scholarly journals Political Attitudes of Voters on Twitter in the Second Round of the Polish Presidential Elections 2015

2021 ◽  
Vol 7 (1) ◽  
pp. 110-123
Author(s):  
Rafał Piotr Paradowski
Keyword(s):  
Presidential Election ◽  
Presidential Elections ◽  
Semantic Analysis ◽  
Political Attitudes ◽  
Election Outcome ◽  
Word Count ◽  
Political Preferences ◽  
Election Results ◽  
Twitter Users

Abstract This study aims to answer the question of whether and how the voting attitudes of Polish Twitter users correlate with the election results. It also attempts to understand the online mechanisms of expressing political preferences. The data sample consisted of 8698 tweets attributed to 3508 users concerning attitudes towards the two candidates in the second round of the 2015 presidential election in Poland. Research included semantic analysis and word count techniques. Both approaches yielded similar results and were extremely close to the official post-election outcome – smallest offset amounted to less than 0.1. Moreover, experimental exploration of tweets, users’ behaviour, interactions and dynamics of tweet activity was conducted.

Nature of Tweets in the 2015 Nigerian Presidential Elections

2015 ◽  
Vol 2 (2) ◽  
pp. 34-52 ◽  
Author(s):  
Nwachukwu Andrew Egbunike ◽  
Noel Ihebuzor ◽  
Ngozi Onyechi
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Presidential Election ◽  
Social Engagement ◽  
Presidential Elections ◽  
Hate Speech ◽  
Political Mobilization ◽  
Election Results

Social media is becoming increasingly important as a means for social engagement. In Nigeria, Twitter is employed to convey opinion and make commentary on matters ranging from football to politics. Tweets are also used to inform, advocate, recruit and even incite. Previous studies have shown that Twitter could be effective for political mobilization. However, there is dearth of research on how Twitter has been used as a purveyor of neutral and/or hate speech in the Nigerian context. This study examined the nature of tweets in the immediate aftermath of the 2015 presidential election in Nigeria. The authors employed content analysis of 250 purposively selected tweets from the #Igbo hashtag which trended between March 29 and 31, 2015. The tweets were then categorized into five explicit hate and one neutral tweet category respectively. Results revealed the dominance of three hate tweet types: derogatory, mocking and blaming. These findings were then discussed bearing in mind earlier theories on the functionality of tweets and voting patterns from an analysis of the election results.

scholarly journals Fake News on Twitter in 2016 U.S. Presidential Election: A Quantitative Approach

2020 ◽  
Vol 3 (2) ◽  
pp. 26
Author(s):  
Karmvir Padda
Keyword(s):  
Cluster Analysis ◽  
Presidential Election ◽  
Quantitative Approach ◽  
Fake News ◽  
Word Count ◽  
Analytical Thinking ◽  
News Organizations ◽  
The World ◽  
Linguistic Inquiry ◽  
Election Results

The flow of misinformation and disinformation around the 2016 U.S. presidential election put the problem of “fake news” on the agenda all over the world. As a result, news organizations and companies have taken measures to reduce or eliminate the production and dissemination of fake news. Linguistic Inquiry and Word Count (LIWC) software was employed in the current study to examine 1,500 randomly selected tweets that were used to influence the 2016 U.S. presidential election. Results showed fake news are less likely to have analytical thinking. Moreover, both alt-Right troll accounts and alt-Left troll accounts posted fake news on Twitter. Lastly, Cluster analysis revealed that the fake news tweets are more likely to be retweeted and use fewer analytical thinking. APA Citation Padda, K. (2020). Fake news on Twitter in 2016 U.S. presidential election: a quantitative approach. The Journal of Intelligence, Conflict, and Warfare, 3(2), 18-45. https://journals.lib.sfu.ca/index.php/jicw/article/view/2374/1810.

scholarly journals A Bayesian Approach for Validation of Election Results

2020 ◽  
Vol 27 (2) ◽  
pp. 22-31
Author(s):  
R.K. Ogundeji ◽  
J.N. Onyeka-Ubaka
Keyword(s):  
Political Parties ◽  
Bayesian Approach ◽  
Bayesian Methods ◽  
Presidential Election ◽  
Presidential Elections ◽  
Empirical Bayes ◽  
Posterior Probability ◽  
Election Process ◽  
Credible Intervals ◽  
Election Results

Election process and results in many countries have resulted in both political and economic instability of that country. Fair and credible election process and results must be evidence-based and statistical proven. This study employed a Bayesian procedure for the validation of election results. Based on Nigerian 2011 and 2015 presidential election results, Bayesian credible intervals were obtained to assess the credibility of Nigeria presidential election results. The study explores Bayesian methods using a Bayesian model called beta-binomial conjugate model to compute posterior probability of electoral votes cast and confirm if these votes are within Bayesian credible intervals. The results obtained showed that election outcomes for the two major political parties in Nigeria 2011 presidential election are not within Bayesian credible bounds while 2015 presidential election results are within computed Bayesian credible bounds. Also, in contrast to frequentist approach, applied Bayesian methodology exhibited smaller variance which is an indication that Bayesian approach is more efficient. Thus, for election to be fair, credible and acceptable by the electorates, Bayesian approach can be used to validate electoral process and results. Keywords: Bayesian Methods, Bayesian Credible Intervals, Beta-Binomial Model, Empirical Bayes, Nigeria Presidential Elections.

scholarly journals Inferring the votes in a new political landscape. The case of the 2019 Spanish Presidential elections.

2020 ◽  
Author(s):  
Didier Grimaldi ◽  
Javier Diaz ◽  
Hugo Arboleda
Keyword(s):  
Presidential Election ◽  
Online Social Networks ◽  
Presidential Elections ◽  
Research Topic ◽  
Machine Learning Algorithms ◽  
The Past ◽  
Political Landscape ◽  
Research Institutes ◽  
Twitter Data ◽  
Election Results

Abstract The avalanche of personal and social data circulating in Online Social Networks over the past 10 years has attracted a great deal of interest from Scholars and Practitioners who seek to analyse not only their value, but also their limits. Predicting election results using Twitter data is an example of how data can directly influence the politic domain and it also serves an appealing research topic. This article aims to predict the results of the 2019 Spanish Presidential election and the voting share of each candidate, using Tweeter. The method combines sentiment analysis and volume information and compares the performance of five Machine Learning algorithms. Several data scrutiny uncertainties arose that hindered the prediction of the outcome. Consequently, the method develops a political lexicon-based framework to measure the sentiments of online users. Indeed, an accurate understanding of the contextual content of the tweets posted was vital in this work. Our results correctly ranked the candidates and determined the winner by means of a better prediction of votes than official research institutes.

scholarly journals Inferring the votes in a new political landscape. The case of the 2019 Spanish Presidential elections.

2020 ◽  
Author(s):  
Didier Grimaldi ◽  
Javier Diaz ◽  
Hugo Arboleda
Keyword(s):  
Presidential Election ◽  
Online Social Networks ◽  
Presidential Elections ◽  
Research Topic ◽  
Machine Learning Algorithms ◽  
The Past ◽  
Political Landscape ◽  
Research Institutes ◽  
Twitter Data ◽  
Election Results

Abstract The avalanche of personal and social data circulating in Online Social Networks over the past 10 years has attracted a great deal of interest from Scholars and Practitioners who seek to analyse not only their value, but also their limits. Predicting election results using Twitter data is an example of how data can directly influence the politic domain and it also serves an appealing research topic. This article aims to predict the results of the 2019 Spanish Presidential election and the voting share of each candidate, using Tweeter. The method combines sentiment analysis and volume information and compares the performance of five Machine Learning algorithms. Several data scrutiny uncertainties arose that hindered the prediction of the outcome. Consequently, the method develops a political lexicon-based framework to measure the sentiments of online users. Indeed, an accurate understanding of the contextual content of the tweets posted was vital in this work. Our results correctly ranked the candidates and determined the winner by means of a better prediction of votes than official research institutes.

scholarly journals Inferring the votes in a new political landscape. The case of the 2019 Spanish Presidential elections.

2020 ◽  
Author(s):  
Didier Grimaldi ◽  
Javier Diaz ◽  
Hugo Arboleda
Keyword(s):  
Presidential Election ◽  
Online Social Networks ◽  
Presidential Elections ◽  
Research Topic ◽  
Machine Learning Algorithms ◽  
The Past ◽  
Political Landscape ◽  
Research Institutes ◽  
Twitter Data ◽  
Election Results

Abstract The avalanche of personal and social data circulating in Online Social Networks over the past 10 years has attracted a great deal of interest from Scholars and Practitioners who seek to analyse not only their value, but also their limits. Predicting election results using Twitter data is an example of how data can directly influence the politic domain and it also serves an appealing research topic. This article aims to predict the results of the 2019 Spanish Presidential election and the voting share of each candidate, using Tweeter. The method combines sentiment analysis and volume information and compares the performance of five Machine Learning algorithms. Several data scrutiny uncertainties arose that hindered the prediction of the outcome. Consequently, the method develops a political lexicon-based framework to measure the sentiments of online users. Indeed, an accurate understanding of the contextual content of the tweets posted was vital in this work. Our results correctly ranked the candidates and determined the winner by means of a better prediction of votes than official research institutes.

scholarly journals Brazilian Presidential Elections: Analysing Voting Patterns in Time and Space Using a Simple Data Science Pipeline

2020 ◽  
Author(s):  
Lucas Henrique Mantovani Jacintho ◽  
Tiago Pinho Da Silva ◽  
Antonio Rafael Sabino Parmezan ◽  
Gustavo Enrique de Almeida Prado Alves Batista
Keyword(s):  
Presidential Election ◽  
Voting Behavior ◽  
Presidential Elections ◽  
Data Science ◽  
First Year ◽  
Behavior Understanding ◽  
Analysis Process ◽  
Short And Long Term ◽  
Election Results ◽  
Political Economic

Since 1989, the first year of the democratic presidential election after a long period of a dictatorship regime, Brazil conducted eight presidential elections. This period was marked by short and long-term shifts of power and two impeachment processes. Such instability is a case of study in electoral studies, e.g., the study of the population voting behavior. Understanding patterns in the population behavior can give us insight into factors and influences that affect the quality of democratic political decisions. In light of this, our paper focuses on analyzing the Brazilian presidential election voting behavior across the years and the Brazilian territory. Following a data science pipeline, we divided the analysis process into five steps: (i) data selection; (ii) data preprocessing; (iii) identification of spatial patterns, in which we seek to understand the role of space in the election results using spatial autocorrelation techniques; (iv) identification of temporal patterns, where we investigate similar trends of votes over the years using a hierarchical clustering method; and (v) evaluation of the results. It is noteworthy that the data in this work represents the election results at the municipal level, from 1994 to 2018, of the two most relevant parties of this period: the Brazilian Social Democracy Party (PSDB) and the Workers’ Party (PT). Through the results obtained, we found the existence of spatial dependence in every electoral year investigated. Moreover, despite the changes in the political-economic context over the years, neighboring cities seem to present similar voting behavior trends.

scholarly journals Simultaneous Elections and the Rise of Female Representation in Indonesia

2021 ◽  
Vol 40 (1) ◽  
pp. 50-72
Author(s):  
Luky Sandra Amalia ◽  
Aisah Putri Budiatri ◽  
Mouliza KD. Sweinstani ◽  
Atika Nur Kusumaningtyas ◽  
Esty Ekawati
Keyword(s):  
Presidential Elections ◽  
Institutional Innovation ◽  
Presidential Candidate ◽  
Female Representation ◽  
Legislative Elections ◽  
Women Candidates ◽  
Presidential Candidates ◽  
Female Candidates ◽  
Election Results ◽  
Legislative Election

In the 2019 election, the proportion of women elected to Indonesia’s People’s Representative Assembly ( Dewan Perwakilan Rakyat, DPR) increased significantly to almost 21 per cent. In this article, we ask whether an institutional innovation – the introduction of simultaneous presidential and legislative elections – contributed to this change. We examine the election results, demonstrating that, overall, women candidates did particularly well in provinces where the presidential candidate nominated by their party won a majority of the vote. Having established quantitatively a connection between results of the presidential elections and outcomes for women legislative candidates, we turn to our qualitative findings to seek a mechanism explaining this outcome. We argue that the simultaneous elections helped women candidates by easing their access to voters who supported one of the presidential candidates, but who were undecided on the legislative election. Rather than imposing additional burdens on female candidates, simultaneous elections assisted them.

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