In this paper, we analyze the growth patterns of Donald Trump's followers (Trumpists, henceforth) on Twitter. We first construct a random walk model with a time trend to study the growth trend and the effects of public debates. We then analyze the relationship between Trump's activity on Twitter and the growth of his followers. Thirdly, we analyze the effects of such controversial events as calling for Muslim ban and his 'schlonged' remark.
There is a burgeoning literature in computer science on using social media data to analyze and predict elections. Research by @cite_4 finds that the number of messages mentioning a party reflects the election results. According to @cite_5 , the number of Facebook fans constitutes an indicator of candidate viability. @cite_6 use user profile images to study and compare the social demographics of Trump followers and Clinton followers. Similarly, our work is also motivated by the high parallel between performance in the polls and popularity in Twitter. @cite_3 employ LDA to model tweet topics and use negative binomial regression on the number of tweet likes' to infer topic preferences of Trump followers.
@cite_5 @cite_3 @cite_4 @cite_6
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In this paper, we propose a framework to infer the topic preferences of Donald Trump's followers on Twitter. We first use latent Dirichlet allocation (LDA) to derive the weighted mixture of topics for each Trump tweet. Then we use negative binomial regression to model the "likes," with the weights of each topic serving as explanatory variables. Our study shows that attacking Democrats such as President Obama and former Secretary of State Hillary Clinton earns Trump the most "likes." Our framework of inference is generalizable to the study of other politicians. The American Voter - a seminal work in political science - uncovered the multifaceted nature of voting behavior which has been corroborated in electoral research for decades since. In this paper, we leverage The American Voter as an analysis framework in the realm of computational political science, employing the factors of party, personality, and policy to structure the analysis of public discourse on online social media during the 2016 U.S. presidential primaries. Our analysis of 50 million tweets reveals the continuing importance of these three factors; our understanding is also enriched by the application of sentiment analysis techniques. The overwhelmingly negative sentiment of conversations surrounding 10 major presidential candidates reveals more "crosstalk" from Democratic leaning users towards Republican candidates, and less vice-versa. We uncover the lack of moderation as the most discussed personality dimension during this campaign season, as the political field becomes more extreme - Clinton and Rubio are perceived as moderate, while Trump, Sanders, and Cruz are not. While the most discussed issues are foreign policy and immigration, Republicans tweet more about abortion than Democrats who tweet more about gay rights than Republicans. Finally, we illustrate the importance of multifaceted political discourse analysis by applying regression to quantify the impact of party, personality, and policy on national polls. The language used in tweets from 1,300 different US counties was found to be predictive of the subjective well-being of people living in those counties as measured by representative surveys. Topics, sets of co-occurring words derived from the tweets using LDA, improved accuracy in predicting life satisfaction over and above standard demographic and socio-economic controls (age, gender, ethnicity, income, and education). The LDA topics provide a greater behavioural and conceptual resolution into life satisfaction than the broad socio-economic and demographic variables. For example, tied in with the psychological literature, words relating to outdoor activities, spiritual meaning, exercise, and good jobs correlate with increased life satisfaction, while words signifying disengagement like ’bored’ and ’tired’ show a negative association. In this work, we study the use of Twitter by House, Senate and gubernatorial candidates during the midterm (2010) elections in the U.S. Our data includes almost 700 candidates and over 690k documents that they produced and cited in the 3.5 years leading to the elections. We utilize graph and text mining techniques to analyze differences between Democrats, Republicans and Tea Party candidates, and suggest a novel use of language modeling for estimating content cohesiveness. Our findings show significant differences in the usage patterns of social media, and suggest conservative candidates used this medium more effectively, conveying a coherent message and maintaining a dense graph of connections. Despite the lack of party leadership, we find Tea Party members display both structural and language-based cohesiveness. Finally, we investigate the relation between network structure, content and election results by creating a proof-of-concept model that predicts candidate victory with an accuracy of 88.0 .