Ratioing the President: An exploration of public engagement with Obama and Trump on Twitter.
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ABSTRACT:
SUBMITTER: Minot JR
PROVIDER: S-EPMC8046224 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This article analyses public debate on Twitter via network representations of retweets and replies. We argue that tweets observable on Twitter have both a direct and mediated effect on the perception of public opinion. Through the interplay of the two networks, it is possible to identify potentially misleading representations of public opinion on the platform. The method is employed to observe public debate about two events: The Saxon state elections and violent riots in the city of Leipzig in 2019. We show that in both cases, (i) different opinion groups exhibit different propensities to get involved in debate, and therefore have unequal impact on public opinion. Users retweeting far-right parties and politicians are significantly more active, hence their positions are disproportionately visible. (ii) Said users act significantly more confrontational in the sense that they reply mostly to users from different groups, while the contrary is not the case.
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Project description:BackgroundLittle empirical evidence exists on the effectiveness of using Twitter as a two-way communication tool for public health practice, such as Twitter chats.ObjectiveWe analyzed whether Twitter chats facilitate engagement in two-way communications between public health entities and their audience. We also describe how to measure two-way communications, incoming and outgoing mentions, between users in a protocol using free and publicly available tools (Symplur, OpenRefine, and Gephi).MethodsWe used a mixed-methods approach, social network analysis, and content analysis. The study population comprised individuals and organizations participating or who were mentioned in the first #LiveFitNOLA chat, during a 75-min period on March 5, 2015, from 12:00 PM to 1:15 PM Central Time. We assessed audience engagement in two-way communications with two metrics: engagement ratio and return on engagement (ROE).ResultsThe #LiveFitNOLA chat had 744 tweets and 66 participants with an average of 11 tweets per participant. The resulting network had 134 network members and 474 engagements. The engagement ratios and ROEs for the #LiveFitNOLA organizers were 1:1, 40% (13/32) (@TulanePRC) and 2:1, -40% (-25/63) (@FitNOLA). Content analysis showed information sharing (63.9%, 314/491) and health information (27.9%, 137/491) as the most salient theme and sub-theme, respectively.ConclusionsOur findings suggest Twitter chats facilitate audience engagement in two-way communications between public health entities and their audience. The #LiveFitNOLA organizers' engagement ratios and ROEs indicated a moderate level of engagement with their audience. The practical significance of the engagement ratio and ROE depends on the audience, context, scope, scale, and goal of a Twitter chat or other organized hashtag-based communications on Twitter.
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Project description:Priming with race-typed names and religious concepts have been shown to activate stereotypes and increase prejudice towards out-groups. We examined the effects of name and religious word priming on views of a specific and well-known person, President Barack Obama. We predicted that politically conservative participants primed with President Obama's middle name (Hussein) would rate him more negatively and be more likely to view him as a Muslim than those not shown his middle name. We also examined whether conservatives primed with concrete religious words would rate President Obama more negatively and be more likely to view him as Muslim than those primed with other word types. Furthermore, we predicted that those who mis-identify President Obama as Muslim would rate him more negatively than would those who view him as Christian. The results provided mixed support for these hypotheses. Conservatives primed with President Obama's middle name rated him significantly more negatively than did those in the control condition. This effect was not found for politically liberal or moderate participants. Name priming did not significantly affect views of President Obama's religious affiliation. Although not statistically significant, conservatives primed with abstract religious words tended to rate President Obama more negatively than did those primed with other word types. Religious word priming significantly influenced views of President Obama's religious affiliation; interestingly, participants primed with abstract religious words were more likely to think President Obama is Muslim than were those primed with religious agent or non-religious words. As predicted, participants who thought president Obama was Muslim rated him significantly more negatively than did those who thought he was Christian. Overall, our results provide some evidence that ethnic name and religious word priming can significantly influence opinions, even with a well-known and specific person.
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Project description:ObjectivesTo quantify the effect of politics on the physical, psychological, and social health of American adults during the four-year span of the Trump administration.MethodsA previously validated politics and health scale was used to compare health markers in nationally representative surveys administered to separate samples in March 2017 (N = 800) and October 2020 (N = 700). Participants in the 2020 survey were re-sampled approximately two weeks after the 2020 election and health markers were compared to their pre-election baselines.ResultsLarge numbers of Americans reported politics takes a significant toll on a range of health markers-everything from stress, loss of sleep, or suicidal thoughts to an inability to stop thinking about politics and making intemperate social media posts. The proportion of Americans reporting these effects stayed stable or slightly increased between the spring of 2017 and the fall of 2020 prior to the presidential election. Deterioration in measures of physical health became detectably worse in the wake of the 2020 election. Those who were young, politically interested, politically engaged, or on the political left were more likely to report negative effects.ConclusionsPolitics is a pervasive and largely unavoidable source of chronic stress that exacted significant health costs for large numbers of American adults between 2017 and 2020. The 2020 election did little to alleviate those effects and quite likely exacerbated them.
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Project description:From the onset of the first confirmed case of COVID-19 in January 2020 to Election Day in November, the United States experienced over 9,400,000 cases and 232,000 deaths. This crisis largely defined the campaign between former Vice President Joe Biden and President Donald Trump, centering on the Trump administration's efforts in mitigating the number of cases and deaths. While conventional wisdom suggested that Trump and his party would lose support due to the severity of COVID-19 across the country, such an effect is hotly debated empirically and theoretically. In this research, we evaluate the extent to which the severity of the COVID-19 pandemic influenced support for President Trump in the 2020 election. Across differing modeling strategies and a variety of data sources, we find evidence that President Trump gained support in counties with higher COVID-19 deaths. We provide an explanation for this finding by showing that voters concerned about the economic impacts of pandemic-related restrictions on activity were more likely to support Trump and that local COVID-19 severity was predictive of these economic concerns. While COVID-19 likely contributed to Trump's loss in 2020, our analysis demonstrates that he gained support among voters in localities worst affected by the pandemic.Supplementary informationThe online version contains supplementary material available at 10.1007/s11109-022-09826-x.
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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.
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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.
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Project description:ObjectiveTo investigate factors associated with engagement of U.S. Federal Health Agencies via Twitter. Our specific goals are to study factors related to a) numbers of retweets, b) time between the agency tweet and first retweet and c) time between the agency tweet and last retweet.MethodsWe collect 164,104 tweets from 25 Federal Health Agencies and their 130 accounts. We use negative binomial hurdle regression models and Cox proportional hazards models to explore the influence of 26 factors on agency engagement. Account features include network centrality, tweet count, numbers of friends, followers, and favorites. Tweet features include age, the use of hashtags, user-mentions, URLs, sentiment measured using Sentistrength, and tweet content represented by fifteen semantic groups.ResultsA third of the tweets (53,556) had zero retweets. Less than 1% (613) had more than 100 retweets (mean = 284). The hurdle analysis shows that hashtags, URLs and user-mentions are positively associated with retweets; sentiment has no association with retweets; and tweet count has a negative association with retweets. Almost all semantic groups, except for geographic areas, occupations and organizations, are positively associated with retweeting. The survival analyses indicate that engagement is positively associated with tweet age and the follower count.ConclusionsSome of the factors associated with higher levels of Twitter engagement cannot be changed by the agencies, but others can be modified (e.g., use of hashtags, URLs). Our findings provide the background for future controlled experiments to increase public health engagement via Twitter.
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