Thread by @MAGACult2

We call it “the machine.” Not sci-fi. It’s bots, influencers, AI, & money pipelines bending reality online. And we’re not the only ones who’ve mapped it. These are the receipts — everyone who’s seen the gears.

Caroline Orr Bueno warned for years: disinfo doesn’t “go viral by accident.” She showed how bots + big accounts fuse into loops that overwhelm truth. Her maps mirror the ones we’ve built.

Renée DiResta (Stanford Internet Observatory) told Congress: amplification is engineered. Her receipts: coordinated networks, not random chatter. Same design we see in X swarms.

Atlantic Council’s DFRLab exposed global takedowns: swarms of accounts posting in lockstep. Same tactic we log daily — reply floods & narrative blitzes. #AllTheReceipts

Kate Starbird (UW) studies “crisis informatics.” She proved rumors race fastest in breaking events — powered by coordinated swarms. Exactly what our burst charts show.

Claire Wardle built the “zombie rumor” framework: lies that die then rise again. Memes, distortions, recycled context. Our narrative loops track the same undead info.

Jonathan Albright mapped fake sites hijacking trends, showing platforms quietly boosting engineered virality. Our suppression receipts echo his forensic style.

Emilio Ferrara (USC) modeled bots mathematically. He proved spikes no human could make. We use the same math — Bayesian + Poisson bursts — to flag fake floods.

William Kory Amyx blew the whistle on how to catch coordination: stylometry, Bayesian inference, burst detection. His disclosure = blueprint for seeing the machine.

Philip Howard & Oxford’s Internet Institute call it “computational propaganda.” Their research: governments + companies deploy armies of bots. We see it here in U.S. politics.

NCRI (Network Contagion Research Institute) tracks hate + extremist amplification. Their maps overlap with our “synthetic majority” findings — floods of fakes posing as real people.

CCDH proved a handful of bad actors push most online hate. Same math explains our suppression: a few mega-amplifiers can drown entire topics.

Botlab dug into click fraud networks — showing views & likes can be bought in bulk. We’ve traced the same “money → narrative → influence” pipelines.

Indiana University’s OSoMe studies bots, coordination, algorithm loops. Their data confirms our stylometry & burst findings: these patterns are not human.

CMU’s Lynnette Ng found 20% of accounts in her sample were bots. She showed how they trick people into thinking they’re real. Same “synthetic majority” we log. Our results show much higher percentages.

Consumer watchdogs flagged xAI’s Grok for abuse — deepfake images, harmful prompts. Same risks our logs show: AI as a disinfo weapon, not a safeguard.

Harvard’s Digital Safety Kit explains harassment swarms & dogpiles. We’ve seen identical suppression floods aimed at investigators & critics.

Notre Dame researchers: platforms fail to block bot floods. Their verdict = structurally broken. Our receipts back it: swarms overwhelm because rules aren’t enforced.

Regulators are catching on. Ireland’s DPC probed xAI for data misuse. Poland filed EU complaints. Our warnings now echo at government levels.

Reporters (AP, Reuters, Guardian, Wired) tested Grok + others: they generated scams, lies, antisemitic tropes. Journalists confirmed the same “model drift” we flagged.

PEN America & Human Rights Watch tracked doxbait + harassment of journalists. Their reports = proof of suppression ops. Exactly the playbook we’ve logged.

Clemson’s Media Forensics Hub mapped troll → bot → influencer loops in elections. Same laundering cycle we track: trolls seed, bots amplify, influencers normalize.

NYU’s Cybersecurity for Democracy proved algorithms boost or bury posts secretly. Our receipts show the same: shadowbans + dampening tilt reality. Imagine 10k real fans, 50k cardboard cutouts shouting over them. That’s the machine.