In 2018, investigative news group Project Veritas investigated Twitter exposing the bias of many of its employees and the secretive tactics that Twitter used to suppress speech on the platform.
In January of 2018, James O’Keefe’s Project Veritas published its investigation of social media site Twitter that confirmed a number of reports published by Breitbart Tech, such as the prevalence of shadow banning, a method of preventing Twitter user’s content appearing in their follower’s newsfeeds — and thus not spreading normally, Twitter’s use of bots to attack pro-Trump and pro-America accounts, and Twitter’s open bias against conservative users regularly banning their accounts based on the personal politics of Twitter’s account reviewers.
Recently, Twitter added a “fact check” label to a pair of tweets from President Trump expressing widely-held concerns about mail-in ballots increasing the risk of voter fraud. The “fact check” link, which urged users to “get the facts about mail-in ballots,” directed users to a Twitter “moment” — a collection of links and tweets, handpicked by Twitter employees.
The “moment,” intended to fact-check the President, was filled with establishment media articles from CNN, the Washington Post and other outlets, baselessly asserting that Trump was lying about mail-in ballots. This is reportedly the first time the social media platform has branded Trump’s tweets with a link to a “fact check” of this type.
Twitter’s “fact check” of the president’s tweet is a direct continuation of a campaign of bias against Conservatives documented both by Breitbart Tech and Project Veritas.
Twitter has claimed to be a neutral platform for some time, but Veritas’ investigation revealed a long history of bias amongst employees at the Silicon Valley firm. Pranay Singh, a direct messaging engineer at Twitter who spoke on camera in Project Veritas’ report, revealed that conservative accounts are actually targeted en-masse using machine learning. When a Project Veritas journalist asked Singh how Twitter staff can tell if an account is a bot or a normal person, Singh replied,
You use machine learning. You look for Trump, or America, or any of, like, five thousand, like, Keywords to describe a redneck. And then you look and you, like, parse all the messages, all like the pictures, and then you look for, like, stuff that matches, like, that stuff. And like if it, so you, like, you assign a value to each thing, so like Trump would be, like, .5, a picture of a gun would be like 1.5, and, like, if it comes up… the total comes up above, like, a certain value, then it’s a bot
Just go to a random tweet, and just look at the followers. They’ll all be like, guns, God, ‘Merica, like, and with the American flag and, like, the cross… Like, who says that? Who talks like that? It’s for sure a bot.
You just delete them, but, like, the problem is there are hundreds of thousands of them, so you got to, like, write algorithms, that do it for you.
I would say majority of it are for Republicans, because they’re all from Russia and they wanted Trump to win, so yeah.
Veritas also revealed that prominent conservatives on the platform are regularly targeted. Twitter’s Policy Manager, Olinda Hassan, told an undercover Project Veritas reporter that the company was working on preventing certain Twitter users from appearing in users timelines:
When asked how the Trust and Safety team prevents certain figures, such as conservative journalist Mike Cernovich, from appearing in user timelines, Hassan stated,
We’re trying to down rank it, but you also need to have control of your timeline… Yeah it’s something we’re working on, where we’re trying to get the shitty people to not show up. It’s a product thing we’re working on.
It’s not hard to guess which “shitty people,” Hassan was referring to. Project Veritas also successfully confirmed the use of “shadow banning” by Twitter, a practice that the firm denied for some time. In 2016, Breitbart Tech reported from exclusive sources that shadow banning was a real phenomenon that was happening every single day. Breitbart News previous reporting stated:
According to the source, Twitter maintains a ‘whitelist’ of favored Twitter accounts and a ‘blacklist’ of unfavored accounts. Accounts on the whitelist are prioritized in search results, even if they’re not the most popular among users. Meanwhile, accounts on the blacklist have their posts hidden from both search results and other users’ timelines.
Our source was backed up by a senior editor at a major digital publisher, who told Breitbart that Twitter told him it deliberately whitelists and blacklists users. He added that he was afraid of the site’s power, noting that his tweets could disappear from users’ timelines if he got on the wrong side of the company.
Project Veritas confirmed Breitbart Tech’s reporting through an undercover conversation with former Twitter software engineer Abhinav Vadrevu who said the following to one of Project Veritas’ undercover reporters,
The idea of a shadow ban is that you ban someone but they don’t know they’ve been banned, because they keep posting but no one sees their content. So they just think that no one is engaging with their content, when in reality, no one is seeing it.
But at the end of the day, no one else interacts… No one else sees what you’re doing. So, all that data is just thrown away. It’s risky though. Because people will figure that shit out and be like… You know, it’s a lot of bad press if, like, people figure out that you’re like shadow banning them. It’s like, unethical in some way. You know? So, I don’t know.
In the past people have been really, really pissed off about that. And even people who haven’t been shadow banned have called it, like, a really terrible thing to do. So, yeah, it’s a risky strategy.
I definitely know Reddit does this, but I don’t know if Twitter does this anymore.
Read more about Project Veritas’ investigation at Breitbart News here.
Lucas Nolan is a reporter for Breitbart News covering issues of free speech and online censorship. Follow him on Twitter @LucasNolan or contact via secure email at the address firstname.lastname@example.org