Part I. Google Employee Confesses To Manufacturing Bots To Support False Russia Collusion Narrative As An Excuse To Kick Authentic Users Off Twitter
Remember that in 2018 a Google employee claimed that it was Google creating the bots on Twitter:
Google (ID: C1jjJZxP) 03/15/18(Thu)05:17:20 No.164036759
“Hello everyone this is my first time posting on 4chan but I need to get this out and I need to stay anonymous. I work for Google. I’m not going to name the Internet tech department, the Internal tech department, sorry, for obvious reasons I don’t want anyone to pinpoint who I am but I’m in tech and work with AI. I’ll explain.
“My team and I created AI bots for Twitter. These bots are slightly different than regular AI bots. These are remote signal bots, but I’ll explain what they do.
“My team and a human intelligence team, which is really just the propaganda team, work together to make certain topics trend and persuade public opinion which persuades political pressure. We do this by a groupthink method. We have a name for it internally, but ‘consensus cracking’ is more used name externally, but the bots we created go into Twitter conversations and push a narrative. Some of the bots are verified accounts, and they start by arguing a point of view against someone, and then more bots join in and thumbs up the comment. We are doing it with gun control now. More people see a consensus of gun control and people on the fence get persuaded to our narrative and politicians get pressured by thinking it’s actual people. We had whole meetings about 4chan because you guys, specifically this board, are disrupting the bots. You are basically doing what we are doing but you are real people. We, not necessarily me, devised a plan to knock you guys from Twitter. We accused Russia of doing what WE are doing, and used the narrative to wipe out ‘suspected bots,’ which we knew weren’t bots at all.
“I feel like shit about this. Here’s the thing, I’m actually a democrat, and I HATE guns, but i believe in balance of the people more than anything. We are using software as a political tool instead of the will of the people.
“This is also a violation of the SEC, we are fabricating twitter users and using them for stocks & advertisers. I signed that I wouldn’t discuss this, so I need to stay anonymous.”Source: 4Chan, March 15, 2018; emphasis added
Part II. Operation Q-T2810C
Future “Conspiracy” push to norms.
Bad mixed w/ good.
They are scared.
Q”Q Post #1013, April 4, 2018
“Clown A185” PowerPoint
These 3 slides were first posted anonymously to Medium.com on January 2018 by “Clown A185.”
What follows is the slides, and then a transcript.
Transcript of Slide 1:
“QAnon Radicalization Pathway”
“Followers of “Q” or “QAnon” on social media display all the classic primary markers of online radicalization leading towards violent extremism, as exhibited by such groups as ISIS”
“Pre-Radicalization -> Exposure, Repetition, & Conversion -> Identification, Indoctrination & Group Bonding -> Justifying Violence -> Violent Extremist Acts”
“Normies” “Redpilled” “IAMQ” “These people are sick” “BOOM”
“Targeting of vulnerable social groups with perceived victim status for political end” – “Algorithmic reinforcement of emotional viral payloads through network propagation” – “Shifting norms & referents to follow perceived peers, reinforcing in-group status” – “Ratcheting up of rhetoric, imagery, aggression & dehumanization of out-group” – “Actual commission of violent extremist acts to reflect & reinforce in-group worldview”
Transcript of Slide 2:
“Disrupt All Stages”
“1. Identify vulnerable groups, characteristics, and emotional triggers.
“2. Map themes of known bad payloads, keywords, and sources.
“3. Analyze influencer networks to identify key actors.
“4. Test & map natural payload propagation pathways in the space.
“5. Throttle distribution of known bad content, & queue for human review probable.
“6. Identify, isolate, and disrupt bad actors moralizing extremist violence.
“7. Discredit known bad sources, & sow uncertainty about peers and movement.
“8. NB test effective counter-messaging.
“9. Elevate Stage IV-B actors to LE.”
Transcript of Slide 3:
– 55% numbered bots w/ minimal history to seed, swarm, & boost (pawn)
– 25% semi-auto Al skins w/ limited interaction to spread FUD (knight)
– 12% automated meme factories for A/B payload testing & broadcast (bishop)
– 7% fully-controlled, aged accs w/ generated backfill data (rook)
– 1% deep cover real accounts (queen)
Part III. “Twitter Files”
Appendix: Further Information to Download and Share
By Dr. Dannielle Blumenthal (Dossy). All opinions are the author’s own. Public domain.