No abstract

We offer comprehensive evidence of preferences for ideological congruity when people engage with politicians, pundits, and news organizations on social media. Using 4 years of data (2016–2019) from a random sample of 1.5 million Twitter users, we examine three behaviors studied separately to date: (i) following of in-group versus out-group elites, (ii) sharing in-group versus out-group information (retweeting), and (iii) commenting on the shared information (quote tweeting). We find that the majority of users (60%) do not follow any political elites. Those who do follow in-group elite accounts at much higher rates than out-group accounts (90 versus 10%), share information from in-group elites 13 times more frequently than from out-group elites, and often add negative comments to the shared out-group information. Conservatives are twice as likely as liberals to share in-group versus out-group content. These patterns are robust, emerge across issues and political elites, and exist regardless of users’ ideological extremity.

Analyzing four years of data from a random sample of about 1.5 million Twitter users (and about 180,000 politically engaged users), we revisit the debate regarding the extent to which social media users live in political ``echo chambers'' with two new analytic approaches. First, we focus on the sharing of content from political elites, arguably the most influential and politically active actors, and estimate the extent to which ordinary users share messages from politicians, pundits, and news media of the same versus opposing ideology. Second, we examine the extent to which this sharing is annotated by users before it is shared (``quoted retweets'') and the tone of these annotations (e.g., do users share out-group content with negative commentary?). We find clear patterns indicative of echo-chambers: the politically engaged users analyzed share in-group messages from elites 14 times more frequently than out-group messages; and in the rare instances when out-group information is shared, a non-trivial amount of times it is accompanied by negative comments. These patterns emerge after accounting for how many in-group versus out-group elites a person follows, and are robust to the political interest of the user or extremity of the elite accounts, the topic of the tweet, and the type of political elite source of the original message. In line with previous research, we also find that this echo chamber is especially pronounced among conservative users, who are about twice as likely as liberals to share in-group vs out-group content. These findings have important implications for how we theorize and study online echo chambers.

This project differentiates between communication that praises one's political in-group ( in-group praise), attacks the opposition ( out-group derogation), or focuses on policy details ( evidence based), testing their effects on network and attitude polarization. We begin with an agent-based model, which shows that congenial evidence-based exchanges polarize the network and the inclusion of identity-driven communications leads to greater polarization. Once out-group derogation reaches a certain threshold, the network of agents splits into two groups, yet the polarizing effects of in-group praise are yet stronger and emerge more rapidly (i.e., a lower threshold of in-group praise is needed to polarize the network). Using an experimental design on a sample of American partisans, we offer a partial validation of the model. In-group praise and out-group derogation polarize attitudes more than balanced evidence-based news, but not more than congenial evidence-based news. Identity-driven news also has no effects on affective polarization. This multidisciplinary evidence shows that the nature of political content matters.

Americans view their in-party members positively and out-party members negatively. It remains unclear, however, whether in-party affinity (i.e., positive partisanship) or out-party animosity (i.e., negative partisanship) more strongly influences political attitudes and behaviors. Unlike past work, which relies on survey self-reports or experimental designs among ordinary citizens, this pre-registered project examines actual social media expressions of an exhaustive list of American politicians as well as citizens’ engagement with these posts. Relying on 1,195,844 tweets sent by 564 political elites (i.e., members of US House and Senate, Presidential and Vice-Presidential nominees from 2000 to 2020, and members of the Trump Cabinet) and machine learning to reliably classify the tone of the tweets, we show that elite expressions online are driven by positive partisanship more than negative partisanship. Although politicians post many tweets negative toward the out-party, they post more tweets positive toward their in-party. However, more ideologically extreme politicians and those in the opposition (i.e., the Democrats) are more negative toward the out-party than those ideologically moderate and whose party is in power. Furthermore, examining how Twitter users react to these posts, we find that negative partisanship plays a greater role in online engagement: users are more likely to like and share politicians’ tweets negative toward the out-party than tweets positive toward the in-party. This project has important theoretical and democratic implications, and extends the use of trace data and computational methods in political behavior.