How Shared Partisanship Leads to Social Media Connections

To conduct the experiment, the researchers collected a list of Twitter users who had retweeted either MSNBC or Fox News tweets, and then examined their last 3,200 tweets to see how much information those people shared from left-leaning or right-leaning websites. From the initial list, the scholars then constructed a final roster of

842 Twitter accounts, evenly distributed across the two major parties.

At the same time, the researchers created a set of eight clearly partisan bots — fake accounts with the appearance of being politically minded individuals. The bots were split by party and varied in intensity of political expression. The researchers randomly selected one of the bots to follow each of the 842 real users on Twitter. Then they examined which real-life Twitter users followed the politically aligned bots back, and observed the distinctly partisan pattern that emerged.

Overall, the real Twitter users in the experiment would follow back about 15 percent of Twitter bots with whom they shared partisan views, regardless of the intensity of political expression in the bot accounts. By contrast, the real-life Twitter users would only follow back about 5 percent of accounts that appeared to support the opposing party.

Among other things, the study found a partisan symmetry in the user behavior they observed: People from the two major U.S. parties were equally likely to follow accounts back on the basis of partisan identification.

“There was no difference between Democrats and Republicans in this, in that Democrats were just as likely to have preferential tie formation as Republicans,” says Rand.

The bot accounts used in this experiment were not recommended by Twitter as accounts that users might want to follow, indicating that the tendency to follow other partisans happens independently of the account-recommendation algorithms that social networks use.

“What this suggests is the lack of cross-partisan relationships on social media isn’t only the consequence of algorithms,” Rand says. “There are basic psychological predispositions involved.”

At the same time, Rand notes, the findings do suggest that if social media companies want to increase cross-partisan civic interaction, they could try to engineer more of those kinds of interactions.

“If you want to foster cross-partisan social relationships, you don’t just need the friend suggestion algorithm to be neutral. You would need the friend suggestion algorithms to actively counter these psychological predispositions,” Rand says, although he also notes that whether cross-partisan social ties actually reduce political polarization is unclear based on current research.

Therefore, the behavior of social media users who form connections across party lines is an important subject for future studies and experiments, Mosleh suggests. He also points out that this experimental approach could be used to study a wide range of biases in the formation of social relationships beyond partisanship, such as race, gender, or age.

Peter Dizikes is the social sciences, business, and humanities writer at the MIT News Office. The article is reprinted with permission of MIT News.