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.