Opinion polling and election predictions

Election prediction by means of opinion polling is a rare empirical success story for social science, but one not previously considered by philosophers. I examine the details of a prominent case, namely the 2012 US presidential election, and draw two lessons of more general interest: 1) Methodology over metaphysics. Traditional metaphysical criteria were not a useful guide to whether successful prediction would be possible; instead, the crucial thing was selecting an effective methodology. 2) Which methodology? Success required sophisticated use of case-specific evidence from opinion polling. The pursuit of explanations via general theory or causal mechanisms, by contrast, turned out to be precisely the wrong path – contrary to much recent philosophy of social science

Towards a New Ontology of Polling Inaccuracy: The Benefits of Conceiving of Elections as Heterogenous Phenomena for the Study of Pre-election Polling Error

A puzzle exists at the heart of pre-election polling. Despite continual methodological improvement and repeated attempts to identify and correct issues laid bare by misprediction, average polling accuracy has not notably improved since the conclusion of the Second World War. In this thesis, I contend that this is the result of a poll-level focus within the study of polling error that is both incommensurate with its evolution over time and the nature of the elections that polls seek to predict. I hold that differences between elections stand as a plausible source of polling error and situate them within a novel four-level model of sources of polling error. By establishing the heterogenous nature of elections as phenomena and its expected impact on polling error, I propose a new election-level ontology through which the inaccuracy of polls can be understood. I test the empirical validity of this new ontology by using a novel multi-level model to analyse error across the most expansive polling dataset assembled to date, encompassing 11,832 in-campaign polls conducted in 497 elections across 83 countries, finding that membership within different elections meaningfully impacts polling error variation. With the empirical validity of my proposed ontology established, I engage in an exploratory analysis of its benefits, finding electoral characteristics to be useful in the prediction of polling error. Ultimately, I conclude that the adoption of a new, multi-level ontology of polling error centred on the importance of electoral heterogeneity not only offers a more comprehensive theoretical account of its sources than current understandings, but is also more specifically tailored to the reality of pre-election polling than existing alternatives. I also contend that it offers pronounced practical benefits, illuminating those circumstances in which polling error is likely to vary

Contributions to modeling with set-valued data: benefitting from undecided respondents

This dissertation develops a methodological framework and approaches to benefit from undecided survey participants, particularly undecided voters in pre-election polls. As choices can be seen as processes that - in stages - exclude alternatives until arriving at one final element, we argue that in pre-election polls undecided participants can most suitably be represented by the set of their viable options. This consideration set sampling, in contrast to the conventional neglection of the undecided, could reduce nonresponse and collects new and valuable information. We embed the resulting set-valued data in the framework of random sets, which allows for two different interpretations, and develop modeling methods for either one. The first interpretation is called ontic and views the set of options as an entity of its own that most accurately represents the position at the time of the poll, thus as a precise representation of something naturally imprecise. With this, new ways of structural analysis emerge as individuals pondering between particular parties can now be examined. We show how the underlying categorical data structure can be preserved in this formalization process for specific models and how popular methods for categorical data analysis can be broadly transferred. As the set contains the eventual choice, under the second interpretation, the set is seen as a coarse version of an underlying truth, which is called the epistemic view. This imprecise information of something actually precise can then be used to improve predictions or election forecasting. We developed several approaches and a framework of a factorized likelihood to utilize the set-valued information for forecasting. Amongst others, we developed methods addressing the complex uncertainty induced by the undecided, weighting the justifiability of assumptions with the conciseness of the results. To evaluate and apply our approaches, we conducted a pre-election poll for the German federal election of 2021 in cooperation with the polling institute Civey, for the first time regarding undecided voters in a set-valued manner. This provides us with the unique opportunity to demonstrate the advantages of the new approaches based on a state-of-the-art survey

Utilizing Learning Algorithms to Improve News Transparency and Electoral Predictions

Governmen

Social media analytics and the role of twitter in the 2014 South Africa general election: a case study

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science., University of the Witwatersrand, Johannesburg, 2018Social network sites such as Twitter have created vibrant and diverse communities in which users express their opinions and views on a variety of topics such as politics. Extensive research has been conducted in countries such as Ireland, Germany and the United States, in which text mining techniques have been used to obtain information from politically oriented tweets. The purpose of this research was to determine if text mining techniques can be used to uncover meaningful information from a corpus of political tweets collected during the 2014 South African General Election. The Twitter Application Programming Interface was used to collect tweets that were related to the three major political parties in South Africa, namely: the African National Congress (ANC), the Democratic Alliance (DA) and the Economic Freedom Fighters (EFF). The text mining techniques used in this research are: sentiment analysis, clustering, association rule mining and word cloud analysis. In addition, a correlation analysis was performed to determine if there exists a relationship between the total number of tweets mentioning a political party and the total number of votes obtained by that party. The VADER (Valence Aware Dictionary for sEntiment Reasoning) sentiment classifier was used to determine the public’s sentiment towards the three main political parties. This revealed an overwhelming neutral sentiment of the public towards the ANC, DA and EFF. The result produced by the VADER sentiment classifier was significantly greater than any of the baselines in this research. The K-Means cluster algorithm was used to successfully cluster the corpus of political tweets into political-party clusters. Clusters containing tweets relating to the ANC and EFF were formed. However, tweets relating to the DA were scattered across multiple clusters. A fairly strong relationship was discovered between the number of positive tweets that mention the ANC and the number of votes the ANC received in election. Due to the lack of data, no conclusions could be made for the DA or the EFF. The apriori algorithm uncovered numerous association rules, some of which were found to be interest- ing. The results have also demonstrated the usefulness of word cloud analysis in providing easy-to-understand information from the tweet corpus used in this study. This research has highlighted the many ways in which text mining techniques can be used to obtain meaningful information from a corpus of political tweets. This case study can be seen as a contribution to a research effort that seeks to unlock the information contained in textual data from social network sites.MT 201