Share
  •  
  •  
  •  
  •  
  •  

A study examines links between partisanship and ties on Twitter. Many people value partisanship as key to their identity. However, it is unclear whether individuals are more likely to form social connections based entirely on shared partisanship.
Mohsen Mosleh, David Rand, and colleagues created eight Twitter bots designed to appear human. Four profiles identified as Republican, four identified as Democrat, and each bot differed in terms of partisanship strength.
Each bot identified with a party in its profile description and followed and shared content from elite accounts, such as politicians, with similar political leanings. Bots with extreme partisanship also featured a background image stating support for either Donald Trump or Joseph Biden. The authors also identified 842 Twitter users who had retweeted MSNBC or Fox News posts.
After assessing users’ partisanship based on their tweets, the authors randomly assigned each user to be followed by one of the bots. Users were almost three times more likely to follow a copartisan bot than a counter-partisan bot, regardless of the bot’s partisanship strength.
Liberal-leaning and conservative-leaning users were equally likely to follow bots that shared their political leanings. The findings suggest that like-minded users follow each other not only because of algorithmic suggestions or pre-existing offline relationships but also because of a basic tendency to connect with copartisans, according to the authors.
Americans are much more likely to be socially connected to copartisans, both in daily life and on social media. However, this observation does not necessarily mean that shared partisanship per se drives social tie formation because partisanship is confounded with many other factors. Here, we test the causal effect of shared partisanship on the formation of social ties in a field experiment on Twitter.
Proceedings of the National Academy of Sciences of the United States of America created bot accounts that self-identified as people who favoured the Democratic or Republican party and that varied in the strength of that identification. We then randomly assigned 842 Twitter users to be followed by one of our accounts. Users were roughly three times more likely to reciprocally follow-back bots whose partisanship matched their own, and this was true regardless of the bot’s strength of identification.

Interestingly, there was no partisan asymmetry in this preferential follow-back behaviour: Democrats and Republicans alike were much more likely to reciprocate follows from co-partisans. These results demonstrate a strong causal effect of shared partisanship on the formation of social ties in an ecologically valid field setting and have important implications for political psychology, social media, and the politically polarized state of the American public.
Partisanship is a core element of social identity for many people. For example, Americans tend to distrust and dislike those from the opposing political party and often report that they are unwilling to be friends with members of the opposing party.
In line with this self-reported dislike for counter-partisans, observational studies find that Americans are substantially more likely to have face-to-face social interactions with co-partisans and to be connected to co-partisans on social media networks–all of which may contribute to “echo chambers” where like-minded individuals preferentially exchange information with, and influence, those who share similar worldviews.
However, are people actually more likely to form social ties purely based on shared partisanship? Observational studies documenting assortment based on partisanship (sometimes described as homophily) do not offer credible evidence of a causal effect of shared partisanship on tie formation. Copartisanship is correlated with a multitude of other factors that are also likely to influence social tie formation.
For example, individuals may simply be forming social ties based on other factors that happen to be correlated with partisanship, such as age, race, geographic location, or other interests and preferences. Furthermore, it may be that people have more opportunities to form ties with co-partisans, rather than an actual preference for forming copartisan ties. In the context of social media in particular, recommendation algorithms may be preferentially suggesting like-minded users as new potential contacts, According to the Proceedings of the National Academy of Sciences of the United States of America.
Thus, experimental evidence on social tie formation is needed. Because one cannot easily randomly introduce individuals to one another and examine who decides to befriend whom, nearly all research investigating causal effects of shared partisanship on interpersonal dynamics has relied on hypothetical self-report measures in survey experiments; a notable exception involves experimentally using a dating website to document copartisan preference in romantic relationships. As a result, despite all of the interest in this area, the extent to which people condition on partisanship when actually forming social ties “in the wild” remains a largely open question.
Here, we shed light on this issue. We do so by leveraging the power of field experiments on social media to allow for the causal identification of copartisanship’s influence on actual social tie formation. Specifically, we created Twitter accounts that varied in their partisanship and examined how likely Twitter users were to reciprocate social tie formation when followed by copartisan versus counter partisan accounts. Our bot accounts were designed to appear as humans with identical descriptions, except 1) which political party they identified with and 2) the strength of that identification.