Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election

Social media has become an emerging alternative to opinion polls for public opinion collection, while it is still posing many challenges as a passive data source, such as structurelessness, quantifiability, and representativeness. Social media data with geotags provide new opportunities to unveil the geographic locations of users expressing their opinions. This paper aims to answer two questions: 1) whether quantifiable measurement of public opinion can be obtained from social media and 2) whether it can produce better or complementary measures compared to opinion polls. This research proposes a novel approach to measure the relative opinion of Twitter users towards public issues in order to accommodate more complex opinion structures and take advantage of the geography pertaining to the public issues. To ensure that this new measure is technically feasible, a modeling framework is developed including building a training dataset by adopting a state-of-the-art approach and devising a new deep learning method called Opinion-Oriented Word Embedding. With a case study of the tweets selected for the 2016 U.S. presidential election, we demonstrate the predictive superiority of our relative opinion approach and we show how it can aid visual analytics and support opinion predictions. Although the relative opinion measure is proved to be more robust compared to polling, our study also suggests that the former can advantageously complement the later in opinion prediction

A meta-analysis of state-of-the-art electoral prediction from Twitter data

Electoral prediction from Twitter data is an appealing research topic. It seems relatively straightforward and the prevailing view is overly optimistic. This is problematic because while simple approaches are assumed to be good enough, core problems are not addressed. Thus, this paper aims to (1) provide a balanced and critical review of the state of the art; (2) cast light on the presume predictive power of Twitter data; and (3) depict a roadmap to push forward the field. Hence, a scheme to characterize Twitter prediction methods is proposed. It covers every aspect from data collection to performance evaluation, through data processing and vote inference. Using that scheme, prior research is analyzed and organized to explain the main approaches taken up to date but also their weaknesses. This is the first meta-analysis of the whole body of research regarding electoral prediction from Twitter data. It reveals that its presumed predictive power regarding electoral prediction has been rather exaggerated: although social media may provide a glimpse on electoral outcomes current research does not provide strong evidence to support it can replace traditional polls. Finally, future lines of research along with a set of requirements they must fulfill are provided.Comment: 19 pages, 3 table

Polling bias and undecided voter allocations: US Presidential elections, 2004 - 2016

Accounting for undecided and uncertain voters is a challenging issue for predicting election results from public opinion polls. Undecided voters typify the uncertainty of swing voters in polls but are often ignored or allocated to each candidate in a simple, deterministic manner. Historically this may have been adequate because the undecided were comparatively small enough to assume that they do not affect the relative proportions of the decided voters. However, in the presence of high numbers of undecided voters, these static rules may in fact bias election predictions from election poll authors and meta-poll analysts. In this paper, we examine the effect of undecided voters in the 2016 US presidential election to the previous three presidential elections. We show there were a relatively high number of undecided voters over the campaign and on election day, and that the allocation of undecided voters in this election was not consistent with two-party proportional (or even) allocations. We find evidence that static allocation regimes are inadequate for election prediction models and that probabilistic allocations may be superior. We also estimate the bias attributable to polling agencies, often referred to as "house effects".Comment: 32 pages, 9 figures, 6 table

At a time of insurgent parties, can societies believe in election polls?. The Spanish experience

The main purpose of this paper is to use the Spanish case, through an econometric analysis of 226 electoral polls, to explain why polls are making more mistakes in times of great socioeconomic slumps, political instability and the emergence of new political parties. In this context, it is the very instrument with which society tries to reduce the reigning uncertainty that, paradoxically, can ultimately drive uncertainty up. Our results show that the prediction error for the new emerging parties is significantly higher than for the traditional parties and this error is not sensitive to solutions for increasing the reliability of surveys, such as increasing sample size, transparency constantly conducting periodical surveys, the closeness of the approaching election or the survey mode that is used. It can be observed that pollsters do not want to make predictions that vary greatly from the average of the other polls. Finally, editorial bias appears to play a significant role, especially in the case of traditional parties.El principal objetivo de este artículo es explicar por qué las encuestas electorales cometen más errores en épocas de crisis económica, inestabilidad política y con partidos emergentes como Podemos y Ciudadanos. Para ello utilizamos una base de datos de 226 encuestas previas a las elecciones generales españolas de 2016. En este contexto, paradójicamente vemos como el instrumento que la sociedad utiliza para reducir su incertidumbre puede acabar aumentándola. Nuestros resultados muestran como el error de predicción de los nuevos partidos es significativamente mayor que los tradicionales e insensible a las soluciones clásicas para aumentar la precisión de las encuestas, como el tamaño de la muestra, el método de muestreo, la experiencia del encuestador, o la proximidad de la cita electoral. Además, se observa que las empresas que desarrollan las encuestas realizan de forma sistemática predicciones muy próximas a las que han realizado las encuestas recientes de sus competidores. Finalmente, el sesgo editorial parece ser una variable relevante, especialmente en lo relativo a las predicciones de los partidos tradicionale

Can electoral popularity be predicted using socially generated big data?

Today, our more-than-ever digital lives leave significant footprints in cyberspace. Large scale collections of these socially generated footprints, often known as big data, could help us to re-investigate different aspects of our social collective behaviour in a quantitative framework. In this contribution we discuss one such possibility: the monitoring and predicting of popularity dynamics of candidates and parties through the analysis of socially generated data on the web during electoral campaigns. Such data offer considerable possibility for improving our awareness of popularity dynamics. However they also suffer from significant drawbacks in terms of representativeness and generalisability. In this paper we discuss potential ways around such problems, suggesting the nature of different political systems and contexts might lend differing levels of predictive power to certain types of data source. We offer an initial exploratory test of these ideas, focussing on two data streams, Wikipedia page views and Google search queries. On the basis of this data, we present popularity dynamics from real case examples of recent elections in three different countries.Comment: To appear in Information Technolog

Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population. Here we develop a method to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to develop an in-domain training set approaching 1 million tweets. We investigate the social networks formed by the interactions among millions of Twitter users and infer the support of each user to the presidential candidates. The resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls by 10 days, showing that Twitter can be an early signal of global opinion trends. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of national polls

Lessons of Election 2000

Many people believe that Election 2000 proved only how divided the nation is over politics and policy. In contrast, this study draws six lessons from Election 2000. Congress should set up a commission to recommend changes in the electoral system; the states should have the choice of accepting the reforms and the obligation to pay for them. The Electoral College should be preserved. The framers designed the Electoral College to limit arbitrary power. Abolishing the Electoral College would weaken the states and damage federalism. The United States is a consitutional republic, not a regime based on "the will of the people." Several politicians have appealed to the will of the people in the Florida struggle. The will of the people is a concept alien to the American political tradition of limited constitutional government. Underlying public attitudes strongly supported limited government in Election 2000. Both the platforms of the candidates and public opinion polls indicate that the public's skepticism about government remains high. Campaign spending enhanced turnout and participation in Election 2000. Both the NAACP and unions spent lavishly on getting out the vote. If campaign spending is restricted, turnout will fall, contrary to the professed desire of advocates of capaign finance restrictions. Congress should not hold hearings about media mistakes. Any punishment for errors or bias by the networks on election night should be left to public opinion