Analyzing #MAGA’s Issue Agendas, Media Ecologies and Viral Images from 2016-2022

• In Event: The Conservative Media Ecosystem

Abstract

Twitter’s public implosion in 2022 after its acquisition by avowed “free speech absolutionist” Elon Musk has ignited widespread concern that it will devolve into a far-right platform. Musk has reinstated deplatformed accounts such as former President Donald Trump and Republican Majorie Taylor Green, gutted content moderation teams that curbed hate speech, censored traditional media journalists, and tweeted mis/disinformation, leading some to suggest he is a far-right activist. Hate speech now overwhelms the platform as progressives flock to alternative platforms like Mastodon. Before Musk’s acquisition, conservatives accused the platform and “big tech” of censoring and shadowbanning conservative accounts. Yet, we have underestimated the power of conservative agenda building on Twitter. Conservative gatekeepers across the spectrum have quietly exploited Twitter’s affordances since the election of Barack Obama in 2008 to generate disorder and chaos. These disruptive anti-democratic agendas, birthed and matured via the affordances of networks and algorithms, harness connective anti-elite sentiment to reveal pervasive white rage, grievance, and resentment. The negative affective sentiments have fueled political and societal violence, including the spectacle of the January 6, 2021 insurrection, and conspiracies as far reaching as election denying mis/disinformation narratives, and covid-19 as a hoax. This paper argues that far right agendas are far more organized than they appear on the surface, and that conservative agendas have been “hiding in plain sight” of Twitter’s algorithmic technology for many election cycles.
As scholars of technology and politics, we must reexamine these conservative communities through their longevity to trace how their issue platforms gain maturity and expression. Such careful analysis can help elucidate the role that Twitter has played in supporting subversive movements that threaten our society’s democratic health and our tolerance within our digital public sphere. Exploring how the conservative movement developed over time into its powerful political appeal requires analysis of conservative digital networks across multiple electoral cycles. The significance of such analysis in contemporary times is made urgent by the evolution of our technological space. As Twitter cedes way to newer applications, fringe publics are set to migrate algorithmic strategies culled from Twitter to new spaces like Gab, TikTok, Rumble and TruthSocial, making it crucial we study these communities that have taken full advantage of Twitter’s open architecture to craft enduring digital political strategies of subversion. Understanding how platforms permit malicious political outcomes requires understanding how conservatives have wielded technology to engineer a robust pseudoreality that drives an agenda of moral panic and paranoia across a variety of racial, economic, and cultural issues. In a quest to determine how Twitter supported these subversive movements, this paper also questions the role of white Christian identity and white identity politics as drivers of significant issues as elevated by Twitter conservative networks.
This study will center on #maga, the digital space that supported Donald Trump’s presidency but also provided the legacy of Trumpist ideology to the conservative movement. This study provides longitudinal analysis of Twitter’s #maga space over the past two presidential election cycles 2016 and 2020, and the recent 2022 midterm electoral cycles. In examining the evolution of the #maga movement over three separate time periods, this study identifies and compares, 1. The prominent issues promoted in each electoral cycle via hashtags and news headlines, 2. The prominent media relied on and shared in these cycles, and 3. The viral images spread and shared in each election cycle. Through examining the range of “small pieces loosely joined” content forms that are afforded within #maga tweets, it is possible to determine how Twitter’s connective affordances of loosely joined content enable agenda building on issues. The #maga sample includes 6.5 million tweets in total from 2016, 2020, and 2022. From these tweets, are over 3500 widely shared headlines and sources within tweets. Over 1500 images will be examined for issue agendas. Theories applied to this study include related political communication theories of media hybridity and networked, algorithmic journalism, digital agenda building via algorithmic affordances, and networked gatekeeping in hybrid media spaces. In examining the nature of #maga’s asymmetric polarization and mis/disinformation via aforementioned content markers, methods include a mix of qualitative and quantitative analysis, and include NLP for manipulating tweets, content and visual analysis (computerized and manual) for analysis of URLS and sources, and semantic network analysis and qualitive comparisons of issue centrality.

Author

• Sharon Meraz, University of Illinois at Chicago