Using Facebook data to predict the 2016 U.S. presidential election.
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ABSTRACT:
SUBMITTER: Chang KC
PROVIDER: S-EPMC8635376 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We use data from the American National Election Studies from 1996 to 2016 to study the role of the internet in the 2016 U.S. presidential election outcome. We compare trends in the Republican share of the vote between likely and unlikely internet users, and between actual internet users and non-users. Relative to prior years, the Republican share of the vote in 2016 was as high or higher among the groups least active online.
| S-EPMC6051565 | biostudies-literature
Project description:ImportanceIn the U.S. presidential election of 2016, substantial shift in voting patterns occurred relative to previous elections. Although this shift has been associated with both education and race, the extent to which this shift was related to public health status is unclear.ObjectiveTo determine the extent to which county community health was associated with changes in voting between the presidential elections of 2016 and 2012.DesignEcological study with principal component analysis (PCA) using principal axis method to extract the components, then generalized linear regression.SettingGeneral community.ParticipantsAll counties in the United States.ExposuresPhysically unhealthy days, mentally unhealthy days, percent food insecure, teen birth rate, primary care physician visit rate, age-adjusted mortality rate, violent crime rate, average health care costs, percent diabetic, and percent overweight or obese.Main outcomeThe percentage of Donald Trump votes in 2016 minus percentage of Mitt Romney votes in 2012 ("net voting shift").ResultsComplete public health data was available for 3,009 counties which were included in the analysis. The mean net voting shift was 5.4% (+/- 5.8%). Of these 3,009 counties, 2,641 (87.8%) had positive net voting shift (shifted towards Trump) and 368 counties (12.2%) had negative net voting shift (shifted away from Trump). The first principal component ("unhealthy score") accounted for 68% of the total variance in the data. The unhealthy score included all health variables except primary care physician rate, violent crime rate, and health care costs. The mean unhealthy score for counties was 0.39 (SD 0.16). Higher normalized unhealthy score was associated with positive net voting shift (22.1% shift per unit unhealthy, p < 0.0001). This association was stronger in states that switched Electoral College votes from 2012 to 2016 than in other states (5.9% per unit unhealthy, p <0.0001).Conclusions and relevanceSubstantial association exists between a shift toward voting for Donald Trump in 2016 relative to Mitt Romney in 2012 and measures of poor public health. Although these results do not demonstrate causality, these results suggest a possible role for health status in political choices.
| S-EPMC5624580 | biostudies-literature
Project description:BackgroundThe 2016 presidential election and the controversial policy agenda of its victor have raised concerns about how the election may have impacted mental health.ObjectiveAssess how mental health changed from before to after the November 2016 election and how trends differed in states that voted for Donald Trump versus Hillary Clinton.DesignPre- versus post-election study using monthly cross-sectional survey data.ParticipantsA total of 499,201 adults surveyed in the Behavioral Risk Factor Surveillance System from May 2016 to May 2017.ExposureResidence in a state that voted for Trump versus state that voted for Clinton and the candidate's margin of victory in the state.Main measuresSelf-reported days of poor mental health in the last 30 days and depression rate.Key resultsCompared to October 2016, the mean days of poor mental health in the last 30 days per adult rose from 3.35 to 3.85 in December 2016 in Clinton states (0.50 days difference, p = 0.005) but remained statistically unchanged in Trump states, moving from 3.94 to 3.78 days (- 0.17 difference, p = 0.308). The rises in poor mental health days in Clinton states were driven by older adults, women, and white individuals. The depression rate in Clinton states began rising in January 2017. A 10-percentage point higher margin of victory for Clinton in a state predicted 0.41 more days of poor mental health per adult in December 2016 on average (p = 0.001).ConclusionsIn states that voted for Clinton, there were 54.6 million more days of poor mental health among adults in December 2016, the month following the election, compared to October 2016. Clinicians should consider that elections could cause at least transitory increases in poor mental health and tailor patient care accordingly, especially with the 2020 election upon us.
| S-EPMC7602772 | biostudies-literature
Project description:Social media has become an emerging alternative to opinion polls for public opinion collection, while it is still posing many challenges as a passive data source, such as structurelessness, quantifiability, and representativeness. Social media data with geotags provide new opportunities to unveil the geographic locations of users expressing their opinions. This paper aims to answer two questions: 1) whether quantifiable measurement of public opinion can be obtained from social media and 2) whether it can produce better or complementary measures compared to opinion polls. This research proposes a novel approach to measure the relative opinion of Twitter users towards public issues in order to accommodate more complex opinion structures and take advantage of the geography pertaining to the public issues. To ensure that this new measure is technically feasible, a modeling framework is developed including building a training dataset by adopting a state-of-the-art approach and devising a new deep learning method called Opinion-Oriented Word Embedding. With a case study of tweets on the 2016 U.S. presidential election, we demonstrate the predictive superiority of our relative opinion approach and we show how it can aid visual analytics and support opinion predictions. Although the relative opinion measure is proved to be more robust than polling, our study also suggests that the former can advantageously complement the latter in opinion prediction.
| S-EPMC7244148 | biostudies-literature
Project description:ObjectiveResearch suggests that sociopolitical stressors connected with the 2016 presidential election were associated with increases in preterm birth among Latina women. This study determined whether periviable births (<26 weeks gestation), which exhibit extremely high rates of infant morbidity and mortality, among US Latina women increased above expected levels after the 2016 US presidential election.MethodsWe assigned singleton live births among Latina and non-Latina white women in the US to 96 monthly conception cohorts conceived from January 2009 through December 2016. We constructed risk ratios by dividing the rate of periviable birth among Latina women by the rate among non-Latina white women. We used time-series methods to determine if the risk ratio of periviable births in cohorts conceived by Latina women and exposed to the election of 2016 exceeded those expected from autocorrelation and calendar effects.ResultsWe found an outlying sequence of risk ratios among Latina women starting with the cohort conceived in April and ending with that conceived in November 2016. Increases in the ratios ranged from 0.07 above an expected of 1.61 for the cohort conceived in June, to 0.39 above an expected of 1.27 for the cohort conceived in April.ConclusionWe find that pregnancies in gestation at the time of the 2016 election among Latina women yielded more than expected periviable births. These findings support the argument that the prospect of anti-immigrant policies promised by the Trump campaign sufficiently stressed Latina women to affect the timing of birth.
| S-EPMC8128056 | biostudies-literature
Project description:IntroductionIn the 2016 U.S. Presidential election, voters in communities with recent stagnation or decline in life expectancy were more likely to vote for the Republican candidate than in prior Presidential elections. We aimed to assess the association between change in life expectancy and voting patterns in the 2020 Presidential election.MethodsWith data on county-level life expectancy from the Institute for Health Metrics and Evaluation and voting data from a GitHub repository of results scraped from news outlets, we used weighted multivariable linear regression to estimate the association between the change in life expectancy from 1980 to 2014 and the proportion of votes for the Republican candidate and change in the proportion of votes cast for the Republican candidate in the 2020 Presidential election.ResultsAmong 3110 U.S counties and Washington, D.C., change in life expectancy at the county level was negatively associated with Republican share of the vote in the 2020 Presidential election (parameter estimate -7.2, 95% confidence interval, -7.8 to -6.6). With the inclusion of state, sociodemographic, and economic variables in the model, the association was attenuated (parameter estimate -0.8; 95% CI, -1.5 to -0.2). County-level change in life expectancy was positively associated with change in Republican vote share 0.29 percentage points (95% CI, 0.23 to 0.36). The association was attenuated when state, sociodemographic, and economic variables were added (parameter estimate 0.24; 95% CI, 0.15 to 0.33).ConclusionCounties with a less positive trajectory in life expectancy were more likely to vote for the Republican candidate in the 2020 U.S. Presidential election, but the Republican candidate's share improved in some counties that experienced marked gains in life expectancy. Associations were moderated by demographic, social and economic factors.
| S-EPMC8209240 | biostudies-literature
Project description:Two studies conducted during the 2016 presidential campaign examined the dynamics of the objectivity illusion, the belief that the views of "my side" are objective while the views of the opposing side are the product of bias. In the first, a three-stage longitudinal study spanning the presidential debates, supporters of the two candidates exhibited a large and generally symmetrical tendency to rate supporters of the candidate they personally favored as more influenced by appropriate (i.e., "normative") considerations, and less influenced by various sources of bias than supporters of the opposing candidate. This study broke new ground by demonstrating that the degree to which partisans displayed the objectivity illusion predicted subsequent bias in their perception of debate performance and polarization in their political attitudes over time, as well as closed-mindedness and antipathy toward political adversaries. These associations, furthermore, remained significant even after controlling for baseline levels of partisanship. A second study conducted 2 d before the election showed similar perceptions of objectivity versus bias in ratings of blog authors favoring the candidate participants personally supported or opposed. These ratings were again associated with polarization and, additionally, with the willingness to characterize supporters of the opposing candidate as evil and likely to commit acts of terrorism. At a time of particular political division and distrust in America, these findings point to the exacerbating role played by the illusion of objectivity.
| S-EPMC7474588 | biostudies-literature
Project description:Contrary to the expectations of many, Hillary Clinton lost the 2016 U.S. presidential election. The initial shock to her supporters turned into despair for most, but not everyone was affected equally. We draw from the literature on political activism, identity, and self-other overlap in predicting that not all Clinton voters would be equivalently crushed by her loss. Specifically, we hypothesize that pre-election measures of political activism, and level of self-other identification between participants and Clinton-that is, how much a person was "with her"-will interact to predict the level of distress of Clinton voters two months later. Longitudinal data support our hypothesis. Notably, among Clinton voters, greater activism negatively predicted depressive symptoms, and positively predicted sleep quality, but only when participants were highly identified with Clinton. We discuss the implications of the results for theory and research on social action and well-being.
| S-EPMC6728069 | biostudies-literature
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.
| S-EPMC6315042 | biostudies-literature
Project description:Previous research suggests that stressors may trigger the onset of acute cardiovascular disease (CVD) events within hours to days, but there has been limited research around sociopolitical events such as presidential elections. Among adults ≥18 y of age in Kaiser Permanente Southern California, hospitalization rates for acute CVD were compared in the time period immediately prior to and following the 2016 presidential election date. Hospitalization for CVD was defined as an inpatient or emergency department discharge diagnosis of acute myocardial infarction (AMI) or stroke using International Classification of Diseases, 10th revision codes. Rate ratios (RR) and 95% confidence intervals (CIs) were calculated comparing CVD rates in the 2 d following the 2016 election to rates in the same 2 d of the prior week. In a secondary analysis, AMI and stroke were analyzed separately. The rate of CVD events in the 2 d after the 2016 presidential election (573.14 per 100,000 person-years [PY]) compared to the rate in the window prior to the 2016 election (353.75 per 100,000 PY) was 1.62 times higher (95% CI 1.17, 2.25). Results were similar across sex, age, and race/ethnicity groups. The RRs were similar for AMI (RR 1.67, 95% CI 1.00, 2.76) and stroke (RR 1.59, 95% CI 1.03, 2.44) separately. Transiently heightened cardiovascular risk around the 2016 election may be attributable to sociopolitical stress. Further research is needed to understand the intersection between major sociopolitical events, perceived stress, and acute CVD events.
| S-EPMC7604431 | biostudies-literature