A series of social media posts signed under the pseudonym "Q", started a movement known as QAnon, which led some of its most radical supporters to violent and illegal actions. To identify the person(s) behind Q, we evaluate the coincidence between the linguistic properties of the texts written by Q and to those written by a list of suspects provided by journalistic investigation. To identify the authors of these posts, serious challenges have to be addressed. The "Q drops" are very short texts, written in a way that constitute a sort of literary genre in itself, with very peculiar features of style. These texts might have been written by different authors, whose other writings are often hard to find. After an online ethnology of the movement, necessary to collect enough material written by these thirteen potential authors, we use supervised machine learning to build stylistic profiles for each of them. We then performed a rolling analysis on Q's writings, to see if any of those linguistic profiles match the so-called 'QDrops' in part or entirety. We conclude that two different individuals, Paul F. and Ron W., are the closest match to Q's linguistic signature, and they could have successively written Q's texts. These potential authors are not high-ranked personality from the U.S. administration, but rather social media activists.

QAnon is so sprawling, it's hard to know where people join. One week, it's the false rumor that 5G cell towers spread disease, another week it's Wayfair.com But QAnon's millions of followers often seem to begin their journey with the same refrain: "I've done my research." If you buy something using links in our stories, we may earn a commission. This helps support our journalism.

Earlier this month, YouTube said it would remove videos containing misinformation about COVID-19 vaccines and would expand its current rules against falsehoods and conspiracy theories about the Pandemic. It also revealed it's removed over 200,000 videos containing dangerous or misleading COVID-19 information since early February. No wonder the World Health Organisation says the world isn't just fighting a pandemic, but an'infodemic' as well. As The New York Times recently put it, we are facing "the mass distortion of truth and overwhelming waves of speech from extremists that smear and distract". The problem, allege citizen data scientists: the infodemic isn't just crazy people talking to each other online, which in 2020 is basically BAU.

When QAnon emerged in 2017, the game designer Adrian Hon felt a shock of recognition. QAnon, as you very likely know, is the right-wing conspiracy theory that revolves around a figure named Q. This supposedly high-ranking insider claims that the deep state--an alleged cabal led by Barack Obama, Hillary Clinton, and George Soros and abetted by decadent celebrities--is running a global child-sex-trafficking ring and plotting a left-wing coup. Only Donald Trump heroically stands in the way. But what intrigued Hon was the style of nonsense.

In this paper, we expand on our previous research of the potential for abuse of generative language models by assessing GPT-3. Experimenting with prompts representative of different types of extremist narrative, structures of social interaction, and radical ideologies, we find that GPT-3 demonstrates significant improvement over its predecessor, GPT-2, in generating extremist texts. We also show GPT-3's strength in generating text that accurately emulates interactive, informational, and influential content that could be utilized for radicalizing individuals into violent far-right extremist ideologies and behaviors. While OpenAI's preventative measures are strong, the possibility of unregulated copycat technology represents significant risk for large-scale online radicalization and recruitment; thus, in the absence of safeguards, successful and efficient weaponization that requires little experimentation is likely. AI stakeholders, the policymaking community, and governments should begin investing as soon as possible in building social norms, public policy, and educational initiatives to preempt an influx of machine-generated disinformation and propaganda. Mitigation will require effective policy and partnerships across industry, government, and civil society.