Elon Musk’s third Twitter dump came Friday night, exposing the internal decision to remove President Donald Trump from the platform after Jan. 6. Twitter executives clearly understood the actions they were taking to suppress free speech.

 

2.The world knows much of the story of what happened…

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3. We’ll show you what hasn’t been revealed:

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4. The first installment covers the period before the election through January 6

5. Whatever your opinion on the decision to remove Trump that day, the internal communications at Twitter between January 6th- January 8th have clear historical import.

6. As soon as they finished banning Trump, Twitter execs started processing new power.

7. Twitter executives removed Trump in part over what one executive called “context surrounding”

8. The bulk of the internal debate leading up to Trump’s ban took place in those three January days.

9. Before J6 Twitter was a unique mix of automated, rules-based enforcement, and more subjective moderation by senior executives. As @BariWeiss reported the firm had a vast array of tools for manipulating visibility, most all of which were thrown at Trump (and others) pre-J6.

10. As the election approached, senior executives – perhaps under pressure from federal agencies, with whom they met more as time progressed – increasingly struggled with rules, and began to speak of “bios” as pretexts to do what the’d likely have done anyway.

11. After J6 Internal Slacks show Twitter execs. getting a kick out of intensified relationships with federal agencies. Here’s Trust and Safety head Yoel Roth, lamenting a lack of “generic enough” calendar descriptions to concealing his “very interesting” meeting partners.

12. These initial reports are based on searches for docs linked to prominent execs, whose names are already public. They include Roth, former trust and policy chief Vijaya Gadde, and recently plank-walked Deputy General Counsel (and former top FBI lawyer) Jim Baker.

13. One particular slack channel offers an unique window into the evolving thinking of top officials in late 2020 and early 2021.

14. On October 8th 2020, executives opened a channel called “us2020_xfn_enforcement”

Through J6, this would be home for discussions about election-related removals, especially ones that involved “high profile” accounts (often called “VITs” or “Very Important Tweeters”)

15. There was at least some tension between safety operations- a larger department whose staffers used a more rules-based process for addressing issues like porn, scams, and threats – and a smaller more powerful cadre of senior policy execs like Roth and Gadde.

16. The latter group were a high-speed Supreme Court of moderation, issuing content rulings on the fly, often in minutes and based on guesses, gut calls, even Google searches, even in cases involving the President.

17. During this time execs were also clearly liasing with federal enforcement and intelligence agencies about moderation of election related content. While were still at the start of reviewing the #TwitterFiles, were finding out more about these interactions every day.

18. Policy Director Nick Pickles is asked if they should say Twitter detects “misinformation” through “ML. human review, and **partnerships with outside experts?*” The employee asks, “I know that’s been a slippery process…not sure if you want our public explanation to hang on that.”

19. Pickles asked if they could “just say partnerships” after a pause he says, “e.g. not sure we’d describe the FBI/DHS as experts.

20. This post about the Hunter Biden laptop situation shows that Roth not only met weekly with the FBI and DHS, but with the Office of the Director of National Intelligence (DNI):

21. Roth’s report to FBI/DHS/DNI is almost farcical in its self-flagellating tone: “We blocked the NYP story, then unblocked it (but said the opposite)..comms is angry, reporters think were idiots, in short FML”

22. was skipped.

23. Some of Roth’s later Slacks indicate his weekly confabs with federal law enforcement involved separate meetings. Here he ghosts the FBI and DHS, respectively, to go first to an “Aspen Institute Thing,” then take a call with Apple.

24. Here, the FBI sends reports about a pair of tweets, the second of which involves a former Tippecanoe County, Indiana Councilor and Republican named @JohnBasham claiming “Between 2% and 25% of Ballots by Mail are Being Rejected for Errors.”

25. The FBI-flagged tweet then got circulated in the enforcement Slack. Twitter cited Politifact to say the first story was “proven to be false” then noted the second was already deemed “no vio on numerous occasions.”

26. The group then decides to apply a “Learn how voting is safe and secure” label because one commentator says, “it’s totally normal to have a 2% error rate” Roth then gives the final go-ahead to the process initiated by the FBI.

 

27. Examining the entire election enforcement Slack, we didn’t see one reference to moderation requests from the Trump campaign, the Trump White House, or Republicans generally. We looked. They may exist: we were told they do. However, they were absent here.

The tweets the skipped to #32.

32. This inspires a long Slack that reads like an @TitaniaMcGrath parody. “I agree it’s a joke,” concedes a Twitter employee, “but he’s also literally admitting in a tweet a crime.”

33. Roth suggest moderation even in this absurd case could depend on whether or not the joke results in “confusion.” This seemingly silly case actually foreshadows serious later issues:

34. In the docs, execs often expand criteria to subjective issues like intent (yes, a video is authentic, but why was it shown?)

35. In another example, Twitter employees prepare to slap a “mail in voting is safe” warning label on a Trump tweet about a postal screwup in Ohio before realizing “the events took place,” which meant the tweet was “factually accurate”:

36. “VERY WELL DONE ON SPEED” Trump was being “visibly filtered” as late as a week before the election. Here, senior execs didn’t appear to have a particular violation, but still worked fast to make sure a fairly anodyne Trump tweet couldn’t be replied to, shared, or liked.”

37. A seemingly innocuous follow-up involved a tweet from actor @realJamesWoods, whose ubiquitous presence in argued-over Twitter data sets is already a #TwitterFiles in-joke.

38. After Woods angrily quote-tweeted about Trump’s warning label, Twitter staff – in a preview of what ended up happening after  J6- despaired of a reason for action, but resolved to “hit him hard on future vio.”

 

39. Here a label is applied to Georgia Republican congresswoman Jody Hice for saying “Say No to big tech censorship!” and, “Mailed ballots are more prone to fraud than in-person balloting…It’s just common sense.”

40. Twitter teams went easy on Ice on applying “soft intervention” with Roth worrying about a “wah wah censorship” optics backlash.

41. Meanwhile, there are multiple instances of involving pro-Biden tweets warning Trump “may try to steal the election” that got surfaces only to be approved by senior executives. This one they decide just “expresses concern that mailed ballots might not make it on time.”

42. “THATS UNDERSTANDABLE”: Even the hashtag #StealOurVotes – referencing a theory that a combo of Amy Coney Barrett and Trump will steal the election – is approved by Twitter Brass, because it’s “understandable” and a “reference to…a U.S. Supreme Court decision.

43. In this exchange, again unintentionally humorous, former Attorney General Eric Holder claimed the U.S. Postal Service was “deliberately crippled,” ostensibly by the Trump administration. He was initially hit with a generic warning label, but it was quickly taken off by Roth:

44. Later, in Nov 2020, Roth asked if staff had a “debunk moment” on the “Scytyl/Smartmatic vote-counting” stories, which his DHS contacts told him were a combination of “about 47” conspiracy theories.

45.

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47. However, in the end, the team had to use older, less aggressive labeling tools at least for that day, until the “L3 entities” went live the following morning.

48. The significance is that it shows that Twitter, in 2020, at least was deploying a vast range of visible and invisible tools to reign in Trump’s engagement, long before J6. The ban will come after other avenues are exhausted.

49. In Twitter docs execs frequently refer to bots e.g. “let’s put a bot on that.” A bot is just any automated heuristic moderation rule. It can be anything: every time a person in Brazil uses “green” and “blob” in the same sentence, action might be taken.

50. In this instance it appears moderators added a bot for a Trump claim made on Breitbart. The bot ends up becoming an automated tool invisibly watching both Trump and apparently, Breitbart (will add media ID to bot). Trump by J6 was quickly covered in bots.

51. There is no way to follow the frenzied exchanges among Twitter personnel from between January 6th and 8th without knowing the basics of the company’s vast lexicon of acronyms and Orwellian unwords.

52. To “bounce” an account is to put it in timeout, usually for a 12-hour review/cool-off.

53. Interstitial one of many nouns used as a verb in Twitterspeak means placing a physical label atop a tweet so it can’t be seen.

54. P11 has multiple meanings, one being “Public Interest Interstitial: ie covering label applied for “public interest” reasons. The post below also references “proactive V,” i.e. proactive visibility filtering.

55. This is all necessary background to J6. Before the riots, the company was engaged in an inherently insane/impossible project, trying to create an ever-expanding, ostensibly rational set of rules to regulate every conceivable speech situation that might arise between humans.

56. This project was preposterous yet its leaders were unable to see this, having become infected with groupthink, coming to believe – sincerely – that it was Twitters responsibility to control as mush as possible, what people could talk about, how often, and with whom.

57. The firm’s executives on day 1 of the January 6th crises at least tried to pay lip service to its dizzying array of rules. By day 2, they began wavering. By day 3 a million rules were reduced to one: what we say, goes.

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