Megan Squire

Dr. Megan Squire is a professor of Computer Science at Elon University (North Carolina, USA). Her main research area is applying data science techniques to understand niche and extremist online communities, particularly radical right-wing groups on social media. Dr. Squire is the author of two books on data cleaning and data mining, and over 40 peer-reviewed articles and book chapters, including several Best Paper awards. In 2017, she was named the Elon University Distinguished Scholar. She currently serves as a Senior Fellow for data analytics at the Southern Poverty Law Center, and as a Senior Fellow and head of the Technical Research Unit at the Center for Analysis of the Radical Right.

Podcasts and Videos

Monetizing Propaganda (2021)

🏆 Best paper award, ACM Web Science conference. How do right-wing extremists use video streaming to make money? This is a deep dive into the network of streamers and donors using the DLive service. (Conference, PDF)

Baseless

"Baseless", Sounds Like Hate

 From the Southern Poverty Law Center
  • Podcast  I help analyze "The Base", an international terrorist group plotting for a race war. Parts 1, 3, and 4. (October, 2020; May 2021)

Hate in the Homeland

"Hate in the Homeland", Reveal

 From Reveal, The Center for Investigative Journalism, and PRX
  • Podcast  We discuss the rise of "Alt-Tech" social media platforms that enable online extremism (June, 2019)

Latest Papers

How are extremist groups organizing online? Who are the key players and what do they believe? Which groups are growing and which are shrinking? Why are some online communities more toxic than others?

I collect, store, and analyze data - mostly from social media, but other sources as well - so we can understand how extremist and niche online communities work.

Here are a few of my latest research papers and findings.

telegram

Monetizing Propaganda (2021)

🏆 Best paper award. How do right-wing extremists use video streaming to make money? This is a deep dive into the network of streamers and donors using the DLive service. (Conference, PDF)

telegram

The Pushshift Telegram Dataset (2020)

Describes our dataset of 27.8K channels and 317M messages from 2.2M unique users, designed to study social movements, protests, political extremism, and disinformation. (Read More on Arxiv)

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The Pushshift Reddit Dataset (2020)

Describes our dataset of Reddit's millions of subreddits, millions of users, and hundreds of millions of comments(Read More on Arxiv)

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Understanding Gray Networks using Social Media Data (2019)

🏆 Best paper runner-up. Is it possible to understand the structure of a clandestine network, for example Proud Boys, using just their social media trace data? (Read More)

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Radical Right-Wing Women on Facebook (2019)

At what rates and in what capacity do women participate in extreme far-right ("radical right") political online communities? (Read More)

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Anti-Muslim Hate Network on Facebook (2018)

How do anti-Muslim political groups use the Facebook social network to build their own online communities? Do they crossover with other far-right political ideologies, such as anti-immigrant or white nationalists? (Read More)

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Analyzing the "Summer of Hate" (2018)

How "united" is the far-right? Which groups share members? Which ideologies tend to attract the same people? Which groups are responsible for uniting such distinct far-right ideologies at an event like the Charlottesville rally? (Read more)

lx

Rudeness on Linux (2017)

How do the leaders of the Linux Kernel project write in email? What are the salient features of their writing, and can we discern one leader from another? (Read more)

Current Projects

Here are some long-term projects I'm working on that have not yet been published.

podcast

Broadcasting Hate: Understanding the Right-Wing Extremist Podcast Ecosystem

This project is a long-term study of several dozen extreme right-wing, racist podcasts. We build the network of guests and hosts, and show how the ecosystem grows and shifts over time. (Manuscript under review)

ci

Network and Text Analysis of Christian Identity Hate Groups

For this work we mine decades of Christian Identity historical documents to create a social network of key players. We then compare that to the current CI landscape on the social media platform Telegram.

steam

Entity Matching Social Media Users: A Machine-Learning Approach for Steam

In this project, we use a machine learning approach to predict the correct user identification for a prior user playing under a false identity.

Media Coverage

I've discussed my work on right-wing extremists and toxic online communities with numerous media outlets, including:

2021

2020

2019

2018

2017

2016

2014

  • Hearsay Culture Radio Show #224. Radio interview, pre-recorded. KZSU Stanford University 90.1 FM. November 13. (Data Mining.)

Other Writing and Appearances

I also blog as a Senior Fellow for the Center for Analysis of the Radical Right (CARR) where my work has been reposted in Rantt and Sage Ocean.

In addition to many research papers and articles for popular media, I have also written two books, Clean Data, and Mastering Data Mining with Python.

Find Me