![]() ![]() ![]() #Tweetadder 3 fullIt won't make sense to buy Twitter if we're heading into World War 3."Ī lawyer for Musk, Alex Spiro of Quinn Emanuel Urquhart & Sullivan, said the characterization of the texts in court was "utter nonsense as the full text chain shows." The full text chain is expected to be filed on the court docket next week. "Putin's speech tomorrow is really important. "Let's slow down just a few days," Twitter's lawyer said, reading out Musk's texts during the hearing. Putin gave a speech May 9 for the 77th anniversary of the Allied victory over Nazi Germany, where he said Russia's decision to invade Ukraine in February was the "only right decision" and baselessly claimed the West was "preparing for invasion of Russia." In these texts, the billionaire also cited a coming speech from Russian President Vladimir Putin. The text messages were sent May 8 to a banker at Morgan Stanley, which is financing part of Musk's deal. Musk told Twitter in July he was officially backing out of his agreement to buy the company in a letter that argued Twitter had wrongfully withheld data on "bots" and spam accounts. #Tweetadder 3 trialThese texts came to light in a hearing Tuesday about pushing back a trial in October over Musk's deal to buy Twitter, along with other disputes about discovery. So while levels of harmful speech might return to an equilibrium after an initial spike, this does not undo the harm experienced by the recipients of the toxic and threatening speech, as well as others who were exposed to it.Elon Musk wanted to "slow down" his $44 billion acquisition of Twitter months after Russia invaded Ukraine, saying in private texts that it wouldn't make sense to buy Twitter "if we're heading into World War 3." ![]() Second, research suggests that women and people of color who are public figures are especially likely to be targeted by harmful speech. Given the scale of the platform, this can amount to thousands if not millions of unflagged harmful tweets targeting an individual or group. (To be sure, there are important examples in which individuals or groups remain the targets of harmful speech over extended time periods.) And while Twitter estimates that 51% of tweets that violate the platform’s terms of service are automatically caught by its machine learning systems, this leaves 49% to be detected by human content moderators. First, this pattern creates a whack-a-mole dynamic in which harmful speech targeting a figure may appear in bursts, only to recede. The bursty pattern presents difficulties for content moderation policies and enforcement. ![]() Taken together, this analysis provides a birds-eye view of how Trump’s recent tweets impact the larger online discourse around the political figures he attacked. #Tweetadder 3 seriesPerspective defines severe toxicity as “very hateful, aggressive, disrespectful” speech, and threats as describing “an intention to inflict pain, injury, or violence against an individual or group.” Third, we construct an interrupted time series with a 24-hour moving average, which enables us to isolate the impact of Trump’s tweet on the levels of severe toxicity and threats. We do so by using Perspective, an open source API created by Jigsaw and Google’s Counter Abuse Technology team to enable the classification of harmful speech online. Ralph Northam, for example, a tweet had to include “Governor of Virginia,” “Governor of VA,” “Northam,” or “Ralph Northam.” Next, we estimate the levels of two characteristics in that corpus: severe toxicity and threats. To be included in the corpus for Virginia Gov. For each politician, we used the politician’s name, Twitter handle, and title. (Everyday, the NYU Center for Social Media and Politics collects a 10% random sample of Twitter, which amounts to tens of millions of daily tweets.) First, we use keywords to identify the corpus of tweets associated with each of the three politicians Trump mentioned. For each of the three politicians mentioned in Trump’s tweets, we complete a three-step analysis on a random sample of tweets. ![]()
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