Abstract

Social media is an important source of news and information in the United States. But during the 2016 US presidential election, social media platforms emerged as a breeding ground for influence campaigns, conspiracy, and alternative media. Anecdotally, the nature of political news and information evolved over time, but political communication researchers have yet to develop a comprehensive, grounded, internally consistent typology of the types of sources shared. Rather than chasing a definition of what is popularly known as “fake news,” we produce a grounded typology of what users actually shared and apply rigorous coding and content analysis to define the phenomenon. To understand what social media users are sharing, we analyzed large volumes of political conversations that took place on Twitter during the 2016 presidential campaign and the 2018 State of the Union address in the United States. We developed the concept of “junk news,” which refers to sources that deliberately publish misleading, deceptive, or incorrect information packaged as real news. First, we found a 1:1 ratio of junk news to professionally produced news and information shared by users during the US election in 2016, a ratio that had improved by the State of the Union address in 2018. Second, we discovered that amplifier accounts drove a consistently higher proportion of political communication during the presidential election but accounted for only marginal quantities of traffic during the State of the Union address. Finally, we found that some of the most important units of analysis for general political theory—parties, the state, and policy experts—generated only a fraction of the political communication.

Highlights

  • The spread of misinformation over social media platforms has become a critical public interest issue

  • What are the salient qualities of political news and information shared over social media and how much of the content purporting to be political news and information during the two events was junk news? Based on our grounded typology, Table 2 breaks down the categorical proportions and total counts of sources that citizens shared on Twitter

  • Social media remains an important source of news and information, it is difficult to know how much political learning occurs on these platforms or how to model the impact a tweet can have on voter turnout or decisions

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Summary

Introduction

The spread of misinformation over social media platforms has become a critical public interest issue. Scholars have argued that the spread of “computational propaganda” sustained by algorithms can negatively impact democratic discourse (Bradshaw & Howard, 2018a; Marchal & Neudert, 2019; Persily, 2017; Tucker, Theocharis, Roberts, & Barberá, 2017; Woolley & Howard, 2016, 2017). Both social network infrastructure and user behaviors provide capacities and constraints for the spread of misinformation (Bradshaw & Howard, 2018b; Flaxman, Goel, & Rao, 2016; Marwick & Lewis, 2017; Pariser, 2011; Wu, 2017). The body of work that is devoted to conceptualizing the broader “information disorder” (Wardle & Derakhshan, 2017) has remained fragmentary, lacks a common vocabulary, and is increasingly weaponized by politically motivated actors (Neudert, Howard, & Kollanyi, 2017; Boyd, 2017; Fletcher, Cornia, Graves, & Nielsen, 2017)

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