Search Results (23)

Cluster randomized experiments and estimator comparisons are well-documented topics. In this paper, using the datasets of the popular vote in the presidential elections of the United States of America (2012, 2016, 2020), we evaluate the properties (SE, MSE) of three cluster sampling estimators: Ratio estimator, Horvitz–Thompson estimator and the linear regression estimator. While both the Ratio and Horvitz–Thompson estimators are widely used in cluster analysis, we propose a linear regression estimator defined for unequal cluster sizes, which, in many scenarios, performs better than the other two. The main objective of this paper is twofold. Firstly, to indicate which estimator is most suited for predicting the outcome of the popular vote in the United States of America. We do so by applying the single-stage cluster sampling technique to our data. In the first partition, we use the 50 states plus the District of Columbia as primary sampling units, whereas in the second one, we use 3112 counties instead. Secondly, based on the results of the aforementioned procedure, we estimate the number of clusters in a sample for a set standard error while also considering the diminishing returns from increasing the number of clusters in the sample. The linear regression estimator is best in the majority of the examined cases. This type of comparison can also be used for the estimation of any other country’s elections if prior voting results are available. Full article

The United States Federal Bureau of Investigation had been investigating the relationship between Russian agents and members of Trump’s presidential campaign since July 2016 out of suspicions that the President-elect worked with Russia to interfere in the 2016 U.S. presidential election, which became a major news event in American media. The headlines from news media outlets illustrate the strategic use of language to shape opinions and frames. Conducted with the tools of System Functional Linguistics, in particular, the appraisal and ideation resources, based on the framing theory of Journalism Studies, this research aims to answer the two research questions: (1) What frames did The New York Times and Fox News construct in their coverage of the Mueller investigation? (2) What linguistic strategies did The New York Times and Fox News use respectively to construct their frames? It was found that The New York Times uses fewer evaluative tools than Fox News, but the expression of attitudes draws on the context in which they are presented and evaluation is expressed in a more sophisticated and refined manner. Fox News is more straightforward without hiding its own opinion and biases. This research is important in further understanding of the American media and their linguistic strategies in forming manipulative frames. Full article

This article investigates the extent to which the COVID-19 pandemic fostered significant shifts in election campaigning. The argument is that COVID-19 might have had an impact on both digitalization and professionalization, which might have been regarded as necessary strategies to curb the difficulties brought about by the pandemic. We apply a most similar systems design with a threefold comparative scheme in order to capture and isolate such effects in the campaigns preceding the 2021 Portuguese presidential elections, using data from campaign spending, campaign activities, and social media activity and impact. Results show that the pandemic crisis has not, generally speaking, brought about a higher level of digitalization of electoral campaigns, in spite of online events having become more common. On the contrary, while there were signs of feebler patterns of normalization of online competition in 2021 vis-à-vis 2016, namely in terms of engagement, normalization was stronger after the lockdown than before. Lastly, relative investment in professionalization was similar in 2016 and 2021, and the difference between the budgeted and the actual investment in 2021 cannot be attributed to the worsening of the pandemic situation or to the lockdown. In sum, we depict a scenario of remarkable stability of the electoral campaigns put forward by presidential candidates in terms of digitalization and professionalization. Its possible causes and consequences are discussed. Full article

Populism has been an inherent phenomenon in the history of the United States since the beginning of the republic to the present, but it is only in 2016 that a populist leader, Donald Trump, has won the presidential election. The article considers Trump’s victory as part of the history of USA populism, taking into consideration the demand and the support for it in specific groups of radicalized, mainly white American citizens, who, since the late 1960s, felt abandoned or even betrayed by the mainstream political leadership through times of economic restructuring, cultural changes, and demographic transitions. This broad overview shows how USA populism, far from being the product of lunatic leaders, is deeply rooted in long-term processes concerning millions of people. The United States are a nation that has been built by immigration and wracked by debates about each successive wave of it: however, the forms debates on immigration have taken vary according to the generations. This paper makes the attempt to analyze the specificities of the present debate. The major changes introduced in migration policies in 1965 have slowly produced demographic changes in the ethnic components of the nation. The transformational demographic change- the majority ethnic group- non-Hispanic white people becoming one of multiple minorities- has been exploited by right-wing populists, such as Pat Buchanan, since the Nineties. Donald Trump’s speech on immigration is connected with different ideological positions—conservatism, paleo-conservatism, nativism, white suprematism—that form the puzzle of Trumpism, which has become a reference for international populists. Furthermore, opposition to immigration means delimiting the borders of the nation: this is an evident symbol of the rejection of the globalist idea of a borderless world that an elite pursues and that is repudiated by Trumpism. With his open contempt for “globalism” (as the idea that economic and foreign policy should be planned in an international way) and for the liberal–cosmopolitan elites who have provided ideological cover for it, Donald Trump has rallied many Americans and gained supporters in different parts of the world. Full article

Social media platforms are part of everyday life, allowing the interconnection of people around the world in large discussion groups relating to every topic, including important social or political issues. Therefore, social media have become a valuable source of information-rich data, commonly referred to as Social Big Data, effectively exploitable to study the behavior of people, their opinions, moods, interests and activities. However, these powerful communication platforms can be also used to manipulate conversation, polluting online content and altering the popularity of users, through spamming activities and misinformation spreading. Recent studies have shown the use on social media of automatic entities, defined as social bots, that appear as legitimate users by imitating human behavior aimed at influencing discussions of any kind, including political issues. In this paper we present a new methodology, namely TIMBRE (Time-aware opInion Mining via Bot REmoval), aimed at discovering the polarity of social media users during election campaigns characterized by the rivalry of political factions. This methodology is temporally aware and relies on a keyword-based classification of posts and users. Moreover, it recognizes and filters out data produced by social media bots, which aim to alter public opinion about political candidates, thus avoiding heavily biased information. The proposed methodology has been applied to a case study that analyzes the polarization of a large number of Twitter users during the 2016 US presidential election. The achieved results show the benefits brought by both removing bots and taking into account temporal aspects in the forecasting process, revealing the high accuracy and effectiveness of the proposed approach. Finally, we investigated how the presence of social bots may affect political discussion by studying the 2016 US presidential election. Specifically, we analyzed the main differences between human and artificial political support, estimating also the influence of social bots on legitimate users. Full article

The green bond market develops rapidly and aims to contribute to climate mitigation and adaptation significantly. Green bonds as any asset are subject to transition climate risk, namely, regulatory risk. This paper investigates the impact of unexpected political events on the risk and returns of green bonds and their correlation with other assets. We apply a traditional and regression-based event study and find that events related to climate change policy impact green bonds indices. Green bonds indices anticipated the 2015 Paris Agreement on climate change as a favorable event, whereas the 2016 US Presidential Election had a significant negative impact. The negative impact of the US withdrawal from the Paris agreement is more prominent for municipal but not corporate green bonds. All three events also have a similar effect on green bonds performance in the long term. The results imply that, despite the benefits of issuing green bonds, there are substantial risks that are difficult to hedge. This additional risk to green bonds might cause a time-varying premium for green bonds found in previous literature. Full article

In this paper, we examined the changes in volatility overflow among the exchange rate of the Japanese yen (JPY), the Nikkei Stock Average (Nikkei), the Tokyo Stock Price Index (TOPIX) and the TOPIX sectoral indices for the period of 10 February 2016 to 24 March 2017. We employed the exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model, the cross-correlation function, and the daily logarithmic returns of JPY, Nikkei, TOPIX and the TOPIX components with a weight of 5% and more in estimations (banks, chemicals, electric appliances, information and communication, machinery and transportation equipment indices). The findings highlighted causality in variance (volatility spillover) among the variables. We revealed that volatility could also spread indirectly among the variables (from one variable to another through a third variable). We demonstrated how the impact of news about the results of the Brexit referendum (BR) and the United States presidential election (USE) in 2016 might spread among the variables indirectly within a week. Full article

(1) The study investigated the social network surrounding the hashtags #maga (Make America Great Again, the campaign slogan popularized by Donald Trump during his 2016 and 2020 presidential campaigns) and #trump2020 on Twitter to better understand Donald Trump, his community of supporters, and their political discourse and activities in the political context of the 2020 US presidential election. (2) Social network analysis of a sample of 220,336 tweets from 96,820 unique users, posted between 27 October and 2 November 2020 (i.e., one week before the general election day) was conducted. (3) The most active and influential users within the #maga and #trump2020 network, the likelihood of those users being spamming bots, and their tweets’ content were revealed. (4) The study then discussed the hierarchy of Donald Trump and the problematic nature of spamming bot detection, while also providing suggestions for future research. Full article

During the 2016 presidential election, Evangelical supporters of Donald Trump presented him as a modern version of the ancient King Cyrus of Persia. To many conservative Christians, the comparison offered a justification of voting for a candidate whose character supposedly was at odds with their Christian virtues. Subsequent to his inauguration, the idea of Trump being an American Cyrus continued to develop and circulate. It is the aim of this article to deepen the understanding of Cyrus as a political tool in the West and explain how he ended up as a means to mobilize American voters. With an emphasis on the last 250 years, the article looks at how various personalities have been compared to Cyrus or presented as modern Cyruses. Based on these examples, it develops a typology, arguing that the modern Cyrus can be best understood as different types and subtypes, of which several have been applied to Trump. The article demonstrates how the various subtypes have separate evolutionary lines, which in turn can be attributed to different goals and functions. Full article

With the wide popularity of social media, it’s becoming more convenient for people to express their opinions online. To better understand what the public think about a topic, sentiment classification techniques have been widely used to estimate the overall orientation of opinions in post contents. However, users might have various degrees of influence depending on their participation in discussions on different topics. In this paper, we address the issues of combining sentiment classification and link analysis techniques for extracting stances of the public from social media. Since social media posts are usually very short, word embedding models are first used to learn different word usages in various contexts. Then, deep learning methods such as Long Short-Term Memory (LSTM) are used to learn the long-distance context dependency among words for better estimation of sentiments. Third, we consider the major user participation in popular social media by adjusting the users weights to reflect their relative influence in user-post interaction graphs. Finally, we combine post sentiments and user influences into a total opinion score for extracting public stances. In the experiments, we evaluated the performance of our proposed approach for tweets about the 2016 U.S. Presidential Election. The best performance of sentiment classification can be observed with an F-measure of 72.97% for LSTM classifiers. This shows the effectiveness of deep learning methods in learning word usage in social media contexts. The experimental results on stance extraction showed the best performance of 0.68% Mean Absolute Error (MAE) in aggregating public stances on election candidates. This shows the potential of combining tweet sentiments and user participation structures for extracting the aggregate stances of the public on popular topics. Further investigation is needed to verify the performance in different social media sources. Full article

Drawing on data generated following the 2016 United States presidential election, in this article the author considers how a classroom makerspace made Black girls’ literacies visible in new ways. During a six-week integrated humanities unit in a third-grade public school classroom in the Midwestern U.S., four Black girls used making to create a space for themselves to collaboratively make sense of contemporary (im)migration issues. In the findings, the author provides two analytic snapshots to illustrate how the girls’ making exemplified the six components of the Black Girls’ Literacies Framework—an asset-oriented framing that highlights how Black girls’ literacies are (1) multiple, (2) connected to identities that are (3) historical, (4) collaborative, (5) intellectual, and (6) political/critical (Muhammad & Haddix, 2016). In closing, the author offers provocations for educational researchers and practitioners to consider, as they facilitate school-based opportunities for Black girls’ literacies to be made visible through making. Full article

Twitter sentiment analysis is an effective tool for various Twitter-based analysis tasks. However, there is still no neural-network-based research which takes both the tweet-text information and user-connection information into account. To this end, we propose the Attentional-graph Neural Network based Twitter Sentiment Analyzer (AGN-TSA), a Twitter sentiment analyzer based on attentional-graph neural networks. AGN-TSA fuses the tweet-text information and the user-connection information through a three-layered neural structure, which includes a word-embedding layer, a user-embedding layer and an attentional graph network layer. For the training of AGN-TSA, dedicated loss functions are designed for the structural controllability of AGN-TSA network. Experiments based on real-world dataset concerning the 2016 presidential election of America exhibit that AGN-TSA is superior under multiple metrics over several prevailing methods, with a performance boost of over 5%. The empirical settings of parameters are given based on extensive rotation experiments. Full article

Socially responsible investing (SRI) reap the benefits of a social consensus and is often presented as a solution to conciliate finance and sustainable development. This article investigates the performance and resilience of both socially responsible and conventional funds listed in the Japan Investment Trust Association (JITA) during two economic shocks (the U.S. election and Brexit) in 2016. To see the immediate reaction in fund performance around different shocks, an event study with market model using ordinary least square (OLS), an event study with market model using exponential generalized autoregressive heteroscedasticity (EGARCH) and an event study with Fama–French multi-factor model was used to avoid common features of return data such as non-normality, heteroscedasticity, and cross-correlation. This study found that the recent U.S. election had a significant positive effect whereas the Brexit referendum event had a significant negative shock on fund returns in Japan around the event window. It is evident from the empirical findings that, compared to conventional funds, socially responsible funds were more resilient to uncertainty around the recent U.S. presidential election whereas conventional funds were more sensitive during the Brexit referendum. The important implications of these findings are the optimal strategies of institutional or individual investors who have direct or indirect exposure to the fund volatility risk in Japan. Full article

In Colombia, right-wing leadership returned to power after winning the presidential elections in 2018 in a campaign in which they opposed the previous government, primarily because of the negotiations and peacemaking with the FARC-EP (Fuerzas Armadas Revolucionarias de Colombia—Ejército del Pueblo ‘Armed Revolutionary Forces of Colombia—People’s Army’), Colombia’s largest guerrilla organization. Globally, there is a vibrant academic debate about how to characterize the current rise of right-wing populism or authoritarianism, but more profound insights from each country’s situation and its political economy implications are needed. The victory in Colombia was due to numerous factors, including the support from some rural elites who have historically obstructed the enforcement of redistributive land policies. However, the populist aspirations of the right-wing government have been persistently frustrated not only by social unrest and political mobilization but also because of the enforcement of institutions previously incorporated into the country’s political scenario. Specifically, in terms of agrarian political economy, two sets of human rights-oriented institutional changes are relevant regarding this matter: (a) the Land Restitution Law enacted in 2011 and (b) the Comprehensive Rural Reform contained in the Agrarian Chapter of the Peace Agreement between the national government and the FARC-EP in 2016. The purpose of this paper is to ground the ongoing theoretical and political debate about the rise of different forms of populism and right-wing authoritarianism in the current Colombian political context, and its implications on the countryside. The analytical contribution of this paper is twofold: On the one hand, I propose an alternative for explaining the nature of the current political regime in Colombia as right-wing authoritarianism; on the other hand, I analyze some features of such regimes in terms of its disputes with the enforcement of human rights-oriented institutions, that are in force as the result of political processes triggered by peasants’ mobilization. Full article

Popular and scholarly accounts of Trump’s ascendency to the presidency of the United States on the part of the American white working-class use different variables to define the sociodemographic group because there is no “working-class White” variable available in benchmark datasets for researchers to code. To address this need, the Author ran a multinomial regression to assess whether income, education and racial identity predict working-class membership among white Americans, finding that income and education are statistically significant predictors of working-class whiteness, while racial identity is not. Arriving at a robust definition of “white working-class” in light of these findings, the paper next turns to a review of the extant literature. By retrieving studies from searches of computerised databases, hand searches and authoritative texts, the review critically surmises the explanatory accounts of Trump’s victory. Discussion of the findings from the review is presented in three principal sections. The first section explains how working-class White communities, crippled by a dearth of social and geographic mobility, have been “left behind” by the political elites. The second section examines how white Americans, whose dominant group position is threatened by demographic change, voted for Trump because of resonance between his populist rhetoric and their latent “racist” attitudes. The third and final section explores the implications of a changing America for native-born whites, and how America’s increasing ethnoracial diversity is eroding relations between its dominant and nondominant groups. The Author surmises by arguing that these explanatory accounts must be understood in the context of this new empirical approximation of “working-class White”. Full article