Using the president's tweets to understand political diversion in the age of social media.
ABSTRACT: 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: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: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:There is considerable concern about the role that social media, such as Facebook and Twitter, play in promoting misperceptions during political campaigns. These technologies are widely used, and inaccurate information flowing across them has a high profile. This research uses three-wave panel surveys conducted with representative samples of Americans during both the 2012 and 2016 U.S. Presidential elections to assess whether use of social media for political information promoted endorsement of falsehoods about major party candidates or important campaign issues. Fixed effects regression helps ensure that observed effects are not due to individual differences. Results indicate that social media use had a small but significant influence on misperceptions about President Obama in the 2012 election, and that this effect was most pronounced among strong partisans. Social media had no effect on belief accuracy about the Republican candidate in that election. The 2016 survey focused on campaign issues. There is no evidence that social media use influenced belief accuracy about these topics in aggregate, but Facebook users were unique. Social media use by this group reduced issue misperceptions relative to those who only used other social media. These results demonstrate that social media can alter citizens' willingness to endorse falsehoods during an election, but that the effects are often small.
Project description:Twitter data are becoming an important part of modern political science research, but key aspects of the inner workings of Twitter streams as well as self-censorship on the platform require further research. A particularly important research agenda is to understand removal rates of politically charged tweets. In this article, I provide a strategy to understand removal rates on Twitter, particularly on politically charged topics. First, the technical properties of Twitter's API that may distort the analyses of removal rates are tested. Results show that the forward stream does not capture every possible tweet -between 2 and 5 percent of tweets are lost on average, even when the volume of tweets is low and the firehose not needed. Second, data from Twitter's streams are collected on contentious topics such as terrorism or political leaders and non-contentious topics such as types of food. The statistical technique used to detect uncommon removal rate patterns is multilevel analysis. Results show significant differences in the removal of tweets between different topic groups. This article provides the first systematic comparison of information loss and removal on Twitter as well as a strategy to collect valid removal samples of tweets.
Project description:Social media, such as Twitter, have become major channels of communication and commentary on popular culture, including conversations on our nation's leading addiction: tobacco. The current study examined Twitter conversations following two tobacco-related events in the media: (1) President Obama's doctor announcing that he had quit smoking and (2) the release of a photograph of Miley Cyrus (a former Disney child star) smoking a cigarette. With a focus on high-profile individuals whose actions can draw public attention, we aimed to characterise tobacco-related conversations as an example of tobacco-related public discourse and to present a novel methodology for studying social media.Tweets were collected 11-13 November 2011 (President Obama) and 1-3 August 2011 (Miley Cyrus) and analysed for relative frequency of terms, a novel application of a linguistic methodology.The President Obama data set (N=2749 tweets) had conversations about him quitting tobacco as well as a preponderance of information on political activity, links to websites, racialised terms and mention of marijuana. Websites and terms about Obama's smoke-free status were most central to the conversation. In the Miley Cyrus data (N=4746 tweets), terms that occurred with the greatest relative frequency were positive, emotional and supportive of quitting (eg, love, and please), with words such as 'love' most central to the conversation.People are talking about tobacco-related issues on Twitter, and semantic network analysis can be used to characterise on-line conversations. Future interventions may be able to harness social media and major current events to raise awareness of smoking-related issues.
Project description:Online social media such as Twitter are widely used for mining public opinions and sentiments on various issues and topics. The sheer volume of the data generated and the eager adoption by the online-savvy public are helping to raise the profile of online media as a convenient source of news and public opinions on social and political issues as well. Due to the uncontrollable biases in the population who heavily use the media, however, it is often difficult to measure how accurately the online sphere reflects the offline world at large, undermining the usefulness of online media. One way of identifying and overcoming the online-offline discrepancies is to apply a common analytical and modeling framework to comparable data sets from online and offline sources and cross-analyzing the patterns found therein. In this paper we study the political spectra constructed from Twitter and from legislators' voting records as an example to demonstrate the potential limits of online media as the source for accurate public opinion mining, and how to overcome the limits by using offline data simultaneously.
Project description:The aims of the present study were to identify and analyse the Diseases Neglected by the Media (DNMs) via a comparison between the most important health issues to the population of Espírito Santo, Brazil, from the epidemiological perspective (health value) and their effective coverage by the print media, and to analyse the DNMs considering the perspective of key journalists involved in the dissemination of health topics in the state media.Morbidity and mortality data were collected from official documents and from Health Information Systems. In parallel, the diseases reported in the two major newspapers of Espírito Santo in 2011-2012 were identified from 10,771 news articles. Concomitantly, eight interviews were conducted with reporters from the two newspapers to understand the journalists' reasons for the coverage or neglect of certain health/disease topics.Quantitatively, the DNMs identified diseases associated with poverty, including tuberculosis, leprosy, schistosomiasis, leishmaniasis, and trachoma. Apart from these, diseases with outbreaks in the period evaluated, including whooping cough and meningitis, some cancers, respiratory diseases, ischaemic heart disease, and stroke, were also seldom addressed by the media. In contrast, dengue fever, acquired immune deficiency syndrome (AIDS), diabetes, breast cancer, prostate cancer, tracheal cancer, and bronchial and lung cancers were broadly covered in the period analysed, corroborating the tradition of media disclosure of these diseases. Qualitatively, the DNMs included rare diseases, such as amyotrophic lateral sclerosis (ALS), leishmaniasis, Down syndrome, and verminoses. The reasons for the neglect of these topics by the media included the political and economic interests of the newspapers, their editorial line, and the organizational routine of the newsrooms.Media visibility acts as a strategy for legitimising priorities and contextualizing various realities. Therefore, we propose that the health problems identified should enter the public agenda and begin to be recognized as legitimate demands.
Project description:Background:Twitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control and Prevention activated its Emergency Operations Center and the World Health Organization released its first situation report about coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment evolved in the early stages of the COVID-19 pandemic has not been described. Methods:We extracted tweets matching hashtags related to COVID-19 from January 14 to 28, 2020 using Twitter's application programming interface. We measured themes and frequency of keywords related to infection prevention practices. We performed a sentiment analysis to identify the sentiment polarity and predominant emotions in tweets and conducted topic modeling to identify and explore discussion topics over time. We compared sentiment, emotion, and topics among the most popular tweets, defined by the number of retweets. Results:We evaluated 126 049 tweets from 53 196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Approximately half (49.5%) of all tweets expressed fear and approximately 30% expressed surprise. In the full cohort, the economic and political impact of COVID-19 was the most commonly discussed topic. When focusing on the most retweeted tweets, the incidence of fear decreased and topics focused on quarantine efforts, the outbreak and its transmission, as well as prevention. Conclusions:Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and potentially target individualized public health messages based on user interest and emotion.
Project description: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.