Static Palace

De-globalization, COVID-19, a surprise winner, the financial crisis – MNEs have been confronted with great uncertainties during the US presidential elections from 2012 to 2020. While there is abundant research on political events affecting MNEs, the role of MNEs´ international and national diversification when political events occur in increasingly polarized, nationalist Western countries has not been studied yet. Through an event study analysis, we investigate the abnormal returns of 2,941 US companies during US presidential elections. Our research contributes to International Business literature as we explore whether international and national diversification are safeguards of MNEs´ performance when electoral and policy uncertainty occur.

The Israeli perspectives on the US presidential elections are characterized by the dichotomy of perception and reality. That is to say, most of the Israeli perspective is based on perception rather than reality. Perception of the candidates and their positions on Iran, the Israeli-Palestinian peace process, Israeli settlements, arms control, and the relationship each of the candidates would have with Israel, all these affected Israeli preferences. The dichotomy between perception and reality has also infiltrated the 2009 Israeli election campaigns of the parties and their leading candidates insofar as they presented themselves as the candidates of “change” or as those most likely to work well with the incoming Obama administration, despite the political realities that may indicate otherwise. This has been referred to as the “Obama effect.”1

Despite the increasing globalization of housing markets, little is known about its political implications. This study investigates whether rising Chinese investments in US homes influenced local voting in recent US presidential elections. Building on pocketbook/sociotropic voting and nativism theories, I develop hypotheses on the electoral consequences of foreign real estate investment through greater home demand and equity, improved local economies, and changing neighborhoods. Using difference-in-differences designs that combine a unique shock to Chinese capital outflows in 2013 with county-level measures of local attractiveness to Chinese investments, I find that greater exposure reduced Democratic vote shares in both the 2016 and 2020 presidential elections. Furthermore, an initially larger white population strengthened this effect, while a larger college-educated population weakened it. In contrast, local equity gains, housing competition, or economic strength did not systematically influence the effect. Together, the results appear more consistent with the pro-conservative effects of nativism.

Influence among individuals is at the core of collective social phenomena such as the dissemination of ideas, beliefs or behaviors, social learning and the diffusion of innovations. Different mechanisms have been proposed to implement inter-agent influence in social models from the voter model, to majority rules, to the Granoveter model. Here we advance in this direction by confronting the recently introduced Social Influence and Recurrent Mobility (SIRM) model, that reproduces generic features of vote-shares at different geographical levels, with data in the US presidential elections. Our approach incorporates spatial and population diversity as inputs for the opinion dynamics while individuals' mobility provides a proxy for social context, and peer imitation accounts for social influence. The model captures the observed stationary background fluctuations in the vote-shares across counties. We study the so-called political strongholds, i.e., locations where the votes-shares for a party are systematically higher than average. A quantitative definition of a stronghold by means of persistence in time of fluctuations in the voting spatial distribution is introduced, and results from the US Presidential Elections during the period 1980-2012 are analyzed within this framework. We compare electoral results with simulations obtained with the SIRM model finding a good agreement both in terms of the number and the location of strongholds. The strongholds duration is also systematically characterized in the SIRM model. The results compare well with the electoral results data revealing an exponential decay in the persistence of the strongholds with time.

Due to new technologies, the speed and volume of disinformation is unprecedented today. As seen in the 2016 US presidential election, especially with the conduct of the Internet Research Agency, this poses challenges and threats for the (democratic) political processes of internal State affairs, and in particular, (democratic) elections are under increasing risk. Disinformation has the potential to sway the outcome of an election and therefore discredits the idea of free and fair elections. Given the growing prevalence of disinformation operations aimed at (democratic) elections, the question arises as to how international law applies to such operations and how States under international law might counter such hostile operations launched by their adversaries. From a legal standpoint, it appears that such disinformation operations do not fully escape existing international law. However, due to open questions and the geopolitical context, many States refrain from clearly labelling them as internationally wrongful acts under international law. Stretching current international legal norms to cover the issue does not seem to be the optimal solution and a binding international treaty would also need to overcome various hurdles. The author suggests that disinformation operations aimed at (democratic) elections in the context of public international law will most likely be regulated (if) by a combination of custom and bottom-up law-making influencing and reinforcing each other.

The 2024 US presidential election has concluded. The Democratic camp was unable to unite sufficiently under Kamala Harris, leading to a significant decline in support from major support groups. In contrast, the Trump camp succeeded in relieving the dissatisfaction of potential Democratic supporters who felt that the Democratic Party did not fulfill their demands. In this election, voting behavior differed significantly from traditional patterns, prompting some to argue that the Republican Party had become the party of the working class, while the Democratic Party had become the party of the wealthy. However, it is premature to assume that a party realignment occurred during this election. In the future, it is possible that the two major parties will compete for workers’ votes in every election, and the winner will vary. Allies, including Japan, also need to build a relationship of trust with the United States from a long-term strategic perspective.

It is widely believed that high inflation reduces the popularity of incumbents, and contributed to poor incumbent performance in recent elections in the United States and elsewhere. Existing research shows that voters’ inflation perceptions are associated with their evaluations of incumbent parties, but these observational studies cannot eliminate the possibility that the causal relationship runs the other way, where opposition to incumbent governments causes individuals to report higher price increases. To help overcome this inferential challenge, this study draws on a pre-registered experiment embedded in a nationally representative survey fielded just days before the 2024 US Presidential election. We find that priming Americans to think about inflation reduced support for the incumbent party. This effect is most pronounced among Independents and Democrats. These findings suggest that inflation likely contributed to the Democrats’ 2024 electoral defeat, and provide novel evidence that inflation has a causal effect on support for incumbent parties.

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.

The 2016 US presidential election was highly contentious, as both candidates, Hillary Clinton and Donald Trump, received strong polarizing support and opposition with controversial campaigns, name-calling, and violence at campaign rallies. This may have contributed to an opinion climate where citizens were reluctant to express support for a candidate. This study tests the spiral of silence theory in the context of this election. We examine the interplay among opinion congruency, fear of isolation, and willingness to express support for a candidate. Data from an online survey show that opinion congruency for Clinton in society at large, and for Trump on Facebook, had indirect associations with willingness to express support for a candidate face-to-face, on Facebook, and in anonymous online settings through fear of isolation.

Twitter is a popular micro-blogging social media platform. For the 2016 US Presidential election, many people expressed their likes or dislikes for a particular presidential candidate. Our work's aim was to calculate the sentiment expressed by these tweets, and then compare this sentiment with polling data to see how much correlation they share. We used a lexicon and Naive Bayes Machine Learning Algorithm to calculate the sentiment of political tweets collected one-hundred days before the election. We used manually labeled tweets as well as automatically labeled tweets based on hashtag content/topic. Our results suggest that Twitter is becoming a more reliable platform in comparison to previous work. By focusing on tweets 43 days before the election (beginning with the first presidential debate), we found a correlation as high as 94% to polling data using a moving average smoothing technique.

This paper examines the phenomenon of humorous Internet memes within the context of news and political culture, sometimes referred to as LOLitics. LOLitics are a category of digital texts created by ordinary individuals that, like most political humour, are usually responses to news events or gaffes committed by political figures. The analysis situates LOLitics as popular culture products that exist within the intersection between pleasure-driven ‘play’ and (arguably) genuine political discourse. LOLitics are prominent due to their spreadability and replicability, and the amount of texts being produced has visibly risen over the last election cycle. Internet memes have become one of the default ways to respond to particular situations online, and this certainly reflects the reaction towards news and political culture. The 2012 US Presidential election is applied as a case study in examining the significance of these Internet memes to everyday citizen discourse, and the relationships between ordinary citizens, the mainstream media, and politics. The results argue that humorous viral texts, both visual and verbal, reveal the potential power that ordinary people have in setting the agenda for newsmakers, and to communicate political criticism through popular culture.

This study examines code-switching behaviours of cross-platform social media users specifically between Twitter and Parler during the 2020 US Presidential Election. Utilising social identity theory as a framework, we examine messages related to voter fraud by users who migrated from Twitter to Parler following Twitter bans. Our analysis covers 38,798 accounts active on both platforms, analysing 1.5 million tweets and more than 100,000 parleys. The key findings of the study are as follows: First, we discovered differing levels of network homophily between high degree centrality and low-degree centrality cross-platform users, illustrating how individuals with varying degrees of influence engage differently across platforms. Second, we observed higher toxicity levels in heterogeneous networks, which include both in-group and out-group members, compared with homogeneous networks that are primarily composed of in-group members. This suggests the level of toxicity in online spaces correlates with the level of group diversity. Third, we found that cross-platform users created distinctive discourse community with in-group and out-group members, indicating that content and discussions within these networks are influenced by the social identity dynamics of the users. Our study contributes to the current research in political communication and information science by proposing comparative user analyses across multiple social media platforms. Focusing on a critical period of platform transition during a contentious political event, our study offers insights into the dynamics of online communities and the shifting nature of political language used by social media users.

US voters have been moving further and further apart, most notably in terms of partisanship. This trend has led to a strong geographic concentration of voters’ preferences. We look at how turnout shows a similar pattern by jointly addressing two features of the data: spatial autocorrelation and heterogeneity of the observed units. Results obtained through a spatial lag regression tree procedure for the 2012 US presidential elections allow us to identify twelve groups of counties with similar characteristics. We find that (i) close counties behave similarly in terms of turnout; (ii) across various groups of counties, some variables have different statistical significance (or lack of it, such as household income and unemployment), and often different signs (such as the shares of adherents to congregations, Blacks, and Hispanics, and urban population). These results are useful for targeting geographically based groups in get out the vote operations.

Despite a relationship between gender and support for populist causes in cross-national research, including in the 2016 US Presidential election, the role of gender has been missing in analysis of support for Brexit, most likely because women and men showed no average aggregate-level differences in voting Leave or Remain. This misses an important explanation for Britain’s vote to leave the European Union. We demonstrate how gender-based resentment motivated Leave votes in the EU referendum, through perceptions of discrimination against men, among men. Using novel survey measures, we demonstrate (i) the distinct nature of perceptions of discrimination towards men in comparison with discrimination towards women; (ii) the sociological sources of perceptions that men are discriminated against; and (iii) the role of these perceptions in Brexit support. Findings reveal that the Brexit referendum provided an opportunity to express broader social grievances than have, to date, been identified as relevant. The paper therefore offers a novel contribution to understanding the cultural backlash behind Britain’s vote to leave the EU, and by so doing, insight into the potential for gender-based backlash effects in elections where gender isn’t significantly primed, unlike the 2016 US presidential election where gender was a major political focus.

Before and after the 2016 US Presidential Election, this research examined Trump and Clinton supporters’ attributions about behavior of each leader, both of whose ethicality had been publicly questioned. American voters (N = 268) attributed significantly more dispositional factors to the outgroup leader than to the ingroup leader. Moreover, when the ingroup candidate won the election (i.e., among Trump supporters), unethical leadership subsequently became more acceptable and there was less desire to tighten the election process when dealing with unethical candidates. The opposite pattern was found among voters whose ingroup candidate lost the election (Clinton supporters). The results and implications are discussed.