Male twinning after the 2008 Obama election: A test of symbolic empowerment.

On November 4, 2008, Barack Obama was elected the first Black President of the United States. His campaign and electoral win served as a symbol of hope for a more just future, fostering an "Obama effect" that appears associated with improved well-being among non-Hispanic (NH) Black communities. Situating the Obama election within the symbolic empowerment framework, we consider the potentially protective role of the Obama election on NH Black fetal death, an important but understudied measure of perinatal health that has stark racial disparities. Using restricted-use natality files from the National Center for Health Statistics, we proxy fetal death using the male twin rate (number of twins per 1000 male live births). Male twins have a relatively high risk of in utero selection that is sensitive to maternal and environmental stressors, making the twin rate an important marker of fetal death. We then estimate interrupted time-series models to assess the relation between the Obama election and male twin rates among NH Black births across monthly conception cohorts (February 2003-October 2008). Greater-than-expected male twin rates signal less susceptibility to fetal loss. Results indicate a 4.5% higher male twin rate among all NH Black cohorts exposed in utero to the Obama election, after accounting for historical and NH white trends (p < 0.005). The greater-than-expected rates concentrated among births conceived in the months preceding Obama's nomination at the Democratic National Convention and Obama's presidential win. These results suggest a salutary perinatal response to election events that likely reduced NH Black fetal loss. They also indicate the possibility that sociopolitical shifts can mitigate persisting NH Black-NH white disparities in perinatal health.

Systematic discrepancies in the delivery of political ads on Facebook and Instagram.

Political advertising on social media has become a central element in election campaigns. However, granular information about political advertising on social media was previously unavailable, thus raising concerns regarding fairness, accountability, and transparency in the electoral process. In this article, we analyze targeted political advertising on social media via a unique, large-scale dataset of over 80,000 political ads from Meta during the 2021 German federal election, with more than 1.1 billion impressions. For each political ad, our dataset records granular information about targeting strategies, spending, and actual impressions. We then study (i) the prevalence of targeted ads across the political spectrum; (ii) the discrepancies between targeted and actual audiences due to algorithmic ad delivery; and (iii) which targeting strategies on social media attain a wide reach at low cost. We find that targeted ads are prevalent across the entire political spectrum. Moreover, there are considerable discrepancies between targeted and actual audiences, and systematic differences in the reach of political ads (in impressions-per-EUR) among parties, where the algorithm favor ads from populists over others.

The interplay of social rank perceptions of Trump and Biden and emotions following the U.S. presidential election 2020.

The outcome of the 2020 U.S. election between Trump and Biden evoked strong emotions. In U.S. American (Study 1; N = 405) and German (Study 2; N = 123) samples, we investigated how observers' group membership (i.e. political orientation) and the social rank attainment of both candidates (i.e. dominance vs. prestige) predicted emotional reactions. Trump was generally perceived as more dominant, and Biden as more prestigious. However, perceptions of social rank attainment differed depending on the observers' political orientation, either matching or not matching with the leaders (i.e. Republicans and Democrats, respectively). The candidate who did not share the participants' political orientation was perceived as less prestigious and more dominant and elicited stronger contrastive emotions (i.e. schadenfreude, malicious envy) and weaker assimilative emotions (i.e. happy-for-ness, sympathy, anger), and vice versa. Crucially, dominance and prestige perceptions explained variance in the emotional reactions of more conservative and more liberal participants. Prestige positively predicted assimilative emotions and dominance contrastive emotions. Our work advances theorising by providing evidence that dominance and prestige perceptions contribute to the elicitation of various emotions. Furthermore, it suggests that prestige and dominance are not fixed characteristics of liberal and conservative leaders but depend on the observers' group membership.

Did private election administration funding advantage Democrats in 2020?

Private donors contributed more than $350 million to local election officials to support the administration of the 2020 election. Supporters argue these grants were neutral and necessary to maintain normal election operations during the pandemic, while critics worry these grants mostly went to Democratic strongholds and tilted election outcomes. How much did these grants shape the 2020 presidential election? To answer this question, we collect administrative data on private election administration grants and election outcomes. We then use advances in synthetic control methods to compare presidential election results and turnout in counties that received grants to counties with similar election results and turnout before 2020. While Democratic counties were more likely to apply for a grant, we find that the grants did not have a noticeable effect on the presidential election. Our estimates of the average effect on Democratic vote share range from 0.03 to 0.36 percentage points. Our estimates of the average effect of receiving a grant on turnout range from 0.03 to 0.14 percentage points. Across specifications, our 95% CIs typically include negative effects and all fail to include effects on Democratic vote share larger than 0.58 percentage points and effects on turnout larger than 0.40 percentage points. We characterize the magnitude of our effects by asking how large they are compared to the margin by which Biden won the 2020 election. In simple bench-marking exercises, we find that the effects of the grants were likely too small to have changed the outcome of the 2020 presidential election.

A Multilevel Spatial Model to Investigate Voting Behaviour in the 2019 UK General Election.

This paper presents a modelling framework which can detect the simultaneous presence of two different types of spatial process. The first is the variation from a global mean resulting from a geographical unit's 'vertical' position within a nested hierarchical structure such as the county and region where it is situated. The second is the variation at the smaller scale of individual units due to the 'horizontal' influence of nearby locations. The former is captured using a multi-level modelling structure while the latter is accounted for by an autoregressive component at the lowest level of the hierarchy. Such a model not only estimates spatially-varying parameters according to geographical scale, but also the relative contribution of each process to the overall spatial variation. As a demonstration, the study considers the association of a selection of socio-economic attributes with voting behaviour in the 2019 UK general election. It finds evidence of the presence of both types of spatial effects, and describes how they suggest different associations between census profile and voting behaviour in different parts of England and Wales.

The effects of Facebook and Instagram on the 2020 election: A deactivation experiment.

We study the effect of Facebook and Instagram access on political beliefs, attitudes, and behavior by randomizing a subset of 19,857 Facebook users and 15,585 Instagram users to deactivate their accounts for 6 wk before the 2020 U.S. election. We report four key findings. First, both Facebook and Instagram deactivation reduced an index of political participation (driven mainly by reduced participation online). Second, Facebook deactivation had no significant effect on an index of knowledge, but secondary analyses suggest that it reduced knowledge of general news while possibly also decreasing belief in misinformation circulating online. Third, Facebook deactivation may have reduced self-reported net votes for Trump, though this effect does not meet our preregistered significance threshold. Finally, the effects of both Facebook and Instagram deactivation on affective and issue polarization, perceived legitimacy of the election, candidate favorability, and voter turnout were all precisely estimated and close to zero.

Voter emotional responses and voting behaviour in the 2020 US presidential election.

Political polarisation in the United States offers opportunities to explore how beliefs about candidates - that they could save or destroy American society - impact people's thoughts, feelings, and behaviour. Participants forecast their future emotional responses to the contentious 2020 U.S. presidential election, and reported their actual responses after the election outcome. Stronger beliefs about candidates were associated with forecasts of greater emotion in response to the election, but the strength of this relationship differed based on candidate preference. Trump supporters' forecast happiness more strongly related to beliefs that their candidate would save society than for Biden supporters. Biden supporters' forecast anger and fear were more strongly related to beliefs that Trump would destroy society than vice versa. These forecasts mattered: predictions of lower happiness and greater anger if the non-preferred candidate won predicted voting, with Biden supporters voting more than Trump supporters. Generally, participants forecast more emotion than they experienced, but beliefs altered this tendency. Stronger beliefs predicted experiencing more happiness or more anger and fear about the election outcome than had been forecast. These findings have implications for understanding the mechanisms through which political polarisation and rhetoric can influence voting behaviour.

Short-term changes in mental health help-seeking behaviors following exposure to multiple social stressors and a natural disaster.

In 2020, unprecedented circumstances led to significant mental health consequences. Individuals faced mental health stressors that extended beyond the devastating impact of the COVID-19 pandemic, including widespread social unrest following the murder of George Floyd, an intense hurricane season in the Atlantic, and the politically divisive 2020 election. The objective of this analysis was to consider changes in help-seeking behavior following exposure to multiple social stressors and a natural disaster. Data from Crisis Text Line (CTL), a national text-based mental health crisis counseling service, was used to determine how help-seeking behavior changed in the wake of each event. Wilcoxon rank sum tests assessed changes in help-seeking behavior for each event in 2020 as compared to the same period in 2019. AutoRegressive Integrated Moving Average (ARIMA) models examined if changes in crisis conversation volumes following each event differed. Higher median conversation volumes noted for the COVID-19 pandemic (+1 to +5 conversations), Hurricane Laura (+1 to +7 conversations) and the 2020 Election (+1 to +26 conversations). ARIMA models show substantial increases in help-seeking behavior following the declaration of a national emergency for the COVID-19 pandemic (+4.3 to +38.2%) and following the 2020 election (+3 to +24.44%). Our analysis found that the mental health response following social stressors may be distinct from natural events, especially when natural disasters occur in the context of multiple social stressors. This analysis adds to the growing body of literature considering the mental health impact of exposure to multiple co-occurring societal stressors, like police violence and a global pandemic.

Systematic literature review of postponed elections during COVID-19: Campaigns, regulations, and budgets.

The postponement of elections during the COVID-19 pandemic reflects a decline in global democracy. The policy of postponing elections due to health concerns, regulatory changes, and economic crises lacks empirical evidence. Several studies suggest that holding elections in countries that proceed with them does not significantly impact the spread of COVID-19. The acceleration of COVID-19 transmission is not limited to countries holding elections; it affects all countries, including those postponing them. Moreover, countries that continue to hold elections have turned this challenge into a COVID-19 mitigation strategy. This study aimed to analyze the findings of original research on election execution during the COVID-19 pandemic, focusing on campaign activities, regulatory changes, and election budgeting. It employed systematic literature review using 12 articles from the Web of Science and Scopus databases. The results show that conducting elections during the COVID-19 pandemic does not contribute to the spread of the virus. This is evident through the adoption of blended campaign strategies by the candidates. Election committees have adjusted voting procedures, including increasing the number of polling stations, tightly scheduling voter arrivals, and visiting red zone lockdown areas to prevent mass mobilization. The increase in election budgets during an economic crisis is not solely for the elections themselves but also for reinforcing health protocols. This research recommends a reconsideration of election postponements and advocates for conducting elections during the COVID-19 pandemic to uphold democracy. Furthermore, future research should explore the social and political implications, as well as the long-term consequences for democracy, which result from the implementation of soft campaign strategies and voting system modifications on a global scale, all in the pursuit of preserving democracy.

The Wrath of Candidates. Drivers of Fear and Enthusiasm Appeals in Election Campaigns across the Globe.

Little is known about why candidates decide to make use of emotional messages when campaigning for a political office, and under which conditions this is more likely to happen. We focus on the use of fear and enthusiasm appeals and assume that these are a function of profile of candidates and the nature of the context in which the election takes place. We use a new large-scale comparative dataset which includes information about campaigning strategies for 636 candidates having competed in 133 presidential and parliamentary elections in 101 countries between June 2016 and March 2020, based on judgments of 2000+ domestic and international experts. Our results show that candidates benefitting from a comparative advantage (incumbents and frontrunners) tend to rely on enthusiasm appeals, more extreme candidates prefer fear to enthusiasm, and more competitive races tend to foster the use of fear appeals. These findings have important implications for electoral competition, communication theory, and political marketing. All data and materials are openly available for replication. Supplemental data for this article is available online at https://doi.org/10.1080/15377857.2021.1930327 .

The role of the 30% threshold for Islamic parties: A fast-growing middle class and religion-based political preferences in Indonesia.

Recently, Indonesia's middle-class Muslims have come under suspicion for strengthening the religious foundation of their political preferences. However, while this suspicion has been triggered by their increasingly confident expressions of Islamic identity in political, social and economic spheres, these public expressions do not consistently translate into electoral outcomes. Despite the significant growth of the middle class, the proportion of votes cast for Islamic parties, encompassing both pro-shari'a or Islamist and Islam-inclusive parties, has declined from 37% in 1999 to 30% in 2019. Utilising panel data from 397 districts and cities spanning three recent general elections (2009, 2014 and 2019), this study examines the association between the middle class's proportion of the total population and the vote share of Islamic parties. Our fixed-effect estimations validate an inverted U-curve relationship between the middle class's share and the vote share of pro-shari'a or Islamist parties, with the turning point occurring at approximately 29% of the middle class's total population. In regions with a lower percentage of the middle class, the vote share of Islamist parties rises in parallel with middle-class growth. Conversely, at a higher level, a growing middle class curtails the vote share of Islamist parties. This suggests that Indonesia's contemporary social structure is undergoing desecularisation in various ways, contingent on levels of modernisation. Notably, this desecularisation process does not imply a movement towards establishing an Islamic state. Indonesia's Muslims are moderate and more inclined to support secular, national or Pancasila-based parties, which tend to be inclusive and secular in their approach.

Correlation between covid19 cases and the electoral choice of municipalities in the presidential election in the second round in 2022 / Correlación entre los casos de covid19 y la elección electoral de los municipios en la elección presidencial en la segunda vuelta de 2022 / Correlação entre casos de covid19 e a opção eleitoral dos municípios na eleição presidencial no segundo turno em 2022

O objetivo desta pesquisa foi verificar se os municípios que votaram majoritariamente em Bolsonaro tiveram mais casos de COVID19 do que aqueles que votaram majoritariamente em Lula. Foram coletados dados oficiais de cada município brasileiro sobre o percentual de votos para Lula (%Lula) e Bolsonaro (%Bolsonaro) no segundo turno das eleições de 2022. O número total de casos de COVID19 (N.casos) e óbitos (Óbitos) até o final de 2022 e os dados da população total (Pop) nos sites do IBGE (Instituto Brasileiro de Geografia e Estatística), TSE (Tribunal Superior Eleitoral) e MS (Ministério da Saúde). Verificou-se a existência de correlação negativa e moderada (-53%) entre (%Lula) x (N.casos/Pop), positiva e fraca (5%) entre (%Lula) x (Óbitos/N.casos) e negativa e moderada (-50%) entre (%Lula) x (Óbitos/Pop), para (%Bolsonaro) os valores foram os mesmos com os sinais opostos. Duas populações diferentes foram comparadas, Lula = municípios em que (%Lula) > 0,55 dos votos e Bolsonaro = municípios em que (%Bolsonaro) > 0,55 dos votos. Foram 3 índices (N.casos/Pop), (Óbitos/N.casos) e (Óbitos/Pop). Ao nível de 99%, descartou-se a hipótese de igualdade entre as médias. Para (%Lula) e (%Bolsonaro) respectivamente: Para (N.casos/Pop) = (0,98; 1,74); (Óbitos/Pop) = (0,14; 0,24); (Óbitos/N.casos) = (0,017; 0,016).

O objetivo desta pesquisa foi verificar se os municípios que votaram majoritariamente em Bolsonaro tiveram mais casos de COVID19 do que os que votaram majoritariamente em Lula. Foram coletados os dados oficiais para cada município brasileiro sobre o percentual de votos em Lula (%Lula) e de Bolsonaro (%Bolsonaro) na eleição de segundo turno de 2022. O número de casos totais de COVID19 (N.casos) e de mortes (Mortes) até o final do ano de 2022 e os dados do total da população (Pop) nos sites do IBGE (Instituto Brasileiro de Geografia e Estatística), TSE (Tribunal Superior Eleitoral) e MS (Ministério da Saúde). Foi verificada a existência de correlação negativa e moderada (-53%) entre (%Lula) x (N.casos/Pop), positiva e fraca (5%) entre (%Lula) x (Mortes/N.casos) e negativa e moderada (-50%) entre (%Lula) x (Mortes/Pop), para (%Bolsonaro) os valores foram os mesmos com os sinais contrários. Duas populações diferentes foram comparadas, Lula = municípios em que o (%Lula) > 0,55 dos votos e Bolsonaro = municípios em que o (%Bolsonaro) > 0,55 dos votos. Foram verificados 3 índices, (N.casos/Pop), (Mortes/N.casos) e (Mortes/Pop). Ao nível de 99% foi descartada a hipótese de igualdade entre as médias. Para (%Lula) e (%Bolsonaro) respectivamente: Para (N.casos/Pop) = (0,98; 1,74); (mortes/pop) = (0,14; 0,24); (Mortes/N.casos) = (0,017; 0,016).

Hybrid optimization assisted channel selection of EEG for deep learning model-based classification of motor imagery task.

OBJECTIVES: To design and develop an approach named HC + SMA-SSA scheme for classifying motor imagery task. METHODS: The offered model employs a new method for classifying motor imagery task. Initially, down sampling is deployed to pre-process the incoming signal. Subsequently, "Modified Stockwell Transform (ST) and common spatial pattern (CSP) based features are extracted". Then, optimal channel selection is made by a novel hybrid optimization model named as Spider Monkey Assisted SSA (SMA-SSA). Here, "Long Short Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BI-GRU)" models are used for final classification, whose outcomes are averaged at the end. At last, the improvement of SMA-SSA based model is proven over different metrics. RESULTS: A superior sensitivity of 0.939 is noted for HC + SMA-SSA that was higher over HC with no optimization and proposed with traditional ST. CONCLUSIONS: The proposed method achieved effective classification performance in terms of performance measures.

"You'd be Right to Indulge Some Skepticism": Trust-building Strategies in Future-oriented News Discourse.

This paper explores trust-building strategies in future-oriented news discourse, marked by a high degree of uncertainty. While current research mainly focuses on audiences' perceptions of news credibility, this study addresses news trust from a production standpoint. We examine the trust-building efforts of media actors, focusing on their discursive labor within the context of election projections. Drawing on rich data from five election rounds in Israel and the US, we qualitatively analyzed 400 news texts and 400 tweets that were produced by 20 US and 20 Israeli media actors. This textual analysis was supplemented by 10 in-depth interviews with Israeli journalists. Our findings demonstrate three types of journalistic trust-building rhetoric in election coverage: facticity, authority, and transparency. These strategies result in a two-fold form of trust, which re-affirms traditional notions of accuracy and validity, while also challenging the ability of newspersons to obtain them in contemporary political and media cultures. Overall, these strategies hold unique opportunities and challenges for sustaining public trust in journalism and illuminate the complex communicative labor involved in building trust with news audiences. Our findings also highlight the importance of studying trust not only in relation to the past and the present, but also in future-oriented discourse.

On the frontiers of Twitter data and sentiment analysis in election prediction: a review.

Election prediction using sentiment analysis is a rapidly growing field that utilizes natural language processing and machine learning techniques to predict the outcome of political elections by analyzing the sentiment of online conversations and news articles. Sentiment analysis, or opinion mining, involves using text analysis to identify and extract subjective information from text data sources. In the context of election prediction, sentiment analysis can be used to gauge public opinion and predict the likely winner of an election. Significant progress has been made in election prediction in the last two decades. Yet, it becomes easier to have its comprehensive view if it has been appropriately classified approach-wise, citation-wise, and technology-wise. The main objective of this article is to examine and consolidate the progress made in research about election prediction using Twitter data. The aim is to provide a comprehensive overview of the current state-of-the-art practices in this field while identifying potential avenues for further research and exploration.

House Republicans were rewarded for supporting Donald Trump's 'stop the steal' efforts.

In early 2021, members of Congress cast a series of high-profile roll call votes forcing them to choose between condoning or opposing Donald Trump's efforts to overturn the 2020 presidential election. Substantial majorities of House Republicans supported Trump, first by opposing the certification of electoral votes from Arizona and Pennsylvania on January 6th, then by opposing the president's impeachment for inciting the attack on the US Capitol, and then by opposing a bill that would have created a national commission to investigate the events of January 6th. We examine whether the House Republicans who voted to support Trump in 2021 were rewarded or punished in the 2022 congressional midterm elections. We find no evidence that members who supported Trump did better or worse in contested general election races. However, Trump supporters were less likely to lose primary elections, more likely to run unopposed in the general election, more likely to run for higher office, and less likely to retire from politics. Overall, there seem to have been no significant political costs and some significant rewards in 2022 for House Republicans who supported Trump's undemocratic behavior.

The politics of abortion rights in the 2022 United States midterm election: Lessons for fledgling democracies around the world.

The United States government, under President Donald Trump, retreated from its traditional role as an exemplar of democracy, defender of press freedom and the rule of law but embraced conspiracy theories, virulent anti-Semites, and authoritarian regimes worldwide. Today, democracy is in crisis and is under assault and in retreat globally. The 2022 United States midterm election has come and is now history with many unexpected outcomes. The three impactful issues during the campaign that produced many upsets were abortion rights, election denialism, and threats to democracy. This editorial examines the history of abortion rights in the United States, the impacts of the Dobbs vs. Jackson ruling on the 2022 midterm election, the threats of election deniers to global democracy, the global status of reproductive health rights, and the lessons of abortion ban for burgeoning democracies worldwide.

Nigeria's 2023 presidential election and the future of healthcare in the country.

On February 25, 2023, Nigerians took a step forward as a democratic state by voting for a new president. The election is history as the ruling party's candidate, Bola Tinubu, was declared the winner. He polled 37% of the vote, his main rival Abubakar Atiku garnered 29%, and Labour's Peter Obi 25%. Only 27% of registered voters came out to vote. International election observers noted that the election lacked transparency and was marred by logistical challenges and multiple incidents of political violence. The currency and fuel shortages in the country burdened many voters and election officials and therefore marginalised many groups, especially women, who continue to face barriers to political office. The outcome of the election is in contention and inconclusive. The aggrieved parties have taken their case to court, so the nation awaits the outcome of the court decision. In this Editorial, AJRH analyses the prospects and implications of Tinubu's presidency for healthcare in Nigeria.

Baseline comparative analysis and review of election forensics: Application to Ghana's 2012 and 2020 presidential elections.

Many allegations have been levelled against the electoral process of many countries across the world by most opposition leaders, especially when they lose a presidential election e.g. Ghana in 2012 and 2020. Therefore, the need to apply election forensic techniques to the certified election results data of valid votes count to statistically verify if some suspected or possible anomalies and irregularities exist in the voting pattern. This paper seeks to provide a comprehensive review of election forensics techniques and make a comparative analysis of Benford's Second-order test of conformity (using the first two digits) and Hartigans' dip test of unimodality to examine the existence of possible anomalies and irregularities in the 2012 and 2020 presidential elections held in Ghana. The findings of the two tests suggest that the electoral process produced possible anomalous data in the 2012 presidential election results (with an overall 16.67% suspected anomalies), whilst possible non-anomalous data was produced in the 2020 presidential election results (with an overall 0% suspected anomaly) of valid votes count. Therefore, the study recommends that for better statistical data analysis on election anomaly detection, Benford's test of conformity and Hartigans' dip test of unimodality should serve as baseline tests (initial screening tools), highlighting areas that may require further investigation or more rigorous analysis and progressively dig deeper into the application of finite mixture fraud models and machine learning techniques. In spite of the promising results Benford's Law, dip test, machine learning algorithms, and network analysis have produced in detecting irregularities in election data, real-world applications remain challenging, particularly when dealing with complex and evolving forms of fraud. Therefore, there is the need for continuous research and innovation to improve the accuracy and effectiveness of these methods and promote transparency and accountability in democratic societies.

Analysis and mining of an election-based network using large-scale twitter data: a retrospective study.

The user-generated Twitter data are a rich source of study and research that reflects the various social, economic, political, and other issues affecting people across the world. Analysis of the social interactions among users, who express themselves online, reveals different internal dynamics and provides detailed insights into real-world phenomena. In this paper, the structure and dynamics of the state assembly election-based tweet-reply network have been studied, as generated by Twitter users across the country of India for a period of 6-weeks. We study the flow of Twitter activity pertaining to the West Bengal assembly elections, along with the identification of the hashtags used by the three main political contenders. This information is used to identify the cluster-level dominance in the Twitter network over the 6-weeks of study. It is observed that this cluster dominance information is representative of the actual outcome of the elections, and can be effectively used as a forecasting tool. The collected tweets are used for lexicon-based emotion detection and further analysis. This highlights the reaction of the social media users in response to the events related to the election. It is observed that fear is the dominant emotion, while happiness is scarce in the opinions expressed during the studied duration. Next, the study and analysis of the complete reply-based social networks during weeks 1, 4, and 6 are undertaken. Important political and media actors are identified with standard network-level measures toward determining the efforts put in by the different clusters and individual actors involved in the election to control the network dominance.