WASHINGTON, D.C. – At a hearing of the Senate Judiciary Committee Subcommittee on Crime and Terrorism on Tuesday, Sean Edgett, Acting General Counsel for Twitter, admitted in his prepared testimony that Twitter employed in the 2016 election algorithms that censored out hashtags critical Hillary Clinton, including the hashtag #PodestaEmails and the hashtag #DNCLeaks.
Perhaps the Senate Judiciary Subcommittee should refer Twitter’s admission in its prepared testimony submitted under oath to various law enforcement and regulatory authorities, including the Federal Election Commission and the Department of Justice, to see if Twitter’s pattern of censoring tweets during the 2016 election campaign constituted “collusion” with Hillary Clinton’s campaign.
This admission opens the door to Congress demanding that Twitter, as well as other social media giants including Facebook and Google/YouTube, explain the full range of detection systems they used in the 2016 campaign to censor out content favorable to Trump or critical of Clinton in what appears to have been an effort to influence the outcome of the election.
In the statement below, Edgett couched Twitter’s censorship efforts as justified by the company’s fight to keep automation and spam off the platform, but Twitter neglected to explain whether the company’s detection systems were equally protective of Trump.
Here is what Edgett admitted in his prepared statement:
Before the election, we also detected and took action on activity relating to hashtags that have since been reported as manifestations of efforts to interfere with the 2016 election. For example, our automated spam detection systems helped mitigate the impact of automated Tweets promoting the #PodestaEmails hashtag, which originated with Wikileaks’ publication of thousands of emails from the Clinton campaign chairman John Podesta’s Gmail account.
The core of the hashtag was propagated by Wikileaks, whose account sent out a series of 118 original Tweets containing variants on the hashtag #PodestaEmails referencing the daily installments of the emails released on the Wikileaks website.
In the two months preceding the election, around 57,000 users posted approximately 426,000 unique Tweets containing variations of the #PodestaEmails hashtag. Approximately one quarter (25%) of those Tweets received internal tags from our automation detection systems that hid them from searches.
As described in greater detail below, our systems detected and hid just under half (48%) of the Tweets relating to variants of another notable hashtag, #DNCLeak, which concerned the disclosure of leaked emails from the Democratic National Committee.
These steps were part of our general efforts at the time to fight automation and spam on our platform across all areas.
Also disclosed in Edgett’s prepared testimony was that Twitter on the average suspends the credentials of users suspected of using the website’s Application Programming Interface to post “bot-generated” automated tweets.
The only example Edgett gave was the suspension of @PatrioticPepe, an account described as “automatically replying to all Tweets from @realDonaldTrump with spam content – an example that suggests Twitter was censoring pro-Trump tweets to find users employing automated spam-posting techniques. Was Twitter equally as rigorous in suspending the credentials of those using automated spam-posting engines to support Clinton?
Edgett’s testimony was silent on this point.
On Aug. 1, 2016, ZeroHedge.com reported that Twitter was “shadow-banning” accounts where people on a designated “blacklist” had their tweets relegated on search results and hidden from users’ timelines, while leftist politicians and commentators on a “whitelist” have their tweets promoted.
Earlier, on Feb. 16, 2016, Breitbart reported Twitter was using “shadow-banning” not to screen out “bot-generated” spam-automated postings, but as a technique used by Twitter to block out content considered “alt-right,” further identified as “populist right.”
On Jan. 26, 2016, Breitbart provided examples of tweets that Twitter allowed to be posted, demonstrating Twitter was biased to allowing leftist users to post content attacking conservatives and libertarians that Twitter should have been banned if the company had imposed without political bias the platform’s rules against abuse, harassment, and threats of violence.