Machine learning algorithms may have unmasked the authors behind a sprawling conspiracy theory that has had a wide-ranging impact on U.S. politics.
What’s new: Two research teams analyzed social media posts to identify Q, the anonymous figure at the center of a U.S. right-wing political movement called QAnon, The New York Times reported. Inspired by Q’s claims that U.S. society is run by a Satanic cabal, QAnon members have committed acts of violence. Some U.S. politicians have expressed support for the movement.
CommuniQués: Q posted over three years starting on the website 4chan in October 2017 before migrating later that year to 8chan, which later shut down and relaunched as 8kun. Q stopped posting in December 2020.
Elements of style: Swiss text-analysis firm OrphAnalytics clustered Q’s posts to track changes in authorship over time.

  • The analysts divided the posts into five time periods and concatenated posts from each period. Within each period, they split the text into sets of 7,500 characters.
  • For each set, they computed a vector representation in which each value represented the frequency of a different three-character sequence, and they computed the distance between each pair of representations.
  • Principal component analysis learned to represent each distance using a vector with two values, a measure of an author’s style. They graphed these two-value vectors as points, color-coded by time period.
  • Points in the period between October 28, 2017, and December 1, 2017, when Q first appeared, formed a cluster. Later points formed a second cluster. The analysts concluded that two authors wrote most of the earlier posts, and a single author was responsible for the majority of later ones.

Meet the authors: Florian Cafiero and Jean-Baptiste Camps at École Nationale des Chartes built support vector machines (SVMs) to classify various authors as Q or not Q.

  • The team collected public online writings — social media, message board posts, blogs, and published articles — attributed to 13 people with connections to QAnon.
  • They divided the writings into sets of 1,000 words and trained a separate SVM on three-character sequences from each candidate’s work.
  • At inference, they concatenated all Q posts in chronological order and classified words 1 through 1,000, 200 through 1200, and so on to detect changes over time. The most likely candidate was the one whose SVM outputted the highest result.
  • The models’ output pointed to Paul Furber and Ron Watkins. Furber, a former 4chan moderator and technology journalist, wrote most of Q’s late-2017 posts on 4chan. Watkins, a son of 8kun’s owner, former site administrator, and current candidate for the U.S. House of Representatives in Arizona, wrote most of the posts after the migration to 8chan/8kun.

Yes, but: Both Furber and Watkins denied writing as Q to The New York Times.

Why it matters: QAnon’s claims have been debunked by numerous fact-checkers, yet a 2022 survey found that roughly one in five Americans agreed with at least some of them. The movement’s appeal rests partly on the belief that Q is an anonymous government operative with a high-level security clearance. Evidence that Q is a pair of internet-savvy civilians may steer believers toward more credible sources of information.
We’re thinking: Machine learning offers an evidence-based way to combat disinformation. To be credible, though, methods must be openly shared and subject to scrutiny. Kudos to these researchers for explaining their work.

Share

Subscribe to The Batch

Stay updated with weekly AI News and Insights delivered to your inbox