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Migration forecasts are crucial for proactive immigration and integration management. While the demand for accurate migration forecasts continues to grow, the current state of migration forecasting is still unsatisfactory, because they tend to lack precision. We introduce an alternative method to forecast migration movements: prediction markets. While prediction markets are mainly unknown in migration studies, they are established in the political economy of forecasting election outcomes. For its application to a complex phenomenon in a more constrained information environment such as migration movements, we argue that prediction markets allow to balance complementarities of current qualitative and quantitative approaches if they provide solutions to avoid thin trading and integrate expert knowledge into the market. We apply the prediction market to forecast immigration in four West European countries in 2020 and find encouraging results. We discuss the strengths and limitations of prediction markets to migration forecasting, including ethical considerations, and guide its future application. [ABSTRACT FROM AUTHOR]

While many instructors are aware of the Literary Digest 1936 poll as an example of biased sampling methods, this article details potential further explorations for the Digest's 1924–1936 quadrennial U.S. presidential election polls. Potential activities range from lessons in data acquisition, cleaning, and validation, to basic data literacy and visualization skills, to exploring one or more methods of adjustment to account for bias based on information collected at that time. Students can also compare how those methods would have performed. One option could be to give introductory students a first look at the idea of "sampling adjustment" and how this principle can be used to account for difficulties in modern polling, but the context is rich in other opportunities that can be discussed at various times in the course or in more advanced sampling courses. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

This article examines the July 2023 general elections in Spain and its potential impact on the governance of the country. Contrary to what most pre-election polls predicted, the two main opposition parties (i.e., PP and Vox) did not manage to earn sufficient electoral support and, as a result, a new coalition government of centre-left parties was formed. The outcome of these elections was notably shaped by the high levels of polarization registered in the country in the last years. This report tackles the most important elements of the elections with a historical perspective and an empirical approach, and how the subsequent formation of a new left-wing coalition government at the national level could affect the future electoral prospects of the incumbent parties. [ABSTRACT FROM AUTHOR]

Prior literature shows that social media could be used to forecast political elections. Most studies have focused on a single social media platform, and few studies have explored the use of social media data across multiple platforms to make election predictions. Though candidates' personal attributes have also been suggested as critical factors affecting election results, there has been little research into the interacting effect of social media and candidate attributes in predicting elections. To address the research gap, this article investigates the role of two different social media platforms, Twitter (now known as "X") and Facebook, in forecasting the 2019 Finnish parliamentary elections and how candidates' political experience moderates the role of the two platforms in predicting elections. The findings show that both the number of Facebook likes and the number of Twitter followers are associated with the election outcome positively. Political experience of candidates moderates the association between the number of Facebook likes and election outcomes as well as the association between the number of Twitter followers and election outcomes. This research adds to the discussion on how social media can predict election results. It considers both different social media platforms and the roles of the candidate attributes. [ABSTRACT FROM AUTHOR]

Predicting the winner of an election is of importance to multiple stakeholders. To formulate the problem, we consider an independent sequence of categorical data with a finite number of possible outcomes in each. The data is assumed to be observed in batches, each of which is based on a large number of such trials and can be modeled via multinomial distributions. We postulate that the multinomial probabilities of the categories vary randomly depending on batches. The challenge is to predict accurately on cumulative data based on data up to a few batches as early as possible. On the theoretical front, we first derive sufficient conditions of asymptotic normality of the estimates of the multinomial cell probabilities and present corresponding suitable transformations. Then, in a Bayesian framework, we consider hierarchical priors using multivariate normal and inverse Wishart distributions and establish the posterior convergence. The desired inference is arrived at using these results and ensuing Gibbs sampling. The methodology is demonstrated with election data from two different settings—one from India and the other from the United States. Additional insights of the effectiveness of the proposed methodology are attained through a simulation study. [ABSTRACT FROM AUTHOR]

This article reviews the record of Germany’s current coalition government consisting of the SPD, Greens and liberal FDP under chancellor Olaf Scholz, a member of the SPD. Since 2021, the coalition’s failure to set a coherent policy agenda, permanent infighting and external challenges, such as the Ukraine war and immigration, have led to a dramatic decay in support for the governing parties. The opposition CDU/CSU has profited little from the government’s weakness, while the rightist AfD rides high in opinion polls and elections. Conversely, Die Linke experienced a split in December 2023 when a group of its legislators—led by Sahra Wagenknecht, the former co‐chair of the left’s parliamentary group, decided to found a new political party, the ‘Alliance Sahra Wagenknecht—Reason and Justice’. The article evaluates Wagenknecht’s political discourse which aims to combine progressive welfare and conservative migration policies. It is suggested that Wagenknecht’s ‘left conservatism’ is a promising electoral project filling a representation gap in German politics. [ABSTRACT FROM AUTHOR]

Public opinion polls have become vital and increasingly visible parts of election campaigns. Previous research has frequently demonstrated that polls can influence both citizens' voting intentions and political parties' campaign strategies. However, they are also fraught with uncertainty. Margins of error can reflect (parts of) this uncertainty. This paper investigates how citizens' voting intentions change due to whether polling estimates are presented with or without margins of error. Using a vignette experiment (N=3224), we examine this question based on a real‐world example in which different election polls were shown to nationally representative respondents ahead of the 2021 federal election in Germany. We manipulated the display of the margins of error, the interpretation of polls and the closeness of the electoral race. The results indicate that margins of error can influence citizens' voting intentions. This effect is dependent on the actual closeness of the race and additional interpretative guidance provided to voters. More concretely, the results consistently show that margins of error increase citizens' inclination to vote for one of the two largest contesting parties if the polling gap between these parties is small, and an interpretation underlines this closeness. The findings of this study are important for three reasons. First, they help to determine whether margins of error can assist citizens in making more informed (strategic) vote decisions. They shed light on whether depicting opinion‐poll uncertainty affects the key features of representative democracy, such as democratic accountability. Second, the results stress the responsibility of the media. The way polls are interpreted and contextualized influences the effect of margins of error on voting behaviour. Third, the findings of this paper underscore the significance of including methodological details when communicating scientific research findings to the broader public. [ABSTRACT FROM AUTHOR]

We correlate county-level partial and full vaccination rates with results from the 2020 United States (U.S.) election, obtaining Pearson correlation values for each individual state, noting top and bottom values, and observing an almost absolute positive correlation between Biden support and vaccination rates, and inversely a negative correlation when considering Trump support. This trend holds for other territorial categories: regions and subdivisions. Correlation values based on county-level data for two other categories of interest are found: urbanization level and ethnic group majority by county. We find a higher degree of correlation for White majority counties, and a lower one for majority minority counties. When analyzing urbanization level we find the lowest values in highly urban counties. We posit a relationship between these two categories, based on the fact that urban counties are generally more ethnically diverse, and suburban and rural areas generally retain white majorities. Our results showcase the current process of political polarization and history leading to this phenomenon, which includes the role that health topics and issues have played in it, reinforcing the idea of politics and health being strongly linked subjects in the United States. [ABSTRACT FROM AUTHOR]

Poll watchers play an increasingly visible role in American elections, yet there is little research into how their presence shapes beliefs about electoral integrity. On the one hand, poll watchers—like other election observers—may lead voters to be more trusting in elections. On the other hand, poll watchers who expressly represent political parties may contribute to polarized views of electoral integrity. We address this gap with two conjoint experiments and one vignette experiment administered on large national samples. The conjoint experiments vary several features of election polling locations, including the presence of poll watchers, voter identification requirements, and other election regulations. We find that regulations around poll watchers shape views of election fairness, but that the nature of this influence depends on the type and partisanship of poll watchers who are present. The vignette experiment shows that perceptions of poll watchers decline when these individuals act in bad faith; effects are especially large for Republican respondents. These findings help identify the potential that poll watchers have to shape Americans' beliefs about election fairness and integrity. [ABSTRACT FROM AUTHOR]

How do voters form accurate expectations about the strength of political candidates in constituency elections if there are no reliable constituency polls available? We argue that voters can use national election polls and past election results to increase the accuracy of their expectations. A survey experiment during the German federal election of 2021 confirms that the provision of national election polls and past results increases the accuracy of voters' expectations. The analysis further shows that voters leverage the information to update their beliefs. The results have relevant implications for debates about belief formation in low-information environments. [ABSTRACT FROM AUTHOR]

This study examined the effect of fake news on electoral outcome. Using post-election surveys, previous studies found associations between exposure to fake news and voting behavior, though these observational studies failed to show that these changes were actually caused by fake news. To examine whether fake news really affects voting behavior, we need to experimentally manipulate voters' exposure to fake news in real elections and see if voters regret their vote choice knowing that the information was false. For this purpose, our study focused on Mexico's 2018 presidential election, which provided an ideal setting. During the campaign, false information about a scandal allegedly involving Ricardo Anaya, a candidate from the National Action Party, was widely disseminated. However, his innocence was officially acknowledged after the election. Using this correction of fake news as a treatment, we tested a sample of 1,561 individuals to assess whether the retraction of fake news caused post-election regret: would Mexican voters have voted differently if they had not been exposed to such false information. Our multivariate analyses found that the retraction of fake news did cause post-election regret among voters with lower internal political efficacy, but voters associated with higher political knowledge and internal political efficacy were not affected by the retraction and were less likely to experience regret. About 20% of the respondents (N = 168) experienced post-election regret, and of those, about 35% would have switched their vote to Anaya. The findings corroborate lasting effects of fake news, which may have non-negligible effects on electoral outcomes. [ABSTRACT FROM AUTHOR]

This paper introduces the election optimization algorithm (EOA), a meta-heuristic approach for engineering optimization problems. Inspired by the democratic electoral system, focusing on the presidential election, EOA emulates the complete election process to optimize solutions. By simulating the presidential election, EOA introduces a novel position-tracking strategy that expands the scope of effectively solvable problems, surpassing conventional human-based algorithms, specifically, the political optimizer. EOA incorporates explicit behaviors observed during elections, including the party nomination and presidential election. During the party nomination, the search space is broadened to avoid local optima by integrating diverse strategies and suggestions from within the party. In the presidential election, adequate population diversity is maintained in later stages through further campaigning between elite candidates elected within the party. To establish a benchmark for comparison, EOA is rigorously assessed against several renowned and widely recognized algorithms in the field of optimization. EOA demonstrates superior performance in terms of average values and standard deviations across the twenty-three standard test functions and CEC2019. Through rigorous statistical analysis using the Wilcoxon rank-sum test at a significance level of 0.05, experimental results indicate that EOA consistently delivers high-quality solutions compared to the other benchmark algorithms. Moreover, the practical applicability of EOA is assessed by solving six complex engineering design problems, demonstrating its effectiveness in real-world scenarios. [ABSTRACT FROM AUTHOR]

In the digital age, maintaining election integrity is critical, especially in Africa, where the security of electronic elections is often questioned. This study presents a blockchain-based vote counting and validation (BBVV) system developed using a mixed methods approach that combines stakeholder questionnaires to capture system specification and randomized historical election data analysis, following the Design Science Research strategy. Using the theory of the Byzantine General Problem, the BBVV protocol is proposed, which provides an accurate local count of votes at polling stations before national aggregation. The system was tested with randomized historical election data on the Algorand blockchain TestNet and confirmed that a local consensus on the vote count could be reached before it is added to the national tally on the blockchain. Our results show that in the cases where consensus was reached, this was the instance in only about 5% of the voting scenarios, with only 10% of the total vote being considered valid due to the strict consensus requirements. In addition, significant discrepancies were found between officials, with no consensus reached in 95% of cases which was due to the rogue values generated by a randomized dataset. The performance of the BBVV system was evaluated using transaction metrics, saturation, throughput, traffic, and latency to assess its efficiency, scalability, and reliability. The results suggest that blockchain technology can significantly improve the integrity of elections by ensuring a transparent, secure, and accurate vote-counting process. Future work will focus on improving the adaptability and scalability of the BBVV system for different electoral situations. [ABSTRACT FROM AUTHOR]

Political journalism often tries to predict the future, especially the outcomes of elections. This has historically been accomplished through written articles or opinion pieces. A more recent development involves the publication of data-driven predictions in online news media. These news items not only contain an estimate for the election results but also often try to visualize potential uncertainties of the prediction. However, the ways in which users react to these forms of journalism have not yet been studied extensively. In this work, we survey users of a predictive journalism piece on the 2021 German federal elections published by the German newspaper Süddeutsche Zeitung to better understand their reactions. While we found an alignment between the designers' intention to show the inherent uncertainty of election predictions with the audience's reception, we encountered mixed results in users' ability to interpret the uncertainty visualizations presented. Most respondents indicated that the predictions did not influence their thinking about the race, and some remained skeptical toward such predictions published by journalists for various reasons. Based on these findings, we suggest the need for rigorous user testing of visualizations for election prediction and increased awareness and future research on ways to increase the transparency of methods and data to develop appropriate trust toward predictive journalism. [ABSTRACT FROM AUTHOR]

Introduction/purpose: The significance of studying voting behaviour is underscored by its ability to gauge the continuity or divergence of electoral politics from historical trends, elucidating the real impact of the transformative ballot box, and contributing to the examination of democracy as a value among both masses and elites. Additionally, it aids in comprehending the intricate process of political socialization. Methods: An inherent strength of the rough set lies in its reliance solely on raw data, devoid of external inputs. The decision-theoretic rough set framework, an evolution of the rough set, has garnered widespread application across diverse domains, serving as a proficient tool for acquiring knowledge, particularly in navigating situations marked by vagueness and uncertainty. Despite the proliferation of mathematical models designed to discern people's voting behavior, a decision-based rough set recommendation remains noticeably absent in existing literature. This paper introduces an innovative three-way decision approach grounded in linguistic information for identifying voting behavior. The proposed approach is based on a hybrid probabilistic rough fuzzy model incorporating linguistic information and providing insights into voting patterns. Results: The three-way decision hybrid models are tested on people and a highly satisfactory result was achieved for identifying their voting behaviours. The justification of results was validated through the mathematical process. Conclusion: A practical illustration is provided to highlight the importance of this hybrid model and to confirm its usefulness in identifying and forecasting voting behaviour. [ABSTRACT FROM AUTHOR]

Voters' beliefs about the strength of political parties are a central part of many foundational political science theories. In this article, we present a dynamic Bayesian learning model that allows us to study how voters form these beliefs by learning from pre-election polls over the course of an election campaign. In the model, belief adaptation to new polls can vary due to the perceived precision of the poll or the reliance on prior beliefs. We evaluate the implications of our model using two experiments. We find that respondents update their beliefs assuming that the polls are relatively imprecise but still weigh them more strongly than their priors. Studying implications for motivational learning by partisans, we find that varying adaptation works through varying reliance on priors and not necessarily by discrediting a poll's precision. The findings inform our understanding of the consequences of learning from polls during political campaigns and motivational learning in general. [ABSTRACT FROM AUTHOR]

Recent high-stake occurrences of polling error led some analysts to conclude that the polls are "broken." One question is whether domain knowledge can help identify the direction and magnitude of polling error before the fact, which would reduce uncertainty and improve accuracy. The present study compares the accuracy of a poll average to 4,494 expert forecasts, obtained across three German federal elections. Experts' domain knowledge was unable to improve upon the accuracy of polls. On average, experts' forecast error was 34% higher than the corresponding polling error, and in more than half of the cases, experts' forecasts pointed in the wrong direction. Polls remain to be an important source of information when it comes to forecasting elections, despite their limitations and underlying uncertainty. [ABSTRACT FROM AUTHOR]

A unifying setup for opinion models originating in statistical physics and stochastic opinion dynamics are developed and used to analyze election data. The results are interpreted in the light of political theory. We investigate the connection between Potts (Curie–Weiss) models and stochastic opinion models in the view of the Boltzmann distribution and stochastic Glauber dynamics. We particularly find that the q-voter model can be considered as a natural extension of the Zealot model, which is adapted by Lagrangian parameters. We also discuss weak and strong effects (also called extensive and nonextensive) continuum limits for the models. The results are used to compare the Curie–Weiss model, two q-voter models (weak and strong effects), and a reinforcement model (weak effects) in explaining electoral outcomes in four western democracies (United States, Great Britain, France, and Germany). We find that particularly the weak effects models are able to fit the data (Kolmogorov–Smirnov test) where the weak effects reinforcement model performs best (AIC). Additionally, we show how the institutional structure shapes the process of opinion formation. By focusing on the dynamics of opinion formation preceding the act of voting, the models discussed in this paper give insights both into the empirical explanation of elections as such, as well as important aspects of the theory of democracy. Therefore, this paper shows the usefulness of an interdisciplinary approach in studying real world political outcomes by using mathematical models. [ABSTRACT FROM AUTHOR]

This study investigates whether news sentiment plays a role in setting social media sentiment to explore the dynamics of sentiment develop and diffusion within the public agenda. Based on the agenda-setting theory, this study analyzed the public and media sentiments towards the 2016 US election and the candidates using data from Twitter, CNN, and Fox News. Focusing on the Twitter messages created by the supporters of Hillary Clinton and Donald Trump, over 1.3 million Twitter messages were collected associated with the election, employing hashtags as indicators of support. The Granger causality test between social media and news sentiments revealed that there is a mutual influence between social media and news sentiments; CNN's overall sentiment was influenced by the sentiment of Hillary Clinton's supporters, whereas Trump supporters' sentiment was influenced by Fox News' negative sentiment. The results suggest that public sentiment is formed in response to public agenda and mass media, indicating that sentiment is a critical component in understanding public opinion. Implications for future studies and limitations are also discussed. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to study the impact of the 2016 presidential election polls on the stock market. Design/methodology/approach: The empirical model includes daily stock returns as the dependent variable and past asset prices, 10-year treasury rates, opinion polls and VIX (market uncertainty) as explanatory variables with a one-year lag. The model was estimated using two sets of daily polling data: from July 1, 2015, to November 8, 2016, and from June 1, 2016, to November 8, 2016. Additional descriptive statistics, such as means and standard deviations, were also calculated. Findings: The estimated results did not reveal any statistically significant effects of opinion polls in favor of one candidate over another on stock returns. Simple statistical tests, however, show that the market performed better when Trump held a polling advantage over Clinton. Originality/value: To the best of the authors' knowledge, this is the only study that has examined the effects of the 2016 presidential election polls on the US stock market. This study adds value to the understanding of the relationship between election polls and the stock market in the USA. [ABSTRACT FROM AUTHOR]

The forecasting of presidential election results (PERs) is a very complex problem due to the diversity of electoral factors and the uncertainty involved. The use of a hybrid approach composed of techniques such as machine learning (ML) and Simulation in forecasting tasks is promising because the former presents good results but requires a good balance between data quantity and quality, and the latter supplies said requirement; nonetheless, each technique has its limitations, parameters, processes, and application contexts, which should be treated as a whole to improve the results. This study proposes a systematic method to build a model to forecast the PERs with high precision, based on the factors that influence the voter's preferences and the use of ML and Simulation techniques. The method consists of four phases, uses contextual and synthetic data, and follows a procedure that guarantees high precision in predicting the PER. The method was applied to real cases in Brazil, Uruguay, and Peru, resulting in a predictive model with 100% agreement with the actual first-round results for all cases. [ABSTRACT FROM AUTHOR]

Google trends (GT), a service aggregating search queries on Google, has been used to predict various outcomes such as as the spread of influenza, automobile sales, unemployment claims, and travel destination planning [1-2]. Social scientists also used GT to predict elections and referendums across different countries and time periods, sometimes with more, sometimes with less success. We provide unique evidence on the predictive power of GT in the German multi-party systems, forecasting four elections (2009, 2013, 2017, 2021). Thereby, we make several contributions: First, we present one of the first attempts to predict a multi-party election using GT and highlight the specific challenges that originate from this setting. In doing so, we also provide a comprehensive and systematic overview of prior research. Second, we develop a framework that allows for fine-grained variation of the GT data window both in terms of its width and distance to the election. Subsequently, we test the predictive accuracy of several thousand models resulting from those fine-grained specifications. Third, we compare the predictive power of different model classes that are purely GT data based but also incorporate polling data as well as previous elections. Finally, we provide a systematic overview of the challenges one faces in using GT data for predictions part of which have been neglected in prior research. [ABSTRACT FROM AUTHOR]

The article discusses the ECPPEC project, which focuses on political participation in parliamentary elections in England from 1695 to 1832. The project has created a website that includes a directory of poll books, information on elections, and voting behavior. The project has collected data on the number of elections and contested elections, providing insights into 18th-century politics. The text highlights the value of poll books in understanding voting behavior and offers potential for future research using big data techniques. [Extracted from the article]

In this article, we argue that electoral context affects the projection mechanisms inherent within polling. This insight applies both to the estimation of party vote shares by pollsters and to poll-based seat projections. To test this argument, we analyse 794 in-campaign polls covering the UK's 21 post-war general elections, as well as an updated version of Jennings and Wlezien's (Jennings, Will, and Christopher Wlezien. 2018. "Election Polling Errors Across Time and Space." Nature Human Behaviour 2 (4): 276–283) international polling dataset. We demonstrate that the election level houses a substantial portion of the observed variance within polling error. This finding is valid across several modelling approaches and a range of measures of polling accuracy both within and beyond the UK. Within the UK, we show that the election level is a particularly important locus of variance when it comes to analysing whether polls give rise to misleading expectations concerning seat distributions. [ABSTRACT FROM AUTHOR]

Election forecasting is a growing enterprise. Structural models relying on "fundamental" political and economic variables, principally to predict government performance, are popular in political science. Conventional wisdom though is these standard structural models fall short in predicting individual blocs' performance and their applicability to multiparty systems is restricted. We challenge this by providing a structural forecast of bloc performance in Ireland, a case primarily overlooked in the election forecasting literature. Our model spurns the economic and performance variables conventionally associated with structural forecasting enterprises and instead concentrates on Ireland's historical party and governance dynamics in the vein of testing whether these patterns alone offer solid predictions of election outcomes. Using Seemingly Unrelated Regression (SUR), our approach, comprising measures of incumbency, short-term party support, and political and economic shocks, offers reasonable predictions of the vote share performance of four blocs: Ireland's two major parties, Fianna Fáil and Fine Gael, Independents, and the Left bloc combined across 20 elections spanning 60 years. [ABSTRACT FROM AUTHOR]

International election observation has been scrutinized over the past two decades by scholars trying to explain its aim, scope, and consequences. As it is now a standard practice in democratizing countries, scholarly literature has almost reached a consensus on the ability of observers to deter election-day fraud, one of the most positive unintended consequences of election observation. Using data from the 2004 Ukraine presidential election, this article extends our knowledge through a natural experiment on polling station-level election results. I will show that observation during counting does not have a stronger effect than observation during opening and/or polling. Results support previous studies showing the usefulness of election observation in reducing election-day fraud but add an important finding that deserves further scholarly attention. [ABSTRACT FROM AUTHOR]

In accordance with the notion of democracy which is the basis of the state of Indonesia, general elections will be held in 2024. In the implementation of the General Election there is a campaign to lead the public vote to choose the best candidate according to public opinion. Twitter social media is one of the media to voice opinions as well as share information to become one of the indirect campaigning platforms. Social media also does not escape negative issues, community rumors, and even the digital footprint of presidential candidates which can be a very important consideration in campaigning. This research aims to see the public's response to the 2024 presidential candidates. This research is conducted based on public opinion on presidential candidates, then public opinion data taken from Twitter social media will go through a preprocessing process to clean the data before the data is classified into Naive Bayes and Linear Regression modeling. The two classification models are then sought for the highest performance accuracy value and confusion matrix with 80:20 splitting data. The results showed that the Naive Bayes classification model had a higher accuracy value than the Logistic Regression classification model, which was 63% for Anies Baswedan candidate, 77% for Ganjar Pranowo candidate, and 44% for Prabowo Subianto. The highest accuracy value was obtained by the sentiment data of 2024 presidential candidate Ganjar Pranowo, which was 77%. [ABSTRACT FROM AUTHOR]