Understanding Gray Networks

Gray networks are semi-clandestine and have an organizational structure that is only partially known. They may have some secrecy to their membership, use aliases, have initiation rituals, engage in illicit behavior, and so on, but they aren’t classified as strictly a criminal enterprise like a traditional “dark” network.

Gray networks are somewhat understudied in the social networks and dark networks literature.

In this paper, I use publicly available social media trace data from Venmo and Facebook to explore the structure of the Proud Boys as a “gray network”.

The Proud Boys Venmo network as it grew between October 2016 and April 2019

My main research questions were around whether we can predict the leadership of the group solely from the features of the network, and what can we learn about the way the real world group is organized by looking at the online social network and comparing that to the actual leaders of the group, for example as revealed by their un-redacted Bylaws document.

Proud Boys financial network on Venmo, as of April 2019. Black nodes represent 5 of the 8 “Elders” who were also using Venmo.

Findings of this paper include:

  • Proud Boys social network data confirms their geographically-oriented structure. In addition, the network predicts that 8 leaders (“Elders”) were probably chosen to represent 8 main geographical areas.
  • Social media trace data reveals that real-world events (e.g. TexFest, WestFest, various rallies) are a main driver of membership, payments, and leadership.
  • Proud Boys social network data reveals both bridges and hubs in the structure, as well as small world features. The presence of these different structures can indicate their preferences for how Proud Boys chose to address the communication-versus-security tradeoff characteristic of clandestine networks.
  • Two of the cells appear to operate largely independently of the larger nationwide structure.
  • Although only 5 of 8 Elders appeared in the financial network, 4 of these 5 were predictable as leaders based on network metrics alone.
  • 1 of the 5 Venmo Elders was not able to be predicted from the social media trace data.
  • 4 additional “leader” nodes (2 hubs, 2 bridges) occupy influential positions even though they are not known to be Elders.

This paper has been accepted for presentation and publication at the 11th International Conference on Social Informatics, to be held in Doha, Qatar November 18-21, 2019. (double-blind refereed, 24% accept rate)

Read the pre-print of the full paper (PDF). Abstract:

Anti-Muslim Groups on Facebook: Images and Text Analysis

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.

Far-right extremist women on Facebook

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”.

Gender of first names of right-wing extremist Facebook users, by ideology

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.

Read “Which way to the wheat field? Women of the radical right on Facebook” here.

Anti-Muslim Networks on Facebook

My work on understanding social networks of anti-Muslim groups on Facebook will be published in the proceedings of the 10th International Conference on Social Informatics (SocInfo 2018). The work will be presented on September 26 in St. Petersburg, Russia.

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.

Updated Facebook co-membership graphs

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.

 

Analysis of the latest 1000 Facebook posts by the Times-News (Aug-Dec)

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 two books), 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.

Findings?

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:

  1. The death of Tim-Bob from Graham Cinema
  2. The abduction of a middle schooler from a bus stop
  3. Kmart closing
  4. 25-minute Christmas Lights show on Maple Ridge Dr.
  5. Housing emergency at Burlington Animal Services

No election-related or weather-related items cracked the top 20.

Get rid of the Quizzstar 2016 “words of the year” app

Many friends are posting results of the Quizzstar “words of the year” app on Facebook. It generates a 2010-style word cloud of the words you used on Facebook posts most frequently. To make the image, the user gives Quizzstar permission to view all their old posts, download them to Quizzstar, at which point Quizzstar generates the image. Below is a screenshot of the Quizzstar web site, showing that this app is currently their #1 most popular. (They also have other apps that harvest your friends list and so on.)

Quizzstar “most used words” app, 2016

What users might not be aware of is that by installing this app in your Facebook account, you are agreeing to have your profile and posts mined in order to change and influence the advertisements that you are subsequently shown.

It’s a little hard to follow, but the relevant parts of Quizzstar’s Privacy Policy are sections 8-18 where they describe all the different ways they mash up your data with other data in order to do the advertisement dance. Third party mashups include: Google services (Youtube, Maps, Google Ad Words, Clicky, Admob, AdSense), Facebook (Social and Remarketing), StatCounter, Criteo, and Taboola.

An example of how they use your FB wall posts are mixed with this third party data is as follows (section 18),

We use the remarketing and ad technology provided by Taboola… in order to improve the relevance of the advertising presented to consumers. [This]… includes technical browser and system information, details of how you used our service, such as your navigation paths the referring site, application, or service as well as might be combined with such data collected on other sources. Taboola might also use “Web Beacons” (small invisible images) to collect information. Through the use of “Web Beacons” simple actions such as the visitor traffic to the website can be pseudonymously recorded and collected.

Doesn’t that sound fun?

If you regret installing this app, here’s how to get rid of it.

On a regular device, such as a laptop or desktop machine (i.e. full screen browser):

  1. Go into privacy, and click “See more settings”

2. On the left, click “Apps”

3. Click “Show All” and hover your mouse over the errant app. Use the “X” to remove it (the Cartwheel app is shown, because I had forgotten to remove this one after an experiment last month! whoops)

Removing it on a mobile device

If you’re using a mobile device, you can remove apps by finding your profile page and click through as shown. Sorry Android users, this is an iPhone – I hope FB mobile is similar on your device!

Removing Data from Quizzstar

Go to their user history page on their site, scroll to the bottom.

See if it shows any history for you. (Mine didn’t because I never had the app, but maybe this works for you?)