Super Tuesday Research Articles

Super Tuesday Lays Out Election’s Environmental Stakes

U.S. elections could pull the country back from several environmental tipping points—or push it beyond them. Scientists are urging people to seek climate action beyond the ballot.

Sentiment and the belief in fake news during the 2020 presidential primaries

Abstract The influence of misinformation on the political decision-making process became a major concern in the United States after the 2016 Presidential election. We study the impact of “fake news” in the 2020 election cycle by conducting an online experiment the day before the “Super Tuesday” primary elections. Respondents attempt to differentiate between fake and true news headlines that vary exogenously with respect to the emotive language in which they are written. In addition to confirming the effect of demographics and identifying a new determinant of beliefs, the illusion of personal objectivity, we find that the sentiment of a headline determines whether it is believed and correctly assessed. We also find that the effects of emotive language vary by political affiliation. These results suggest ways to protect oneself against misinformation and engage critically with the news.

Do people want smarter ballots?

We ascertain whether citizens want to have smart ballots, that is, whether they appreciate having the possibility to express some support for more than one option (expression across options) and to indicate different levels of support for these options (expression within options). We conducted two independent yet complementary survey experiments at the time of the Super Tuesday Democratic primaries to examine which voting method citizens prefer, one with the real candidates in the states holding Democratic primaries and one with fictitious candidates in the whole country. In both surveys, respondents were asked to vote using four different voting rules: single, approval, rank, and point (score). After they cast their vote, respondents were asked how satisfied they were using each voting method. The findings are consistent in both studies: the single vote is the most preferred voting method. We show that this is a reflection of a status quo bias, as citizens’ views are strongly correlated with age.

The Disappearing Female Candidates: A Framing Analysis of Newspaper Headlines in the 2020 Presidential Election

This study content analyzed 1,215 headlines from 63 newspapers in the U.S. on presidential candidates from the Iowa Caucuses to Super Tuesday in the 2020 Primary elections. The findings indicate that female candidates receive significantly less coverage than male candidates, which confirm the previous studies of female politicians in the media. However, study results also suggest female candidates are making progress in terms of negative, positive, active, and passive framing in media spaces.

Choosing to vote as usual

In the United States, there is a growing momentum towards ranked choice voting. In this study, we conducted two independent yet complementary surveys to examine which voting system citizens prefer, one with the real candidates in the states holding Super Tuesday Democratic primaries and one with fictitious candidates in the whole country. In both surveys, respondents were invited to vote using four different voting rules – single, approval, rank, and point (score) – and then asked how satisfied they were using each system. In both studies the single vote remains the most preferred voting method. We find that citizens’ views are strongly correlated with age, with the eldest being much more favorable to the single vote. Surprisingly, these views do not vary across education levels. Finally, those who have had personal experience with ranked ballot are more positive towards ranked voting, which suggests the presence of a status quo bias.

Clearing the Field: How do Presidential Primary Candidates Win Big on Super Tuesday?

In presidential primaries, Super Tuesday elections play a significant role in winnowing candidate fields and establishing nomination frontrunners. Despite their importance, scholars know little about why and how candidates win or lose the states comprising these events. This study explores which factors help explain candidate performance in Super Tuesday primaries between 2008 and 2016. Using pooled cross-sectional time-series analysis, the results indicate three key drivers of Super Tuesday success: candidate viability, public attention, and media attention. These findings imply that presidential campaigns continue to be complex electoral events beyond the early primary states and suggest that underdog candidates can still win states under the right conditions. Future research should explore the interrelatedness of these three critical factors.

The Coup de Grâce to the Sanders Campaign: Michigan, Momentum & Viability.

We illuminate the dynamics of primary campaigns by looking at how individuals changed their candidate support, favorability and viability within a crucial state in the 2020 primary. Via a multi-wave survey of likely primary voters in Michigan, we show that Joe Biden’s comeback win was due primarily to the change in perception of his viability following South Carolina and Super Tuesday, especially among the more moderate portion of the Democratic electorate. In subsequent county-level analyses we find that the difference between Bernie Sanders’s win in 2016 and loss in 2020 was more a function of changes in the Democratic primary electorate than in his appeal. Our work suggests that gains in viability do not benefit all candidates equally. Moreover, viability can be generated later in the schedule than previously thought and to decisive ends. We consider the implications of our findings for understanding Michigan’s outcome in November.

Firm-Level Risk Exposures and Stock Returns in the Wake of COVID-19

Firm-level stock returns differ enormously in reaction to COVID-19 news. We characterize these reactions using the Risk Factors discussions in pre-pandemic 10-K filings and two text-analytic approaches: expert-curated dictionaries and supervised machine learning (ML). Bad COVID-19 news lowers returns for firms with high exposures to travel, traditional retail, aircraft production and energy supply – directly and via downstream demand linkages – and raises them for firms with high exposures to healthcare policy, e-commerce, web services, drug trials and materials that feed into supply chains for semiconductors, cloud computing and telecommunications. Monetary and fiscal policy responses to the pandemic strongly impact firm-level returns as well, but differently than pandemic news. Despite methodological differences, dictionary and ML approaches yield remarkably congruent return predictions. Importantly though, ML operates on a vastly larger feature space, yielding richer characterizations of risk exposures and outperforming the dictionary approach in goodness-of-fit. By integrating elements of both approaches, we uncover new risk factors and sharpen our explanations for firm-level returns. To illustrate the broader utility of our methods, we also apply them to explain firm-level returns in reaction to the March 2020 Super Tuesday election results.

Voting Error across Multiple Ballot Types: Results from Super Tuesday (2020) Experiments in Four American States

This paper reports findings on voting error from online experiments involving real presidential candidates on hypothetical ballots. Over 6,000 respondents in four American states in February and March of 2020 were given the opportunity to vote in a simulated Democratic Party presidential primary and a simulated blanket (all-party) presidential primary. Experimental subjects were randomly assigned one of three different voting methods for each contest: a traditional single-mark (a.k.a. “exclusive” or “categorical”) ballot, a ranking (a.k.a “ordinal”) ballot, and a grading (a.k.a. “evaluative” or “range”) ballot. Results show that, on the most basic and widespread measure of voting error, the traditional ballot produced more void (a.k.a. “residual” or “rejected”) votes than the reform alternatives. At the same time, the rates of mismarked votes, or those with at least one violation of the ballot’s instructions, were higher for the reform alternatives than for the traditional ballot. Though the opportunity to mark each and every candidate on ranking and grading ballots generated more violations of the instructions, it also enabled more voters to express a clear, countable judgment of at least one candidate. Other results suggest that group-based inequalities in voting error – measured by the discrepancies in void rates across age, gender, and race cohorts – are smaller with reform ballots. These findings cast doubt on the conventional wisdom that single-mark voting is inherently “natural” or “intuitive” for all voters, irrespective of personal traits, group identities, or socialization. They also defy the presumed theoretical trade-off, or zero-sum game, between more expressive ballot types and more accessible (easy-to-use) ones.

Firm-Level Risk Exposures and Stock Returns in the Wake of COVID-19

Firm-level stock returns differ enormously in reaction to COVID-19 news. We characterize these reactions using the Risk Factors discussions in pre-pandemic 10-K filings and two text-analytic approaches: expert-curated dictionaries and supervised machine learning (ML). Bad COVID-19 news lowers returns for firms with high exposures to travel, traditional retail, aircraft production and energy supply — directly and via downstream demand linkages — and raises them for firms with high exposures to healthcare policy, e-commerce, web services, drug trials and materials that feed into supply chains for semiconductors, cloud computing and telecommunications. Monetary and fiscal policy responses to the pandemic strongly impact firm-level returns as well, but differently than pandemic news. Despite methodological differences, dictionary and ML approaches yield remarkably congruent return predictions. Importantly though, ML operates on a vastly larger feature space, yielding richer characterizations of risk exposures and outperforming the dictionary approach in goodness-of-fit. By integrating elements of both approaches, we uncover new risk factors and sharpen our explanations for firm-level returns. To illustrate the broader utility of our methods, we also apply them to explain firm-level returns in reaction to the March 2020 Super Tuesday election results.

Event Detection in Twitter Stream Using Weighted Dynamic Heartbeat Graph Approach [Application Notes

Tweets about everyday events are published on Twitter. Detecting such events is a challenging task due to the diverse and noisy contents of Twitter. In this paper, we propose a novel approach named Weighted Dynamic Heartbeat Graph (WDHG) to detect events from the Twitter stream. Once an event is detected in a Twitter stream, WDHG suppresses it in later stages, in order to detect new emerging events. This unique characteristic makes the proposed approach sensitive to capture emerging events efficiently. Experiments are performed on three real-life benchmark datasets: FA Cup Final 2012, Super Tuesday 2012, and the US Elections 2012. Results show considerable improvement over existing event detection methods in most cases.

Enhanced Heartbeat Graph for emerging event detection on Twitter using time series networks

With increasing popularity of social media, Twitter has become one of the leading platforms to report events in real-time. Detecting events from Twitter stream requires complex techniques. Event-related trending topics consist of a group of words which successfully detect and identify events. Event detection techniques must be scalable and robust, so that they can deal with the huge volume and noise associated with social media. Existing event detection methods mostly rely on burstiness, mainly the frequency of words and their co-occurrences. However, burstiness sometimes dominates other relevant details in the data which could be equally significant. Besides, the topological and temporal relationships in the data are often ignored. In this work, we propose a novel graph-based approach, called the Enhanced Heartbeat Graph (EHG), which detects events efficiently. EHG suppresses dominating topics in the subsequent data stream, after their first detection. Experimental results on three real-world datasets (i.e., Football Association Challenge Cup Final, Super Tuesday, and the US Election 2012) show superior performance of the proposed approach in comparison to the state-of-the-art techniques.

Scientists fare poorly in Super Tuesday primary vote

Scientists fare poorly in Super Tuesday primary vote

On the Similarity of Lower-Stratospheric Potential Vorticity Dipoles above Tropical and Midlatitude Deep Convection

Abstract Simulations of the effects of deep convection on the structure of potential vorticity (PV) in the upper troposphere and lower stratosphere (UTLS) have shown that a common signature in the presence of ambient horizontal vorticity is a horizontal PV dipole. Here, the relationship between convection and PV structures in the UTLS in Tropical Cyclone Talas and the extratropical “Super Tuesday” cyclone is investigated with the University of Wisconsin Nonhydrostatic Modeling System (UWNMS). Dipoles of potential temperature in the UTLS are interpreted as an upward deflection of the ambient flow over the updraft (cold), followed by subsidence in its lee (warm), aligned with the wind direction. PV dipoles larger than ±20 PV units (1 PVU = 10−6 K kg−1 m2 s−1) are identified, with typical vertical and horizontal extents of ~3 and ~200 km, respectively, and lifetimes up to 12 h. Confirming the findings of Chagnon and Gray, it is found that horizontal PV dipoles are related to vortex tilting, where horizontally oriented vorticity associated with vertical shear of the ambient wind is bent into a horseshoe shape by the updraft, yielding a PV dipole. This suggests that theta dipoles are perpendicular to PV dipoles and that “low PV lies to the left of the wind shear,” or, in the case of tropical cyclones, “low PV lies radially outward.” Mesoscale jets occur between the dipoles, which oppose the ambient anticyclonic flow. During the extratropical transition of Talas, convective PV anomalies evolved under synoptic-scale deformation into a pair of PV streamers, which modified the midlatitude westerly jet.

미국 대통령 예비선거에 적용한 시공간 의존성을 고려한 자기로지스틱 회귀모형 연구

미국 대통령 예선은 선거인단이 시차를 두고 여러 회에 걸쳐 진행되는 특징이 있음에도 많은 연구가 진행되지 않았다. 본 연구에서는 다양한 자기로지스틱 모형을 통해 미국 대통령 예비선거 결과와 사회경제적 변수간의 시공간 의존성의 관계를 파악하고자 한다. 2016년 데이터에 적용한 분석결과 각 카운티의 노년층, 흑인, 여성 그리고 히스패닉 인구 비율이 높은 지역일수록 힐러리 클린턴을 지지할 확률이 높은 것으로 나타났다. 또한, 주변 카운티에서 많은 지지를 받은 후보가 이웃 지역에서도 많이 지지를 받을 확률이 높고 이전 선거에서 많은 지지를 받는 것과 다음 선거 지역의 결과 간의 상관관계도 확인되었다. 시공간 의존성을 알아보기 위한 모형 중에서 슈퍼화요일의 선거 결과가 이후 선거와 관련이 있다고 가정한 모형의 설명력이 가장 높은 것으로 판명되었다. The US presidential primaries take place sequentially in different places with a time lag. However, they have not attracted as much attention in terms of modelling as the US presidential election has. This study applied several autologistic models to find the relation between the outcome of the primary election for a Democrat candidate with socioeconomic attributes in consideration of spatial and temporal dependence. According to the result applied to the 2016 election data at the county level, Hillary Clinton was supported by people in counties with high population rates of old age, Black, female and Hispanic. In addition, spatial dependence was observed, representing that people were likely to support the same candidate who was supported from neighboring counties. Positive auto-correlation was also observed in the time-series of the election outcome. Among several autologistic models of this study, the model specifying the effect of Super Tuesday had the best fit.

Hillary Clinton's presidential campaign rhetoric: Making America whole again

This article sought to investigate Hillary Rodham Clinton's 2016 campaign rhetoric for the U.S. President as found in her South Carolina Primary Victory Speech and Super Tuesday Victory Speech. Qualitative content analysis was used through Leanne's analytical tool of rhetorical strategies to deconstruct the texts. It gave way to new analytical narratives to decipher the core meaning as expressed in the central ideas and the main ideas of the two speeches. By so doing, access was easily made for studying the four components of Clinton's campaign rhetoric. The article showed that both speeches shared similar themes in the four components of the campaign rhetoric, illuminating consistently Clinton's lines of thought and political goals. She succinctly spelled out her agenda to make America whole again; thus, providing opportunity, dignity, and justice for every American. She framed her political stand to continue the preceding Democratic President's success to improve the American economy. She presented herself as a strong leader capable of advancing the American economy that worked for every American, breaking all barriers and restoring their common faith for a better America. She evoked hopes on the part of her prospective voters for their common better future and urged them to desire love and kindness in their lives, and use the emotion of anger against the barriers that divided them.

The Anti-Trumps: In Their Own Words

On “Super Tuesday” March 1, 2016, Donald Trump solidified his lead as the GOP frontrunner by seizing 8 of the 12 States that were up for grabs that day. He did so even though he had insulted every fellow Republican who has criticized him, the nation of Mexico, the entirety of the Muslim world and even Pope Francis: the undisputed sovereign of Christianity (albeit very carefully). Trump’s comments were consistently tinged with racism and intolerance; and these veiled epithets garnered the support of such disparate figures as Vladimir Putin and David Duke. Conventional wisdom would dictate that his political aspirations should have been derailed long ago. But his constant vitriol only served to increase his support among a large plurality of the electorate who felt increasingly disenfranchised by the policies of their government both domestically and abroad. Beginning with Trump’s announcement that he would run for the presidency in June, 2015, the mainstream media became fascinated by the collective psyche and enthusiasm of Trump supporters. There were Anti-Trumps all the while but they did not demonstrate nearly as much overt enthusiasm in their efforts to derail his candidacy. They just figured that Trump’s ideas were so outrageous that he would derail himself. But with Trump’s decisive victories on March 1, which now looks like a significant turning point, the entire dynamic of the presidential race phased into an entirely new dynamic. The media spotlight, which had previously shone mainly on Trump and his supporters, was about to be shared with a widely disparate group of oppositionists who shared one common goal: Stop Trump! And strange bedfellows they became.

A rule dynamics approach to event detection in Twitter with its application to sports and politics

The increasing popularity of Twitter as social network tool for opinion expression as well as information retrieval has resulted in the need to derive computational means to detect and track relevant topics/events in the network. The application of topic detection and tracking methods to tweets enable users to extract newsworthy content from the vast and somehow chaotic Twitter stream. In this paper, we apply our technique named Transaction-based Rule Change Mining to extract newsworthy hashtag keywords present in tweets from two different domains namely; sports (The English FA Cup 2012) and politics (US Presidential Elections 2012 and Super Tuesday 2012). Noting the peculiar nature of event dynamics in these two domains, we apply different time-windows and update rates to each of the datasets in order to study their impact on performance. The performance effectiveness results reveal that our approach is able to accurately detect and track newsworthy content. In addition, the results show that the adaptation of the time-window exhibits better performance especially on the sports dataset, which can be attributed to the usually shorter duration of football events.

The Millennial Vote in the 2016 Democratic Primary Elections

The Millennial Vote in the 2016 Democratic Primary Elections Fred M. Shelley (bio) and Ashley M. Hitt (bio) introduction and background In the spring of 2016, Democratic Party voters began the process of selecting their party’s nominee for President to succeed Barack Obama. During 2015, five candidates began campaigning for the Democratic nomination, including former Senator and Secretary of State Hillary Clinton of New York, former Governors Martin O’Malley of Maryland and Lincoln Chafee of Rhode Island, former Senator James Webb of Virginia, and Senator Bernard Sanders of Vermont. Chafee, Webb, and O’Malley generated very little enthusiasm and soon dropped out. By late 2015, it had become clear that Sanders would become Clinton’s primary challenger. Many journalists and observers regarded Sanders, who was 74 years old, a self-described democratic socialist, and not even a formal member of the Democratic Party for most of his career, as an unlikely challenger to Clinton. Clinton had been a well-known public figure for more than two decades, including eight years as First Lady, eight years as a Senator, and four years as Secretary of State. Sanders has served in Congress for 25 years, but as an obscure Senator from a small state had received little public attention. During late 2015 and early 2016, however, public opinion polls showed steady increases in support for Sanders. By February 2016, some public opinion surveys showed that support for Sanders and Clinton among likely Democratic primary voters had become even. According to these public opinion polls, Sanders’ strength came particularly from millennial voters, or those aged between 17 and 29.1 Survey after survey revealed that a large majority of millennial Democrats preferred Sanders to Clinton—a pattern reminiscent of 2008, in which millennial voters gave then-Senator Barack Obama a large majority of votes over Clinton in both the Democratic Party primaries and the general election (Shelley and Hollen 2011). This unexpectedly high level of support for Sanders among millennial voters helped to keep Sanders’ campaign afloat in the early primaries. [End Page 273] In 2016, the process of selecting the Democratic Party nominee began with precinct caucuses in Iowa on February 1. The first primary election took place in New Hampshire on February 9, followed by caucuses in Nevada on February 20 and a primary election in South Carolina on February 27. These first four contests were followed by eleven more on March 1, which was dubbed “Super Tuesday” and included primaries in Alabama, Arkansas, Georgia, Massachusetts, Oklahoma, Tennessee, Texas, Vermont, and Virginia and caucuses in Colorado and Minnesota. Among these fifteen states, Sanders and Clinton fought to a virtual tie in Iowa. Clinton won in Alabama, Arkansas, Georgia, Massachusetts, Nevada, South Carolina, Tennessee, Texas, and Virginia, while Sanders won Colorado, Minnesota, New Hampshire, Oklahoma, and Vermont. During the weekend of March 5 and 6, Sanders won caucuses in Kansas, Maine, and Nebraska while Clinton won a primary in Louisiana. On March 8, Clinton won by a landslide in Mississippi but lost narrowly to Sanders in Michigan, which she had been expected to carry by more than ten percentage points. A week later, Clinton won Florida handily, won North Carolina and Ohio by substantial margins, and won narrow victories in Illinois and Missouri. In this paper, we investigate geographical dimensions of support for Sanders among millennial voters in these early primaries and caucuses, and how racial identification affected the decisions of young voters. First, we review reasons underlying Sanders’ popularity among younger Democrats relative to Clinton. Drawing from social media sites and various other media sources, and using exit poll data at the state level, we compare levels of support for Sanders among millennial voters relative to other voters. We then investigate other aspects of millennial support for Sanders, using county-level election data from 2016 and 2008, census data and data taken from social media sites. why have millennial voters preferred sanders to clinton? As Sanders’ support increased in public opinion polls in early 2016, observers noticed that the surge in levels of support for Sanders’ candidacy was particularly evident among millennial voters. Public opinion surveys indicated that current college students, most of whom are millennials, are more liberal...

Introduction to Special Forum on the Geographies of the Presidential Election

Introduction to Special Forum on the Geographies of the Presidential Election Hilda E. Kurtz As the U.S. Presidential campaigns grew increasingly turbulent in the midwinter of 2016, the editorial team at Southeastern Geographer began to wonder how different geographers might make sense of the complex socio-spatial dynamics shaping the election season. Viewing the journal as a forum in which to wrestle with the complex geographies of the southern region of the U.S., we were particularly interested in the implications of the growing relevance of southern voters to presidential elections, as evidenced in stacking of Super Tuesday primaries to include four southern states (Alabama, Georgia, Tennessee, and Virginia) among the 12 states voting. At the same time, viewing the region relationally, we were interested to bring together voices that could iterate between a regional and national perspective on the presidential campaigns. To that end, we reached out to geographers from across the discipline and across the country whose work could help us make some sense of the twists and turns of this campaign season. We invited seasoned political geographers John Agnew and Fred Shelley and urban/cultural/political geographers Caroline Nagel Winders to draw on their respective areas of expertise. Together, these essays offer textured perspectives on the voting patterns and political discourses which distinguish the current presidential campaign season. John Agnew and Michael Shin, having drawn powerfully on geographical analysis to shed light on the rise and fall of Sergio Berlusconi in Berlusconi’s Italy: Mapping Contemporary Italian Politics (2008, Temple University Press), consider the parallels between Silvio Berlusconi and Donald Trump. Their essay offers a critical reflection on the dramaturgy of Donald Trump’s presidential campaign, reading Trump’s ascendance in part against re-alignments of party politics. Dr. Agnew has invoked the concept of political dramaturgy as a way to consider the staging of political parties’ interests at different geographical scales (Agnew 1997), and the term resonates here in a way that sheds light on the “Theater of and Jamie Politics”. Fred Shelley and Ashley Hitt blend traditional techniques of electoral analysis (which Shelley helped pioneer, see Archer and Shelley 1986; Shelley 1984) [End Page 262] with data derived from social media to tease apart the demographics of support for Senator Bernie Sanders of Vermont. It is well known by now that Sanders plays especially well to millennial voters. In an innovative and iterative approach, Shelley and Shin combine social media data with exit poll data to offer insight into the demographics of support for the candidate who has, by many accounts, used social media more effectively than any other candidate for U.S. office, ever. In an exploratory mode, and with caveats in mind about large but biased samples, Shelley and Hitt juxtapose the results of exit polls with the geography of face-book “likes” for Democratic candidates Hillary Clinton and Bernie Sanders in overwhelmingly Republican Alabama and Oklahoma. Caroline Nagel’s essay offers an historical lens on the mixed reception of immigrants into Southern communities (Winders 2013; Ehrkamp and Nagel 2014; Steusse and Coleman2014), and the deep ambivalence with which the U.S. as a whole has received refugees over recent decades. In an essay which richly situates conditions in southern states in relation to national discourses and anxieties, Nagel considers how it is that President Obama’s decision to admit a mere 10,000 refugees from critically war-torn Syria “create[d] such a fuss”. Nagel draws on her ongoing research into the reception of immigrants and refugees (Ehrkamp and Nagel 2012, 2014; Nagel 2013) in southern communities to unpack the appeal of national scale anti-Muslim, pro-border control discourses to Southern voters. Finally, Jamie Winders draws on her past work examining the figure of the immigrant within political and organizational discourse (Winders 2011) to sift through the “headlines, debate zingers, and soundbites” of the fractious presidential campaign season. She examines the immigration policy statements of the leading (remaining) candidates for the highest office in the U.S. tracing differential emphases on immigrants as sources of threat, as members of families, and as workers. Winders also notes a temporal dimension to the competing immigration stances of the presidential candidates, evaluating candidates...