Election Prediction Research Articles

여론조사에서 무응답 대체를 통한 선거 결과 예측

In the results of the election polls, the candidates' approval ratings are not only a major concern of the people, but also an important factor in establishing the election strategy of the party concerned. However, if there is an inaccurate or distorted result of the election poll, it can cause a big problem because of its influence, and the controversy about the poll often arises. There are many cases in which the actual election results are different from the results of the polls, and political parties and research institutes, which are related to the polls, are making efforts to improve the accuracy of the election forecasts. The purpose of this study is to improve the accuracy of election prediction by replacing non-response through machine learning algorithm using the survey data of the 20th Presidential Election, the 2021 Busan Mayor By-election, and the 8th National Simultaneous Local Election (Busan Metropolitan City, Nam-gu, Gimhae Mayor). For this purpose, first, the data excluding the non-response (I do not know/don't know) was separated into train data and the data selected as non-response (I do not know/don't know) was separated into test data. Second, Random Forest, XGBoost, and LightGBM methods were applied, and only the top three questions were used by deriving the importance of questions when predicting candidates through train data. Third, through the model learned based on the train data, the non-response of the test data was replaced with the response of one candidate, and the corresponding data and the train data were combined to calculate the percentage of votes for each candidate. As a result of comparing the actual election results with the percentage of votes for each candidate by replacing the non-response, it was confirmed that the Random Forest method is suitable for the model for improving the accuracy of election prediction through non-response substitution.

Calling “Gevald”: on the emergence of negative election forecasts in partisan communications

AbstractIndividuals were found to anonymously predict positive election outcomes for their preferred candidate. Yet, there is little scientific knowledge about election predictions made in the context of same-camp political communications (i.e., partisan communications) that are presumably meant to encourage other supporters. In five studies of low-information elections and a study of hypothetical U.S. elections (n = 1889), we found that people tended to communicate favorable forecasts to others sharing their view, compared to the neutral point and to the actual election outcomes. On the other hand, negative framing reduced the positivity of forecasts in these communications to the extent that it led most participants to predict an election loss. This occurred in response to a single addressee acting discordantly and even more strongly when the election results were phrased as a drop. When both positive and negative framing options were available, this still negativity affected participants’ predictions even though only a minority selected the negative framing option. Thus, people tend to make optimistic election predictions in partisan communications, but this pattern is easily manipulable given subtle changes in the forecasting prompt, either by negative framing or selectable positive and negative framing.

ELECTION RESULT PREDICTION

This study utilizes Text Blob for sentiment analysis of social media and news data to predict election results. By analyzing sentiment polarity, a comprehensive understanding of public opinion towards political entities is obtained. Machine learning algorithms are employed to build predictive models using historical election data. Results demonstrate the effectiveness of sentiment analysis in enhancing prediction accuracy compared to traditional methods. This research has implications for political campaigns and policymakers in gauging public sentiment and anticipating electoral outcomes. Keywords— Text Blob, sentiment analysis, election prediction, social media, machine learning.

The Role of Social Media Platforms in Forecasting Elections: A Comparison of Twitter and Facebook

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 paper investigates the role of two different social media platforms, Twitter 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.

Critical Review of Stack Ensemble Classifier for the Prediction of Young Adults’ Voting Patterns Based on Parents’ Political Affiliations

Aim/Purpose: This review paper aims to unveil some underlying machine-learning classification algorithms used for political election predictions and how stack ensembles have been explored. Additionally, it examines the types of datasets available to researchers and presents the results they have achieved. Background: Predicting the outcomes of presidential elections has always been a significant aspect of political systems in numerous countries. Analysts and researchers examining political elections rely on existing datasets from various sources, including tweets, Facebook posts, and so forth to forecast future elections. However, these data sources often struggle to establish a direct correlation between voters and their voting patterns, primarily due to the manual nature of the voting process. Numerous factors influence election outcomes, including ethnicity, voter incentives, and campaign messages. The voting patterns of successors in regions of countries remain uncertain, and the reasons behind such patterns remain ambiguous. Methodology: The study examined a collection of articles obtained from Google Scholar, through search, focusing on the use of ensemble classifiers and machine learning classifiers and their application in predicting political elections through machine learning algorithms. Some specific keywords for the search include “ensemble classifier,” “political election prediction,” and “machine learning”, “stack ensemble”. Contribution: The study provides a broad and deep review of political election predictions through the use of machine learning algorithms and summarizes the major source of the dataset in the said analysis. Findings: Single classifiers have featured greatly in political election predictions, though ensemble classifiers have been used and have proven potent use in the said field is rather low. Recommendation for Researchers: The efficacy of stack classification algorithms can play a significant role in machine learning classification when modelled tactfully and is efficient in handling labelled datasets. however, runtime becomes a hindrance when the dataset grows larger with the increased number of base classifiers forming the stack. Future Research: There is the need to ensure a more comprehensive analysis, alternative data sources rather than depending largely on tweets, and explore ensemble machine learning classifiers in predicting political elections. Also, ensemble classification algorithms have indeed demonstrated superior performance when carefully chosen and combined.

PollCast India 2024: Harnessing Data for Accurate Election Predictions

PollCast India 2024: Harnessing Data for Accurate Election Predictions

Natural Language Processing for Prediction of Election Results on Twitter Engagement and Polls

With the ability to predict political outcomes and provide insights into public opinion, using Twitter data to predict election results has gained popularity. Twitter offers a massive supply of data for analysis due to its enormous user base and real-time nature. Researchers use sentiment analysis tools to categorize tweets as good, harmful, or neutral and follow sentiment patterns over time. Network analysis finds influential users and digs deeper into the dynamics of political discourse. The accuracy of predictions is improved by combining traditional polling data with machine learning methods. Twitter data analysis has the potential to offer insightful information for election campaigns and improve political strategies despite issues like representativeness and identifying genuine sentiment. Ongoing research focuses on refining methodologies and addressing limitations, advancing the reliability of election prediction using Twitter data. The paper shows the results of election prediction for Indian political parties based on Twitter data

On the frontiers of Twitter data and sentiment analysis in election prediction: a review.

Election prediction using sentiment analysis is a rapidly growing field that utilizes natural language processing and machine learning techniques to predict the outcome of political elections by analyzing the sentiment of online conversations and news articles. Sentiment analysis, or opinion mining, involves using text analysis to identify and extract subjective information from text data sources. In the context of election prediction, sentiment analysis can be used to gauge public opinion and predict the likely winner of an election. Significant progress has been made in election prediction in the last two decades. Yet, it becomes easier to have its comprehensive view if it has been appropriately classified approach-wise, citation-wise, and technology-wise. The main objective of this article is to examine and consolidate the progress made in research about election prediction using Twitter data. The aim is to provide a comprehensive overview of the current state-of-the-art practices in this field while identifying potential avenues for further research and exploration.

Election predictions in the news: how users perceive and respond to visual election forecasts

ABSTRACT 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.

Correction to: Application of Twitter sentiment analysis in election prediction: a case study of 2019 Indian general election

Correction to: Application of Twitter sentiment analysis in election prediction: a case study of 2019 Indian general election

Application of Twitter sentiment analysis in election prediction: a case study of 2019 Indian general election

Application of Twitter sentiment analysis in election prediction: a case study of 2019 Indian general election

A Deep Learning Framework for News Readers' Emotion Prediction Based on Features From News Article and Pseudo Comments.

With the rapid development of the Internet, readers tend to share their views and emotions about news events. Predicting these emotions provides a vital role in social media applications (e.g., sentiment retrieval, opinion summary, and election prediction). However, news articles usually consist of objective texts that lack emotion words, making emotion prediction challenging. From prior studies, we know that comments that come directly from readers are full of emotions. Therefore, in this article, we propose a deep learning framework that first merges article and comment information to predict readers' emotions. At the same time, in the prediction process, we design a pseudo comment representation for unpublished news articles by the comments of published news. In addition, a better model is required to encode articles that contain implicit emotions. To solve this problem, we propose a block emotion attention network (BEAN) to encode news articles better. It includes an emotion attention mechanism and a hierarchical structure to capture emotion words and generate structural information during encoding. Experiments performed on three public datasets show that BEAN achieves the state-of-the-art average Pearson (AP) and accuracy (Acc@1). Moreover, results on four self-collected datasets show that both the introduction of emotional comments and BEAN in our framework improve the ability to predict readers' emotions.

Prediction of the General Election 2024 Result in Pakistan: Using Markov Chain

Prediction of the General Election 2024 Result in Pakistan: Using Markov Chain

Election Prediction Based on Messages Feature Analysis in Twitter Social Network

Election Prediction Based on Messages Feature Analysis in Twitter Social Network

From Social Media to Ballot Box: Leveraging Location-Aware Sentiment Analysis for Election Predictions

From Social Media to Ballot Box: Leveraging Location-Aware Sentiment Analysis for Election Predictions

News Stance Discrimination Based on a Heterogeneous Network of Social Background Information Fusion

Media with partisan tendencies publish news articles to support their preferred political parties to guide the direction of public opinion. Therefore, discovering political bias in news texts has important practical significance for national election prediction and public opinion management. Some biased news often has obscure expressions and ambiguous writing styles. By bypassing the language model, the accuracy of methods that rely on news semantic information for position discrimination is low. This manuscript proposes a news standpoint discrimination method based on social background information fusion heterogeneous network. This method expands the judgment ability of creators and topics on news standpoints from external information and fine-grained topics based on news semantics. Multi-attribute features of nodes enrich the feature representation of nodes, and joint representation of heterogeneous networks can reduce the dependence of position discrimination on the news semantic information. To effectively deal with the position discrimination of new news, the design of a multi-attribute fusion heterogeneous network is extended to inductive learning, avoiding the cost of model training caused by recomposition. Based on the Allsides dataset, this manuscript expands the information of its creator's social background and compares the model for discriminating political positions based on news content. In the experiment, the best transductive attribute fusion heterogeneous network achieved an accuracy of 92.24% and a macro F1 value of 92.05%. The effect is improved based purely on semantic information for position discrimination, which proves the effectiveness of the model design.

FORECASTING THE RESULTS OF THE PRESIDENTIAL ELECTIONS IN FRANCE BASED ON TWITTER DATA

This paper presents the study to collect, store and analyze data from Twitter to forecast French presidential election results, compared to sociological polls. The first and probably the most important step of the research is to collect, store and clean data, the whole result depends on the amount and quality of data. In the next step of research, datasets are analyzed. Lastly, complete report and visualizations are provided. In the study, we propose modern technics, mathematical algorithms, and machine learning approaches to analyze big amounts of data from the Twitter social network in order to forecast the 2022 French presidential election results. The determined outcome is compared with sociological polls and the real results of elections.
 In the conducted research modern types of media are compared to select the best one for election prediction. Selected Twitter social network as the one with the most appropriate data and availability to download big amounts of useful information. The approach based on the usage of Python programming language, Selenium browser emulation and MongoDB database was used to collect, store and clean data about the main French election candidates – Emmanuel Macron and Marine Le Pen. The research was made from August 2021 until the election itself in April 2022. The determined outcome is compared with sociological polls and the results of elections and showed that analysis of social network data could be a good alternative to traditional sociological polls as it shows the same trends month by month and well predicted the win of Emmanuel Macron in elections. Moreover, the proposed approach has its benefits compared to sociological polls such as always being fresh, and close to real-time information, the price of research is much lower and could be reused for the next parliamentary or presidential elections with a small modification.
 The research could be extended and adapted for other countries. Currently, the proposed algorithms and mathematical models showed good results in the French and Ukraine elections. It works well with English, French, Ukrainian and Russian languages. This allows us to claim that it will also work fine with other Latin or Cyrillic alphabets but for Asian or Arabic languages more research would be needed. Twitter is a good choice for European and American countries. In the future, other social networks should be considered for the countries in which it is not so popular.

Aggregate earnings and market expectations in United States presidential election prediction markets

This study uses election futures market data to provide the first empirical evidence that aggregate earnings conveys timely “election-relevant” information effecting betting market participants' expectations about the likely outcomes of United States presidential election campaigns. I document that aggregate earnings news is associated with multiple facets of U.S. economic health affecting voter utility. I then use high-frequency data from the Iowa Electronic Political Prediction Market (IEM) to document that aggregate earnings news, including cash flow news, is significantly related to changes in the expected outcomes of U.S. presidential elections and incremental of other measures of economic health.

The Degenerative Tendencies of Long‐Serving Governments…1963…1996…2009…the Conservatives in 2022?

AbstractIs Boris Johnson's government suffering from the degenerative tendencies of long‐serving government as identified by Timothy Heppell? For his 2008 study, Heppell identified five symptoms of governing degeneration, namely: governing competence; leadership credibility; ideological division; abuses of power; and a renewed opposition/sense that it is time for a change. This approach was tested by Heppell on the longstanding Conservative administrations of 1951–64 and 1979–97, and the Labour administration between 1997 and (at the time of original publication) 2008. Thus, my hypothesis asks whether the similarly long‐serving Conservative government from 2010 to‐date might be exhibiting signs of similar governing degeneration on each of Heppell's criteria. I ask whether the Conservatives under Boris Johnson appear to lack the clear direction of travel and purpose that characterised the final period of the Macmillan/Home, Major, and Brown premierships. Whilst it is by no means the aim of this article to produce an electoral prediction, there is evidence to suggest the current Conservative administration has met the level of governing degeneration that affected the last years of the longstanding administrations evaluated by Heppell.

Asking about Social Circles Improves Election Predictions Even with Many Political Parties

Abstract Traditionally, election polls have asked for participants’ own voting intentions. In four elections, we previously found that we could improve predictions by asking participants how they thought their social circles would vote. A potential concern is that the social-circle question might predict results less well in elections with larger numbers of political options because it becomes harder to accurately track how social contacts plan to vote. However, we now find that the social-circle question performs better than the own-intention question in predicting two elections with many political parties: The Netherlands’ 2017 general election and the Swedish 2018 general election.