Stylistic variation on the Donald Trump Twitter account: A linguistic analysis of tweets posted between 2009 and 2018.
ABSTRACT: Twitter was an integral part of Donald Trump's communication platform during his 2016 campaign. Although its topical content has been examined by researchers and the media, we know relatively little about the style of the language used on the account or how this style changed over time. In this study, we present the first detailed description of stylistic variation on the Trump Twitter account based on a multivariate analysis of grammatical co-occurrence patterns in tweets posted between 2009 and 2018. We identify four general patterns of stylistic variation, which we interpret as representing the degree of conversational, campaigning, engaged, and advisory discourse. We then track how the use of these four styles changed over time, focusing on the period around the campaign, showing that the style of tweets shifts systematically depending on the communicative goals of Trump and his team. Based on these results, we propose a series of hypotheses about how the Trump campaign used social media during the 2016 elections.
Project description:Does president Trump's use of Twitter affect financial markets? The president frequently mentions companies in his tweets and, as such, tries to gain leverage over their behavior. We analyze the effect of president Trump's Twitter messages that specifically mention a company name on its stock market returns. We find that tweets from the president which reveal strong negative sentiment are followed by reduced market value of the company mentioned, whereas supportive tweets do not render a significant effect. Our methodology does not allow us to conclude about the exact mechanism behind these findings and can only be used to investigate short-term effects.
Project description:Social media has arguably shifted political agenda-setting power away from mainstream media onto politicians. Current U.S. President Trump's reliance on Twitter is unprecedented, but the underlying implications for agenda setting are poorly understood. Using the president as a case study, we present evidence suggesting that President Trump's use of Twitter diverts crucial media (The New York Times and ABC News) from topics that are potentially harmful to him. We find that increased media coverage of the Mueller investigation is immediately followed by Trump tweeting increasingly about unrelated issues. This increased activity, in turn, is followed by a reduction in coverage of the Mueller investigation-a finding that is consistent with the hypothesis that President Trump's tweets may also successfully divert the media from topics that he considers threatening. The pattern is absent in placebo analyses involving Brexit coverage and several other topics that do not present a political risk to the president. Our results are robust to the inclusion of numerous control variables and examination of several alternative explanations, although the generality of the successful diversion must be established by further investigation.
Project description:The COVID-19 pandemic likely had an effect on the outcome of the 2020 US presidential election. Was it responsible for the defeat of incumbent President Donald Trump? The present study makes an initial attempt at, and provides a model for, understanding the pandemic's influence on Trump support. The study employed a mixed experimental and correlational design and surveyed separate samples of adults (N = 1,763) in six waves beginning March 23, 2020 and ending June 1, 2020. Participants were randomly assigned to report their Trump support either before or after being reminded of the pandemic with a series of questions gauging their level of concern about it. Results revealed complex and dynamic effects that changed over time. Depending on survey wave, the pandemic seems to have lowered Trump support among Democrats, while (marginally) raising it among independents. Republicans' reactions also changed over time; of particular note, Republicans who were more concerned about the pandemic reported higher Trump support after being reminded of the pandemic in its early stages, but this effect reversed by the time the economy began reopening (coinciding with a dip in Trump's approval ratings). Although the correlational results in the present study did not converge neatly with the experimental results, the combined experimental and correlational approach has the potential to increase researchers' confidence in the causal effects of salient national and international events on political attitudes.
Project description:Technology and social media use are increasingly associated with delays in nightly sleep. Here, we consider the timing of President Trump's official Twitter account posts as a proxy for sleep duration and how it relates to his public performance. The President wakes around 6am, a routine which has not changed since early 2017. In contrast, the frequency of Twitter activity 11pm-2am increased 317% from under one day per week in 2017 to three days a week in 2020. The President's increased late-night activity is not accounted for by increases in the frequency of his use of social media over time, his travel schedule, or seasonality. On the day following one where he posts late at night, his Twitter followers interact less with his posts, described as "official statements by the President of the United States". He receives 7400 fewer likes per tweet, 1300 fewer retweets per tweet, and 1400 fewer replies per tweet after a late night (drops of 6.5%-8%). Tweets aside, the President's speeches and interview transcripts have previously been coded for their dominant emotion through text analysis. On the day following a late night, the President's inferred emotion is less likely to be "happy" and nearly three times more likely to be "angry" in his interviews and speeches. Finally, the 2020 election odds of the President's chief opponent also increase after a late night, while the President's are unchanged. The pattern we document is consistent with a progressive shortening of the President's sleep over his first term and compromised performance from sleep deprivation.
Project description:From many perspectives, the election of Donald Trump was seen as a departure from long-standing political norms. An analysis of Trump's word use in the presidential debates and speeches indicated that he was exceptionally informal but at the same time, spoke with a sense of certainty. Indeed, he is lower in analytic thinking and higher in confidence than almost any previous American president. Closer analyses of linguistic trends of presidential language indicate that Trump's language is consistent with long-term linear trends, demonstrating that he is not as much an outlier as he initially seems. Across multiple corpora from the American presidents, non-US leaders, and legislative bodies spanning decades, there has been a general decline in analytic thinking and a rise in confidence in most political contexts, with the largest and most consistent changes found in the American presidency. The results suggest that certain aspects of the language style of Donald Trump and other recent leaders reflect long-evolving political trends. Implications of the changing nature of popular elections and the role of media are discussed.
Project description:Immigration and demographic change have become highly salient in American politics, partly because of the 2016 campaign of Donald Trump. Previous research indicates that local influxes of immigrants or unfamiliar ethnic groups can generate threatened responses, but has either focused on nonelectoral outcomes or analyzed elections in large geographic units, such as counties. Here, we examine whether demographic changes at low levels of aggregation were associated with vote shifts toward an anti-immigration presidential candidate between 2012 and 2016. To do so, we compile a precinct-level dataset of election results and demographic measures for almost 32,000 precincts in the states of Florida, Georgia, Michigan, Nevada, Ohio, Pennsylvania, and Washington. We employ regression analyses varying model specifications and measures of demographic change. Our estimates uncover little evidence that influxes of Hispanics or noncitizen immigrants benefited Trump relative to past Republicans, instead consistently showing that such changes were associated with shifts to Trump's opponent.
Project description:Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite the large amount of work addressing this question, there has been no clear validation of online social media opinion trend with traditional surveys. Here we develop a method to infer the opinion of Twitter users by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to build an in-domain training set of the order of a million tweets. We validate our method in the context of 2016 US Presidential Election by comparing the Twitter opinion trend with the New York Times National Polling Average, representing an aggregate of hundreds of independent traditional polls. The Twitter opinion trend follows the aggregated NYT polls with remarkable accuracy. We investigate the dynamics of the social network formed by the interactions among millions of Twitter supporters and infer the support of each user to the presidential candidates. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of traditional surveys.
Project description:The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated by www.opensources.co , we find that 25% of these tweets spread either fake or extremely biased news. We characterize the networks of information flow to find the most influential spreaders of fake and traditional news and use causal modeling to uncover how fake news influenced the presidential election. We find that, while top influencers spreading traditional center and left leaning news largely influence the activity of Clinton supporters, this causality is reversed for the fake news: the activity of Trump supporters influences the dynamics of the top fake news spreaders.
Project description:BACKGROUND:Dementia is a prevalent disorder among adults and often subjects an individual and his or her family. Social media websites may serve as a platform to raise awareness for dementia and allow researchers to explore health-related data. OBJECTIVE:The objective of this study was to utilize Twitter, a social media website, to examine the content and location of tweets containing the keyword "dementia" to better understand the reasons why individuals discuss dementia. We adopted an approach that analyzed user location, user category, and tweet content subcategories to classify large publicly available datasets. METHODS:A total of 398 tweets were collected using the Twitter search application programming interface with the keyword "dementia," circulated between January and February 2018. Twitter users were categorized into 4 categories: general public, health care field, advocacy organization, and public broadcasting. Tweets posted by "general public" users were further subcategorized into 5 categories: mental health advocate, affected persons, stigmatization, marketing, and other. Placement into the categories was done through thematic analysis. RESULTS:A total of 398 tweets were written by 359 different screen names from 28 different countries. The largest number of Twitter users were from the United States and the United Kingdom. Within the United States, the largest number of users were from California and Texas. The majority (281/398, 70.6%) of Twitter users were categorized into the "general public" category. Content analysis of tweets from the "general public" category revealed stigmatization (113/281, 40.2%) and mental health advocacy (102/281, 36.3%) as the most common themes. Among tweets from California and Texas, California had more stigmatization tweets, while Texas had more mental health advocacy tweets. CONCLUSIONS:Themes from the content of tweets highlight the mixture of the political climate and the supportive network present on Twitter. The ability to use Twitter to combat stigma and raise awareness of mental health indicates the benefits that can potentially be facilitated via the platform, but negative stigmatizing tweets may interfere with the effectiveness of this social support.
Project description:Recently we have witnessed a number of rapid shifts toward populism in the rhetoric and policies of major political parties, as exemplified in the 2016 Brexit Referendum, 2016 US Election, and 2017 UK General Election. Our perspective here is to focus on understanding the underlying societal processes behind these recent political shifts. We use novel methods to study social dynamics behind the 2016 Presidential election. This is done by using network science methods to identify key groups associated with the US right-wing during the election. We investigate how the groups grew on Twitter, and how their associated accounts changed their following behaviour over time. We find a new external faction of Trump supporters took a strong influence over the traditional Republican Party (GOP) base during the election campaign. The new group dominated the GOP group in terms of new members and endorsement via Twitter follows. Growth of new accounts for the GOP party all but collapsed during the campaign. While the Alt-right group was growing exponentially, it has remained relatively isolated. Counter to the mainstream view, we detected an unexpectedly low number of automated 'bot' accounts and accounts associated with foreign intervention in the Trump-supporting group. Our work demonstrates a powerful method for tracking the evolution of societal groups and reveals complex social processes behind political changes.