US Presidential Election Research Articles

Consolidation or Fracturing: Network Change and Real-World Events

How do organizational networks respond to changing environments? Specifically, do networks fracture, with organizational actors and units splitting into balkanized clusters, or do they consolidate, becoming more connected? This study seeks to address this question by examining network change in the online community Reddit in response to two major events, the 2008 US Presidential Election and the 2009 Swine Flu Pandemic. To do so, this study focuses not just on change in structural networks, but also change in cultural networks. In doing so, the aim of this study is to paint a more complete picture of network change. Findings are largely consistent across both events, with structural and cultural networks consolidating around the time of the events. While structural and cultural trends resemble one another, differences emerge, painting a fuller picture of network change. Notably, some elements of cultural change appear to be longer-lasting compared to corresponding elements of structural change.

Craftivism in the time of Covid: resisting toxic masculinity through feminized labor

ABSTRACT This study focuses on the social media discourse of craftivism between March 2020 and December 2020, coinciding the Covid-19 lockdowns across the United States and the US Presidential Election. The timeframe under examination also includes some of the most extensively covered Black Lives Matter protests to date in the United States, thus requiring an intersectional lens and attention to whose crafts are circulated and lauded. These three deeply intertwined events raised tensions on social media platforms to new extremes, resulting in shifting moderation policies, and reflecting the increased significance of online communities to sustaining all relationships and movements. During this time, we argue that a fundamental shift in the discourse of craftivism has occurred, rejecting previous assumptions of ”gentleness” while demanding a public recentering of the domestic and ”femininized” labor. We contextualize this shift through three case studies: global memorial project Covid-19 quilt; political art project Tiny Pricks; and activist mask-making collective Auntie Sewing Squad. At the same time, this revision must be understood alongside collective failures of labor valuation and widespread economic crisis disproportionately impacting women (particularly women of color). In these cases, we see important intersectional resistance, even as white femininity in craft continues to be centered.

Public opinion monitoring through collective semantic analysis of tweets.

The high popularity of Twitter renders it an excellent tool for political research, while opinion mining through semantic analysis of individual tweets has proven valuable. However, exploiting relevant scientific advances for collective analysis of Twitter messages in order to quantify general public opinion has not been explored. This paper presents such a novel, automated public opinion monitoring mechanism, consisting of a semantic descriptor that relies on Natural Language Processing algorithms. A four-dimensional descriptor is first extracted for each tweet independently, quantifying text polarity, offensiveness, bias and figurativeness. Subsequently, it is summarized across multiple tweets, according to a desired aggregation strategy and aggregation target. This can then be exploited in various ways, such as training machine learning models for forecasting day-by-day public opinion predictions. The proposed mechanism is applied to the 2016/2020 US Presidential Elections tweet datasets and the resulting succinct public opinion descriptions are explored as a case study.

Digital Hegemony and the Russia-Ukraine War

In this article, the impact of US-based, global hi-tech companies like Google or Starlink, on the Russia and Ukraine War. Also, grounding from this sample, further projections about the traditional debate on MNC’s ( Multinational Companies) v.s. state, will be made. Since the US Presidential Elections held in 2008 and Occupy Wall Street Movements, social media and internet started to be used for political means. With the start of the Arab Spring events, the sphere of influence of such politically oriented movements on the internet, became international. Then, with the Second Karabakh War ( The 44 Days War) , social media, internet and misinformation started to be used directly for foreign policy interests. Moreover, it can be asserted that, with the cultural and technologic sanctions imposed on Russian people while popular hi-tech and communication sector willingfully make their services easier to use for Ukrainian authorities, the process of using social media and internet for specific foreign policy goals, entered into the maturity period. Considering the increasing importance of hi-tech goods in our daily lives, sampling from the habits that we got during the Covid-19 pandemic, the effect of the companies that provide these products and services, will raise. Adding lack of home-grown alternatives for these products and know-how transfer, this one-sided dependency could bring weaknesses for several nation-states.

(Re)Conceptualizing Black motherwork as political activism

AbstractIn 2020, Black Americans voted in historic numbers in the US presidential election. Black women political activists were instrumental in navigating voter suppression to increase voter turnout among Black communities. Here, I draw linkages between Patricia Hill Collins's conceptualization of motherwork and Black women's political activism. I reconceptualize Black women's motherwork as a political tool for preparing Black women for political candidacy to address issues that threaten Black family stability. I provide a conceptual history of motherwork and map out what Black women's political motherwork looks like for families and communities. I conclude with theoretical, analytical, and practical implications for family scientists to consider when using Black political motherwork in their scholarship and practice.

Analyzing voter behavior on social media during the 2020 US presidential election campaign

Every day millions of people use social media platforms by generating a very large amount of opinion-rich data, which can be exploited to extract valuable information about human dynamics and behaviors. In this context, the present manuscript provides a precise view of the 2020 US presidential election by jointly applying topic discovery, opinion mining, and emotion analysis techniques on social media data. In particular, we exploited a clustering-based technique for extracting the main discussion topics and monitoring their weekly impact on social media conversation. Afterward, we leveraged a neural-based opinion mining technique for determining the political orientation of social media users by analyzing the posts they published. In this way, we were able to determine in the weeks preceding the Election Day which candidate or party public opinion is most in favor of. We also investigated the temporal dynamics of the online discussions, by studying how users’ publishing behavior is related to their political alignment. Finally, we combined sentiment analysis and text mining techniques to discover the relationship between the user polarity and sentiment expressed referring to the different candidates, thus modeling political support of social media users from an emotional viewpoint.

Pre-adult political experiences and political participation

ABSTRACT Young adults are less likely to vote than older Americans, but they do not enter adulthood as blank slates. The political experiences before young adults are eligible to vote can shape their future likelihood of participation. In this paper, we explore how one particular event, the result of the 2016 presidential election, changed the likelihood of participation among young adults. We designed an original experiment that randomly assigned adults eligible to vote in a presidential election for the first time to be primed to remember their emotional reactions to the 2016 election. We found that participants who were asked to think about their emotional reaction to the 2016 United States presidential election were significantly more likely to say that they would vote in an election if one were held today. These findings suggest that, at least in the case of the 2016 election, reminding young adults of past political experience is likely to increase their motivation to vote.

Using polls to forecast popular vote share for US presidential elections 2016 and 2020: An optimal forecast combination based on ensemble empirical model

This study introduces the Ensemble Empirical Mode Decomposition (EEMD) technique to forecasting popular vote shares in general elections. The technique is useful when using polling data. Our main interest in this study is shorter- and longer-term forecasting and, thus, we consider from the shortest forecast horizon of 1-day to three months ahead. The EEMD technique is used to decompose the election data for the two most recent US presidential elections; 2016 and 2020. Three models, Support Vector Machine (SVM), Neural Network (NN) and ARIMA models are then used to predict the decomposition components. Subsequently, the final hybrid model is constructed by comparing the prediction performance of the decomposition components. The predicting performance of the combination model is compared with the benchmark individual models: SVM, NN, and ARIMA. Finally, this is also compared to the single prediction market IOWA Electronic Markets. The results indicate that the prediction performance of combined EEMD model is better than that of the individual models.

Communities with an anchor institution have higher coronavirus vaccination rates.

Anchor institutions ("anchors") are large employers, rooted in a community by reason of mission, capital, or relationships. Many anchors have encouraged coronavirus vaccination for employees and their families. Our objective was to determinewhether the presence of an anchor was associated with a higher county-level vaccination rate. A cross-sectional study focused on 745 small- and mid-sized US counties. We used data from the Centers for Disease Control and Prevention, Reference USA's US Business Database, Economic Innovation Group's Distressed Communities Index database, 2021 County Health Ratings and Rankings, 2020 US Presidential Election popular vote data, and National Center for Health Statistics urban-rural classification data. We constructed 3 explanatory variables of interest: a binary variable indicating whether the county had an anchor; a continuous variable representing the number of anchors within a county; and the percent of all workers in the county who were employed by an anchor. Multivariable linear regression models were adjusted for race/ethnicity, political party allegiance, rurality, economic distress, and prevalence of smoking and adult obesity. Counties with an anchor had vaccination rates 2.31 (P<.01) percentage points higher than those without an anchor. The number of anchors in a county was also significantly associated with higher vaccination rates. Efforts by anchors to encourage vaccination may have been successful, and that anchors may be well positioned to amplify public health messages. However, the influence and efforts of anchors to increase vaccination did not fully mitigate disparities in vaccination rates by race, ethnicity, and political party allegiance.

Populism in Vietnam today: status-quo and policy recommendation

Populism has been on the rise globally since the earliest years of the twenty-first century. Recently, the term populism has appeared with relatively high frequency on a number of news media, notably at a time during the double shock of the 2016 Brexit movement and the success in the US presidential election of Donald Trump. In Vietnam, populism does not have enough grounds to exist in a form of ism in the original sense, but mainly in specific manifestations. Despite the fact that the idea has not yet governed the Vietnamese peoples lives, its influence is still considerable. This is one of the complicated problems that need to be carefully studied. This article focuses on analyzing the status-quo of populism in Vietnam, thus offering some policy recommendations.

Responding to “Fake News”: Journalistic Perceptions of and Reactions to a Delegitimising Force

The “fake news” phenomenon has permeated academic scholarship and popular debate since the 2016 US presidential election. Much has been written on the circulation of “fake news” and other forms of mis- and disinformation online. Despite its ongoing proliferation, less effort has been made to better understand the work of those engaged in daily news production—journalists themselves. Funded by the Australian Research Council project Journalism Beyond the Crisis, this study investigates how journalists perceive and respond to this phenomenon at a time when the industry has come under significant attack, and trust in news media has fallen globally. To do so, it draws on in-depth interviews with journalists in Australia and the UK, providing topical insights on their perceptions of and reactions to this profoundly delegitimising force. While on one hand, our findings show journalists expressing significant concern about the rise of “fake news,” they also proactively seek—and, in some cases, implement—deliberate counterstrategies to defend their profession. These strategies range from discursive means—such as stressing and re-asserting journalists’ professional authority and legitimacy—to tangible measures at an organisational level, including newsroom diversity and increased transparency in the news production process.

A REAL CHALLENGE FOR LIBERAL DEMOCRACIES: “FAKE NEWS” OR DOMESTIC REGULATIONS TO COUNTER FAKE NEWS?

This article aims to identify the greater threat to liberal democracies:&#x0D; “fake news” or domestic regulations intended to combat “fake news”.&#x0D; First, it assesses the impact of fake news on elections by analysing the&#x0D; 2016 US Presidential election in which the world faced the modern&#x0D; version of fake news for the first time and the 2019 EU Parliament&#x0D; election in which a non-regulatory initiative was launched to challenge&#x0D; fake news. Then, it evaluates the impact of regulations on free speech&#x0D; by reviewing liberal democracies’ pioneering regulatory frameworks&#x0D; intended to combat fake news: French Law no. 2018-1202, Germany’s&#x0D; Netzwerkdurchsetzungsgesetz, and UK’s Online Harms White Paper.&#x0D; It argues that, while damage to the functioning of democracy caused&#x0D; by fake news during election periods has not been as great as was&#x0D; feared, since fake news has several, highly politicised meanings, legal&#x0D; frameworks tend to over-regulate, which may violate the freedom of&#x0D; expression according to the case-law of the European Court of Human&#x0D; Rights (ECtHR). The article concludes that enhancing media literacy and&#x0D; non-regulatory efforts globally would contribute much more to prevent&#x0D; the impacts of fake news and to protect freedom of expression than&#x0D; legislative frameworks could, and that adopting regulatory frameworks&#x0D; to tackle the online dissemination of fake news should be reconsidered.

Explainability of Text Clustering Visualizations—Twitter Disinformation Case Study

While text clustering methods have been available for decades, there is a paucity of material that would help practitioners with the choice and configuration of suitable algorithms and visualizations. In this article, we present a case study analyzing two disinformation datasets composed of tweets from the era of the 2016 United States Presidential Election. We use this to demonstrate steps for selecting the best configuration of the clustering algorithm and consequently conduct a user experiment for evaluating the comprehensibility of three alternate visualizations. A supplementary GitHub repository contains source code with examples.

Sekuritisasi Isu Imigran di Amerika Serikat di Masa Pemerintahan Donald Trump

The issue of securitization regarding immigrants in the US is an issue that has become a public discussion since Donald Trump campaign as a US presidential candidate in the 2016 election. He has publicly conveyed xenophobic anti-immigrant rhetoric. Researchers assume that the existence of securitization makes the issue of immigrants a threat that endangers US nationalsecurity. This is done by conveying speech acts to influence public opinion, thus making the issue of immigrants a problem that requires emergency handling or extraordinary policies that have never been carried out by the previous government. This study aims to describe the stagesof the securitization process on the issue of immigrants in the US during the Donald Trump administration. This research was analyzed with the conceptual framework of the securitization stages of Barry Buzan, Waever, and Jaap de Wilde, which consisted of the stage of raising the issue and the stage of convincing the audience. This research method uses a qualitative approach with descriptive-analytical research and uses secondary data sources. This study found that the securitization process begins with the stages of raising issues through the role of the mass media, the influence of the Republican party promoted by Donald Trump, then continues with the stage of convincing the audience through the delivery of speeches by Donald Trump, Sebastian Gorka, Michael Flynn and Jeff Sessions. Then by issuing emergency and securitization measures legitimized by the audience as evidenced by the increasing US public voice supporting the expansion of the US-Mexico border wall.

COVID-19 in US Economy: Structural Analysis and Policy Proposals

Based on an input–output framework, this paper analyses the intersectoral structure of the US economy and estimates the COVID-19 multiplier effects on this economy. For this purpose we employ a model of matrix multipliers—which, except for the technical conditions of production, also considers imports, income distribution, savings, and consumption patterns—using data from the input–output table of the US economy for the year 2015, i.e., the latest available data at the time of this research (a few months after the US presidential election). Furthermore, we detect the key commodities that are considered appropriate for implementing economic policies in the short term, i.e., for boosting growth and job creation, as well as the commodities that are suitable for long-term, structural policies. Our findings suggest that short-term policies for a direct recovery after COVID-19 should be based on public consumption expenditures and investments as well as through exports. It is also shown that there is a great variety of short-term and long-term policies that can be adjusted according to the challenges of the US economy. Finally, for reasons of completeness, we estimate the impact of the main plans of the American president’s policy program, i.e., the “American Jobs Plan and the American Families Plan”, and we show that both plans would cumulatively increase the US output by about USD 6.07 trillion over the next ten years, not only helping the US economy recover from the COVID-19 shock but also ensuring macroeconomic stability and social cohesion.

DONALD TRUMP’S PERSUASION TECHNIQUES IN THE U.S. PRESIDENTIAL DEBATE 2016

Persuasion is an important aspect of communication in our daily lives. The main purpose of persuasion is to convince other people to change their belief, attitude and behaviour. Here, the researcher wanted to find out the techniques of persuasion used by Donald Trump in his political speeches in American presidential election 2016. This research was designed by using descriptive qualitative method. here, the researcher used discourse analysis because the researcher tried to study and understand the functional use of language, that was, persuasion which was used by people in their conversation to influence other's attitude, belief and behavior. The data were Donald Trump’s utterances containing persuasion which were observed from his speeches within American presidential election. The data had represented all data of Donald Trump's speeches. The data were analyzed inductively through the observation done by the researcher himself. In addition, the approach for analyzing the technique of persuasion was based on the Gorys Keraf’s theory of persuasion. It was found that Donald Trump used several techniques of persuasion to get support from the audience and invest his idea in delivering his speeches. These techniques are: rationalization, identification, suggestion, conformities, compensation, displacement and projection.

Anticipated Sexual Minority Stress and Mental Health after the 2016 Presidential Election: Examining a Psychological Mediation Framework

ABSTRACT Sexual minorities are at an increased risk for psychopathology, compared to heterosexual counterparts, in part due to stressors unique to their sexual minority identity. The greater socio-political climate may exacerbate sexual minority stress, and the context of the 2016 United States presidential election infringed upon the rights and well-being of LGBQ individuals. In our sample of sexual minorities (n = 253), we examined the association between anticipated stigma in response to the 2016 presidential election and symptoms of anxiety and depression, and the potential mediating role of self-compassion, hopelessness and social support. Greater anticipated stigma was associated with less self-compassion, less perceived social support, and greater hopelessness, and, in turn, greater symptoms of anxiety and depression. Anticipated stigma may erode feelings of environmental support and may be internalized as negative views of the self and future. From a public health perspective, policy-makers should be aware that the discussion and/or enactment of policies which discriminate against LGBQ persons may negatively impact mental health. Clinically, bolstering self-compassion and interpersonal functioning, and targeting hopelessness, through strategies such as Acceptance and Commitment Therapy and Compassion-Focused Therapy, may buffer the impact of minority stress among sexual minorities.

The Whos and the Whys Behind Donald Trump’s Victory in the 2016 U.S. Presidential Election

Given the unexpected victory of Donald Trump in the 2016 United States (U.S.) Presidential Election, this study aims to examine the 2016 electorate, unfolding the factors behind Trump’s victory, especially how different groups of people voted in the election. Hence, this paper addresses two questions, the “who” and “why” in the 2016 U.S. Presidential Election - who voted for The Apprentice’s talk show host, Donald Trump, to be the most influential individual globally, and why did they do so? A logistic regression model is deployed to model survey data from the 2016 American National Election Studies, a series of election studies conducted since 1948 on public opinion in the U.S. presidential elections. This empirical methodology determined the socio-economic and political factors underlying these voters’ preferences. The findings showed that racial identity, education level, and income level on a demographic basis were crucial in determining voting choice while controlling for the respondents’ party affiliation. On an issue basis, voters were primarily dissatisfied with Obama’s performance and were attracted to Trump’s conservative tones and his exuberant personality, which resembles leadership qualities to his supporters.

A large-scale sentiment analysis of tweets pertaining to the 2020 US presidential election

We capture the public sentiment towards candidates in the 2020 US Presidential Elections, by analyzing 7.6 million tweets sent out between October 31st and November 9th, 2020. We apply a novel approach to first identify tweets and user accounts in our database that were later deleted or suspended from Twitter. This approach allows us to observe the sentiment held for each presidential candidate across various groups of users and tweets: accessible tweets and accounts, deleted tweets and accounts, and suspended or inaccessible tweets and accounts. We compare the sentiment scores calculated for these groups and provide key insights into the differences. Most notably, we show that deleted tweets, posted after the Election Day, were more favorable to Joe Biden, and the ones posted leading to the Election Day, were more positive about Donald Trump. Also, the older a Twitter account was, the more positive tweets it would post about Joe Biden. The aim of this study is to highlight the importance of conducting sentiment analysis on all posts captured in real time, including those that are now inaccessible, in determining the true sentiments of the opinions around the time of an event.

Explainable machine learning pipeline for Twitter bot detection during the 2020 US Presidential Election

Explainable machine learning pipeline for Twitter bot detection during the 2020 US Presidential Election