This thesis explores the how the media filtered the rhetoric of candidate Donald Trump, specifically concerning how both right wing and left wing media reported his campaign announcement speech in June of 2015.

During the 2016 presidential election, public opinion polls consistently showed a lead in the popular vote and Electoral College for Hillary Clinton over Donald Trump. Following Trump's surprise victory, the political pundits and public at large began to question the accuracy of modern public opinion polling. Fielding a representative sample, convoluted and opaque methodologies, the sheer amount of polls, and both the media's and general public's inability to interpret poll results are among the flaws of the polling industry. An alternative or supplement to traditional polling practices is necessary. This thesis seeks to investigate whether Google Trends can be effectively used as a measure of public opinion during presidential elections. This study gathers polling data from the 2016 presidential election from states that were considered swing states. Specifically, this study examines six total polls, three from states that swung in the way the polls predicted they would – Nevada and Virginia – and three from states that swung against the prediction – Michigan, Wisconsin, and Pennsylvania. Answers to the "Most Important Issue" question in each poll are compared to their corresponding topics in Google Trends by calculating Pearson product moment correlations for each pair. Results indicated that in states that swung as predicted, Google Trends was an effective supplement to traditional public opinion polls. In states that did not swing as predicted, Google Trends was not an effective supplement. Implications of these results and future considerations for the polling industry and Google are discussed. / Master of Arts

Throughout the 2016 U.S. presidential election, public debate and media coverage was shaped by so called "fake news" – news articles which were intentionally false, and designed to influence opinion and policy. Although fake news itself is not a new concept, the way in which it was covered, and the was it was spread on social media platforms, was. Given this, scholarly literature examining fake news, and specifically the content or stylistic characteristics of fake news, is minimal. My research seeks to address that gap through examining different content characteristics of fake news articles spread on social media in 2016, and testing the impact of those characteristics on Facebook engagement (the number of likes or shares an article received). I find political fake news circulated during the 2016 U.S. election is relatively homogeneous in content: it avoids policy discussion, is highly partisan, and negative in tone. Furthermore, personal content, policy discussion, partisan lean, and article tone have no detectable effect on the engagement received on Facebook. My research serves to provide avenues for future research, and increase our understanding of how fake news is spread. More importantly, given the negative influence fake news has on public discussion and democratic legitimacy, my research also increases our understanding of how to best combat the influence of fake news, and how to limit its spread. / Master of Arts / Throughout the 2016 U.S. presidential election, public debate and media coverage was shaped by so called “fake news” – news articles which were intentionally false, and designed to influence opinion and policy. Although fake news itself is not a new concept, the way in which it was covered, and the was it was spread on social media platforms, was. Given this, scholarly literature examining fake news, and specifically the content or stylistic characteristics of fake news, is minimal. My research seeks to address that gap through examining different content characteristics of fake news articles spread on social media in 2016, and testing the impact of those characteristics on Facebook engagement (the number of likes or shares an article received). I find political fake news circulated during the 2016 U.S. election is relatively homogeneous in content: it avoids policy discussion, is highly partisan, and negative in tone. Furthermore, personal content, policy discussion, partisan lean, and article tone have no detectable effect on the engagement received on Facebook. My research serves to provide avenues for future research, and increase our understanding of how fake news is spread. More importantly, given the negative influence fake news has on public discussion and democratic legitimacy, my research also increases our understanding of how to best combat the influence of fake news, and how to limit its spread.

This thesis is concerned with the question of whether mainstream political media has been captured by misleading, narrative driven content during the post-primary portion of the 2016 United States presidential election cycle. I conducted an analysis of content of political articles and compared the factual information contained within to how the publication framed the story. I surveyed of two types of political news story from the election: conspiracy theory and scandal. When narrative influence was found, I then traced the narrative to its origin to determine which actors had gatekeeping power over mainstream media.

Social media has over the years partly become a platform to express opinions and discuss current events. Within the field of Computer Science, Twitter has been used both as the basis for political analysis - for example using sentiment analysis to predict election results - and within the field of cluster analysis, where the question of how to best design and use an algorithm to extract topics from tweets has been studied. The ClusTop algorithm is specifically designed to cluster tweets based on topics. This paper aims to explore whether it is possible to (a) use an implementation of the ClusTop algorithm to identify topics connected to tweets about Trump and Clinton just before the American 2016 election, and (b) distinguish between the topics used in connection with a specific candidate in states where they won versus states where they lost the election. The problem is approached through the method of a controlled experiment where the data collected from Twitter is divided into groups and run through the ClusTop algorithm. The topics are then compared to draw tentative conclusions about their validity as a basis for election prediction. The study finds that it is indeed possible to adapt the ClusTop algorithm to use with tweets and geolocation to identify different topics, thus confirming the usefulness of the algorithm. In addition to this, the study confirms that manually examining the words used within the topics makes it possible to see differences between them. The work thereby places itself in the tradition of exploring how Twitter can be used for election prediction by being one of the first studies to look at clustering as a way of approaching the problem.

This essay aims to analyze how Hillary Clinton and Donald Trump communicated verbally and non-verbally during the second presidential debate of the 2016 US election. To do this the essay utilizes a method focused on analyzing actio. The essay also analyses main trends in the rhetoric’s of the candidates. To aid the findings from this analysis the essay takes support from theories such as Impression management, footing, face as well as different theories in representation, including stereotypes, gender in politics and the construction of otherness.   The results that the study reached where that very little critic could be raised towards Clinton's actio except for questioning how effective her choice to face the live audience instead of the tv audience was. That only a few major errors could be found in Clinton's actio & her rhetoric as well, was theorized to be due to the stark contrast of her errors against Trump's which many times where a lot worse. The study also found some similarities in how to candidates used similar rhetorical and, to some extent, actio techniques to compel their audience, thou stark differences could be found in the execution of said techniques. Further the study could not point to any definite female or male use of body language, but theorized that this is due to the nature of a political debate and how Clinton might have adapted to the male dominated world of politics. Finally, in the end discussion a speculation was raised that whoever the audience perceived as a “winner” of the debate came down largely to pre-perceived notions of each candidate at least when it came to body language and rhetoric.

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This thesis asks what conditions elevated the Cambridge Analytica (CA) conflict into a sustained and global political issue? Was this a privacy conflict and if so, how was it framed as such? This work demonstrates that the public outcry to CA formed out of three underlying structural conditions: The rise of the alt-right as an ideology, surveillance capitalism, and a growing and unregulated voter analytics industry. A network of actors seized the momentum of this conflict to drive the message that voter surveillance is a threat to democratic elections. These actors humanized the CA conflict and created a catalyst for a large scale public outrage to these previously ignored structures. Their focus on democratic threat also allowed this conflict to transcend the typical contours of a privacy conflict and demonstrate that the consequences of CA are societal, rather than personal. Despite the democratic threat of voter surveillance, Canada and the United States have yet to address the wider implications of voter surveillance adequately. Thus, how these systems are used will be a question of central importance in upcoming elections. / Graduate