How do audiences make sense of and interact with political junk news on Facebook? How does the platform’s “emotional architecture” intervene in these sense-making, interactive processes? What kinds of mediated publics emerge on and through Facebook as a result? We study these questions through topic modeling 40,500 junk news articles, quantitatively analyzing their engagement metrics, and a qualitative comment analysis. This exploratory research design allows us to move between levels of public discourse, zooming in from cross-outlet talking points to microsociological processes of meaning-making, interaction, and emotional entrainment taking place within the comment boxes themselves. We propose the concepts of delighting and detesting engagement to illustrate how the interplay between audiences, platform architecture, and political junk news generates a bivalent emotional dynamic that routinely divides posts into highly “loved” and highly “angering.” We argue that high-performing (or in everyday parlance, viral) junk news bring otherwise disparate audience members together and orient their dramatic focus toward objects of collective joy, anger, or concern. In this context, the nature of political junk news is performative as they become resources for emotional signaling and the construction of group identity and shared feeling on social media. The emotions that animate junk news audiences typically refer back to a transpiring social relationship between two political sides. This affectively loaded “us” versus “them” dynamic is both enforced by Facebook’s emotional architecture and made use of by junk news publishers.

A central problem in the analysis of observational data is inferring causal relationships-what are the underlying causes of the observed behaviors? With the recent proliferation of Big Data from online social networks, it has become important to determine to what extent social influence causes certain messages to 'go viral', and to what extent other causes also play a role. In this paper, we present a causal framework showing that social influence is confounded with personal similarity, traits of the focal item, and external circumstances. Combined with a set of qualitative considerations on the combination of these sources of causation, we show how this framework can enable investigators to systematically evaluate, strengthen and qualify causal claims about social influence, and we demonstrate its usefulness and versatility by applying it to a variety of common online social datasets.

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