Shared partisanship dramatically increases social tie formation in a Twitter field experiment

Abstract

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. We created bot accounts that self-identified as people who favored 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 behavior: Democrats and Republicans alike were much more likely to reciprocate follows from copartisans. 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.

The Psychology of Fake News

“…Fake news and other forms of online misinformation represent a powerful (and ecologically valid) testing ground for evaluating theories from cognitive, social, and political psychology. More so than many other domains, when studying fake news and misinformation it is often possible to combine traditional laboratory experiments with large-scale social media data [71,109] or even to conduct actual field experiments on-platform [21,110,111]. Furthermore, these topics also motivate theory development by highlighting a new class of problems in need of further explanation: only a small fraction of online content draws sufficient attention and interest to be shared on social media.…”

Preference Falsification: How Social Conformity as an Interdependent, Recursive, and Multilevel Process Corrupts Public Knowledge

“…For example, text-analysis (e.g., analyzing aggregate Twitter data across time; Kennedy et al, 2021) tracks dynamic changes in public opinion, the stock market, public sentiment, and election outcomes (e.g., Asur & Huberman, 2010;Bollen et al, 2011;Liu, 2012;O'Connor et al, 2010;Pang & Lee, 2008;Tumasjan et al, 2010). Moreover, network analysis of user behavior on Twitter, and the behavior of accounts within a social network, tracks individual differences in cognitive reflection (Mosleh et al, 2021), as well as antecedents of online political segregation (Goldenberg et al, 2020). These findings suggest that studying online behaviors across large networks of people can reveal how social norms and pressures fluctuate across time, and how these fluctuations correspond with more or less PF online.…”

Field experiments on social media

“…One possibility is to examine how users' willingness to form social ties with an experimenter account varies based on the characteristics of the account. For example, one experiment randomly assigned a politically balanced set of Twitter users to be followed by researcher accounts that described themselves as Republican or Democrat (Figure 1), and found that users were almost three times more likely to follow-back co-partisan accounts compared to counter-partisan accounts (Mosleh, Martel, Eckles, & Rand, 2021b). (Mosleh, Martel, et al, 2021b) Another possibility is to examine how treatments affect subsequent social media behavior (e.g., content that users share, like, etc.).…”

“…Figure 1. (a) Examples of researcher Twitter accounts used in(Mosleh, Martel, et al, 2021b) to investigate the effect of shared partisanship on social tie formation. (b) Probability of followingback the researcher accounts in each experimental condition.…”