Strategic Identity Signaling in Heterogeneous Networks

Abstract

Individuals often signal identity information to facilitate assortment with partners who are likely to share norms, values, and goals. However, individuals may also be incentivized to encrypt their identity signals to avoid detection by dissimilar receivers, particularly when such detection is costly. Using mathematical modeling, this idea has previously been formalized into a theory of covert signaling. In this paper, we provide the first empirical test of the theory of covert signaling in the context of political identity signaling surrounding the 2020 U.S. presidential elections. We use novel methods relying on differences in detection between ingroup and outgroup receivers to identify likely covert and overt signals on Twitter. We strengthen our experimental predictions with a new mathematical modeling and examine the usage of selected covert and overt tweets in a behavioral experiment. We find that people strategically adjust their signaling behavior in response to the political constitution of their audiences and the cost of being disliked, in accordance with the formal theory. Our results have implications for our understanding of political communication, social identity, pragmatics, hate speech, and the maintenance of cooperation in diverse populations.