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The 2016 US presidential election was like none other in the history of US electoral cycles in relation to the populist appeal of candidates, Republican Donald Trump and Democrat Bernie Sanders. Donald Trump, riding a wave of populism among rural white and white evangelical voters, rose to victory over Hillary Clinton, who was dubbed the “establishment candidate” by dissenters. His victory brought the power of populism to the fore in the election of television personality with scant formal political experience. The 2016 US presidential election was also unprecedented in the depth and complexity of Russian interference in the US election, as cyberwarfare and the injection of fake news narratives by both bots and Russian paid operatives resulted in cascades of disinformation flooding social media channels. Networked publics were largely unable to determine fact from fiction as they sought the expertise of algorithmic gatekeepers in social media channels. The current tide of populism so pervasive in US politics is not unique, but mirrored by a worldwide gripping populist sentiment across Europe and Latin/South America, resulting in closed borders and anti immigrant sentiment. In this current global climate of disillusionment and distrust, networked publics are embracing personalized digital activism afforded by these connective platforms to voice opinions or affirm their political stances. Researchers must question how this turn to populism is expressed and facilitated through the prominent content choices elevated by networked publics. We must also interrogate the nature of this populism given the growth of hate speech, white nationalism, and anti-immigrant sentiment.
Several emerging studies have unearthed the impact of textual messages on Twitter and Facebook on the 2016 US presidential election, specifically as it relates to the spread of fake news narratives on the Internet. But to date, limited work has been conducted on the vital growing impact of political visual virals, specifically images and memes, on the shaping of political messages and political meanings that emerge in social media channels. Some work has been conducted from political candidates’ use of Instagram: Munoz & Towner (2017) found that political candidates often employ ideal candidate frames with their Instagram posts, and Towner and Munoz (2017) found these images are capable of setting the media agenda. Scholars like Shifman (2014) have revealed that aspects such as humor, flawed masculinity and ordinary authorship predict content becoming memetic, while Nahon & Hemsely (2013) found virals to essentially travel intact with speed and reach across variant networks. To date, however, little targeted work has been directed towards the comprehensive study of the political viral visual as a consequential content object. This gap in our field must be addressed giving the growing influence of memes and visual virals as the bedrock of participatory culture online. This current study grounds itself in the theoretical foundations of memetics and virality, as well as populism, in order to question how these political viral and memetic visuals shaped and revealed the network’s message about the promise of populism and the hope of an altered political reality. Centering on the 2016 presidential election cycle, this paper advances several targeted questions, that include 1, Who is presented in the visual content of the image, 2. What are the textual frames that emerge in the overlay of the imagery, 3. What is the relationship of the image content to establishment and anti-establishment rhetoric, 4. How is power advanced and (re)articulated by the viral image, and 5. What is the relationship of the visual viral to disinformation and fake news dissemination during the 2016 US presidential election. Ultimately the goal of these questions are to compare the messages that emerge about each candidate in relation to the political viral visuals that are elevated to prominence.
This study utilizes combined methodological toolkits that involve both automated and manual analysis. The feeds that will be examined include feeds for Donald Trump (#TrumpTrain, #Trump2016 and #MakeAmericaGreatAgain), Bernie Sanders (#FeelTheBern and #Bernie2016) and Hillary Clinton (#Hillary2016, #ImWithHer, and #HillYes). Over 10 million tweets will form the sample for extracting prominent viral visuals across the entire arc of the presidential election cycle from Feb-November 2016. Methodologies beyond statistical analysis will include natural language script parsing, content analysis (manual and automated), computerized semantic network analysis, and automated network frame analysis. In questioning whether these viral visual images resulted in a populist message that was truly democratic, this paper will ultimately question how these visuals served to advance a message about the meaning of populism in a reconfigured vision of a new American reality.