, as it exists in 2017. The focus is on local events and those that I have personal familiarity with (e.g. Charlottesville).
A most heartfelt thanks to everyone who supplied photos for this presentation – I could not have made this without their diligence and beautiful camera work.
Qz has a beautiful visualization and explanation showing what happened to the bitcoins that were paid by the Petya/NotPetya ransomware attack victims. It explains how bitcoin mixing works.
