Tori Tran, “The Impact of Discursive Representations of Immigration in a Post Truth Era with a Focus on DACA”
Mentor: Sandra Pucci, Linguistics

The 2016 election of Donald Trump ushered in a level of anti-immigrant rhetoric not seen in recent years. While there have been multiple targets of this rhetoric, there is no denying that much of it focuses specifically on undocumented immigrants, including students brought to the US as young children and those seeking refugee status along the US southern border. Drawing on the work of Bhatia and Jenks (2018), the purpose of this study is to analyze Trump’s tweets from 2015-18 through the lens of historical authenticity, linguistic and semiotic action, and social impact to explore the effects of the words on public opinion and policy actions that impact schooling for undocumented immigrants, specifically those receiving Deferred Action for Childhood Arrivals (DACA). The study uses mediated ethnographic discourse analysis to search Trump’s twitter feed. Mediated Discourse Analysis (MDA) has been used to study popular media as a force in influencing opinion and belief. MDA is used in this study to examine the link between policy text/discourses and micro-level language, e.g. data in the form of tweets from @realdonaldtrump. These were gathered from an online archive that includes more than 34,000 tweets, including deleted tweets since 01/27/2017. The analytic approach looked at three specific discursive characteristics: fact construction, social action, and rhetorical appeals. We used line-by-line open coding, followed by axial coding, to look for relationships between the three factors. Preliminary findings speak to the notion of politics as performance rather than the exchange of substantive information related to politics or political agendas – with the targets of the tweets being ignored as individuals. Trump’s tweets on immigration consistently attempt to construct de-facto policy texts by emphatically pointing to “truths” that fall outside of faculty knowledge.