Twitter’s algorithm does not seem to silence conservatives
SINCE LAUNCHING a policy on misleading information in May, Twitter has clashed with President Donald Trump. When he described mail-in ballots as substantially fraudulent, the platform told users to get the facts and linked to articles that proved otherwise. After Mr Trump threatened looters with deathwhen the looting starts, the shooting startsTwitter said his tweet broke its rules against glorifying violence. On July 28th the site took down a tweet by Donald Trump junior promoting a malaria drug for covid-19 that plenty of studies discredit. The president says that social media platforms totally silence conservatives voices. However, a study by The Economist finds the opposite. Twitters feed used to show people the latest posts from accounts they followed, but in 2016 it launched an algorithm to serve relevant tweets to users, even if they were days old and from unfamiliar accounts. We compared the two systems, and found that the recommendation engine appears to reward inflammatory language and outlandish claims. Our experiment began in June 2019, when we created a clone of Mr Trumps profile. This bot used his picture, biography and location, and followed the same people as he did. We used it to re-post some of the presidents old tweets over several weeks, so that the algorithm could learn what our Trump clone cared about. Then from September to December we checked every ten minutes if Mr Trump had tweeted something. If so, three things happened. First, our clone repeated the tweet. Second, we checked its Twitter feed and recorded the first 24 posts served by the algorithm. Finally, we simulated what a chronological feed might have looked like, using the 24 most recent tweets by accounts that Mr Trump follows. Our algorithmic and chronological feeds differed starkly. Nearly half the recommended tweets were from users whom Mr Trump does not follow. Using sentiment-analysis tools to extract feelings from text, we found the average curated tweet was more emotive, on every scale, than its chronological equivalentand more so than Mr Trumps own posts, too. Sentiment analysis can be confusing. The emotional scores assigned to tweets by, say, Sean Hannity, a right-wing pundit, might be highly negativenot because they reflect poorly on him, but because he stridently criticises others, such as Democrats. Nonetheless, in a sample of 120,000 tweets, the posts recommended by the algorithm were more likely to sit near either end of a positive-to-negative spectrum. Twitter might also boost extreme views. Researchers at Indiana University have classified a list of left- and right-wing websites as untrustworthy or hyper-partisan. We found 1,647 links to such domains on our clones algorithmic feed, but only 895 on the chronological one. (Almost all cases on both feeds were right-wing.) Our experiment ended when a change in Twitters interface broke our bot. The platform also suspended another Trump clone that copied his looting threat. However, if an algorithmic penchant for sensationalism has remained, then Twitter may be amplifying and profiting from misleading tweets, rather than removing them. Its business is serving ads to 330m users, even if that means grabbing their attention by showing them exactly what they want to believe. Flagging a presidential whopper every now and then will not change that. Sources: Twitter; Technology Review; Chen et al. (working paper); The Economist