Predicting Election Results Research Articles

Election Voting Trend Prediction System

This paper presents the use of Bayesian networks and K-Nearest Neighbor algorithms for predicting election results. Our motivation stemmed from the complexities of the election data available, which spans over 120,000 voting locations across 36 states in Nigeria, and the requirement to develop a procedure that takes into consideration voter trends that are influenced by political parties seeking to win. The system architecture's translation was utilised, and the prototyping methodology was adopted. In order to realize the requirements, the system was designed and implemented using Java and MySQL in accordance with specifications. Since the outcome is positive, it can serve as a benchmark for further study in this field, particularly when it comes to using data mining tools to analyze election results.

DECODING THE BALLOT: PREDICTING INDIAN GENERAL ELECTIONS WITH MACHINE LEARNING

This paper explores the complexities of predicting election outcomes in India, focusing on the winning party and the probability of incumbent reelection. Leveraging historical voting data and socio-economic indicators from the Socioeconomic High-resolution Rural-Urban Geographic (SHRUG) dataset and the Lok Dhaba database, the study employs advanced machine learning models to forecast electoral results. The main goal of this paper is to find these models ability to forecast the victorious party and determine the likelihood of reelection is the main goal. Several models, including Random Forest, Gradient Boosting, and Decision Tree, were assessed to meet these goals. With an accuracy of 99.89%, the Random Forest model outperformed the rest of them. This is because of its ensemble learning strategy, which lowers overfitting and increases predictive power. Additionally successful were the Decision Tree and Gradient Boosting models, which yielded accuracies of 98.75% and 99.78%, respectively. The study faced challenges such as computational complexity and potential bias introduced by the dataset, particularly due to the historical dominance of the Indian National Congress (INC) party. Despite these challenges, the models provided valuable insights into voter behaviour and electoral trends. The implications of this study are significant for political analysts and campaign strategists. Accurate predictions can guide the development of targeted campaign strategies and enhance understanding of electoral dynamics. Future research should address dataset biases and explore more efficient algorithms to improve the robustness and applicability of these predictions in real-world scenarios.

Social Media, Sentiments and Political Discourse ? An Exploratory Study of the 2021 Canadian Federal Election

Social media are widely used for online political discourse. Opinions shared on social media have different sentiments associated with them. Given the very high adoption rates of Twitter (now X) among adults, those who share their opinions on Twitter (X) not only represent a sizable segment of the society, but also influence (through emotion contagion) an even larger segment who are passive (non-contributing) users of the platform. Further, the discourse that is initiated on Twitter (X) typically spreads to other more traditional media. As a result, Twitter (X) is influential, which makes it useful to understand the factors related to the sentiments expressed in tweets. Such understanding can help policymakers to take actions that align with public needs and priorities. This research focuses on identifying the drivers (keywords) of sentiments associated with political discourse on Twitter (X). We also explore virality, i.e., how much a message (the tweet) spreads, and the relationship between sentiments and virality. Finally, we explore whether the clustering of tweets among sentiment and virality groups can improve the potential of social media content for predicting election results. Sentiment Analysis of 764,000 tweets related to the 2021 Canadian Federal election was followed by text clustering to identify sentiment-driving topics. We found some keywords predominantly present within a positive or negative sentiment that are suggestive of entities or ideas to invest in or mitigate by political decision makers. We were also able to find partial evidence for “negativity bias” by detecting a negative relationship between sentiment (positivity) and virality (number of retweets). Finally, we demonstrated that high positivity on the political discourse does not reflect election outcomes and examining Twitter (X) content in more neutral groups can improve predictive power. Our findings have implications for political decision makers and social media analytics researchers.

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.

AI in International Politics

Abstract: Over the past ten years, artificial intelligence (AI) has significantly impacted many industries,including politics. A growing number of people are turning to artificial intelligence (AI) in politics because it offers a number of advantages that can speed up decision-making and enhance governance. Data analysis is one of the primary ways AI is being applied in politics. Data analytics is being used by governments and political parties to understand vote trends and public opinion. They canuse this to create more effective campaigns and policies that are suited to the requirements ofvarious constituencies. Additionally, AI is being used to increase the effectiveness of governmental operations. Artificialintelligence-powered chatbots and virtual assistants are being utilised to deliver information to users about various government services and programmes. Consequently, less work will need to be done by government employees, and citizens will receive information more quickly and accurately. Predicting election results is another area in politics where AI is having an impact. In order to anticipate the outcome of elections, AI algorithms are being employed to analyse a variety of elements, including polling data and social media activity. This can aid candidates and political parties in concentrating their efforts on the groups most likely to decide the election. However, the use of AI in politics also raises many concerns. One concern is the use of AI could lead to the manipulation of public opinion. There are concerns that AI algorithms could be used to create fake news or spread disinformation to influence voters.

Can Latin American Voters see the Future?

The present study examined whether Argentinian citizens could predict election results at the sub-national level. We targeted Argentinian Twitter users in seven provinces with polls using Twitter Ads. Argentinian Twitter users constitute a high-ability subgroup that possesses several characteristics that enhance citizen forecasting competence. The polls asked citizens to predict what party would win the first round of the upcoming presidential election in their province. We present a preliminary citizen forecast of the first round of the 2023 Argentinian presidential election. The forecast demonstrates three preliminary findings. First, citizens expect a competitive election in their respective provinces. Second, citizens in almost all the provinces expect an opposition victory. Finally, a high degree of uncertainty surrounds these predictions, with no party obtaining a greater than 50 percent probability of winning in any of the provinces.

Proposed Machine learning model for predicting Egyptian Parliament Election Results

Political life and election have become one of the most important comments on social media sites. Governments have shown a keen interest in predicting the results of elections, whether presidential or parliamentary. The purpose of this study is to predict the results of the Egyptian Parliament elections using sentiment analysis, specifically Support Vector Machines (SVM), Naive Bayes, Decision Trees, and Random Forests in the context of machine learning. In this study, a sentiment analysis approach is employed to analyze public sentiment towards political parties and candidates leading up to Parliament elections. The sentiment analysis techniques are utilized to classify sentiment from textual data collected from Tweeter; Data were obtained in November 2020 before and during election days. The study utilizes a machine learning framework to train and test the models using a labeled dataset of sentiment-labeled political texts. The findings of this study reveal that sentiment analysis using machine learning can effectively predict the results of Parliament elections. The accuracy and performance of each technique are evaluated and compared to determine the most accurate and reliable predictor of election outcomes. This study contributes to the existing literature by applying sentiment analysis techniques to predict Parliament election results. The use of machine learning algorithms in combination with sentiment analysis, offers a novel approach to election forecasting. The findings of this study can be valuable for political analysts, election strategists, and policymakers seeking to understand public sentiment and predict election outcomes accurately.

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.

Use of ridge calibration method in predicting election results

Use of ridge calibration method in predicting election results

Harnessing Twitter: Sentiment Analysis for Predicting Election Outcomes in India

This paper explores the potential of Twitter, a popular social media platform, as a tool for predicting election outcomes. Sentiment analysis has emerged as a powerful tool for predicting election outcomes, with numerous studies showcasing its effectiveness in various countries. For instance, research has utilized sentiment analysis to forecast election results in nations like the USA, India, Pakistan and other countries, demonstrating the utility of social media data in gauging public opinion and predicting electoral results [1]. Elections in India are always considered important events that most people look forward to the rapid growth of social media in the past has provided end users with powerful tools to share their ideas. Twitter, which is one such platform, provides daily updates on political events through various hashtags and trends. People react to political events and give their opinions. Our approach is to collect tweets from top political parties contesting the Gujarat Assembly Elections 2022, and then calculate sentiment scores. The database includes a variety of recent and well-liked tweets about a specific political party. Party tweets are generated with specific keywords like “BJP”, “AAP”,“Congress” and so on. In the context of India, Twitter sentiment analysis tools and classification have been used to predict the outcomes of state assembly elections, underscoring the potential of social media data in forecasting electoral results within the country [2]. We used standard machine learning algorithms like VADER sentiment analyzer on Random Forest and Decision Tree for our classification and testing data to classify tweets as positive and negative. As a result, this work uses sentiment analysis to evaluate tweets gathered from Twitter and forecast election outcomes. This work shows the growing influence of social media on politics and the feasibility of using such platforms for predictive analysis. The findings of this study could provide valuable insights for political parties, policymakers, and researchers interested in the intersection of social media and politics. Random Forest and Decision Tree models performed well in predicting election outcomes based on sentiment analysis on Twitter data with 89% and 86% respectively.

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

Effects of masculinity vs. femininity on competence judgement of politician faces and election outcome prediction

First impressions of politician faces can be effective in predicting election outcomes, based on perceived competence from candidate photographs. However, it remains unclear whether such effects arose from facial features or other non-facial information present in the photographs (e.g. hairstyles, clothes, or poses). In four pre-registered studies, participants completed two tasks in a counter-balanced order: rating competence of individually presented faces and predicting election outcome of each pair of winner and runner-up faces. We examined competence judgment and election outcome prediction on faces from male politicians depicted on original portraits (Experiment 1), or on computer-generated faces with facial features extracted from the portraits (Experiment 2). The faces were then either masculinized or feminized (Experiments 3 and 4). We found that competence ratings were significantly higher for winners than runners-up and that winners were more likely predicted to win the elections than runners-up in all but Experiment 4, where faces of the winners were feminized and faces of the runners-up were masculinized. Regardless of facial feature changes, correlations were found between competence ratings and election outcome prediction. These findings suggest that facial features are critical for evaluating competence and predicting election outcome, and that masculine features may enhance stereotypical leadership impressions.

Predicting Election Results Via Social Media: A Case Study for 2018 Turkish Presidential Election

Social media platforms provide massive amounts of data that can be used to analyze social issues and forecast events in the future. However, it is a challenging task due to the biased and noisy nature of the data. In this work, we propose a method to predict election results via Twitter. In particular, we first detect the stance of social media accounts using their retweets. Subsequently, we develop four different counting methods for our prediction task. In the simple user counting (SC) method, we count labeled users without taking any further steps to reduce bias. In the city-based weighted counting (CBWC) method, we apply a weighted counting based on the number of electorate in each city. The closest-city-based prediction (CCBP) method utilizes sociological similarity between cities to predict results for cities with limited sample sizes. The using former election results (UFERs) method compares predictions for each city against former election results to detect data bias and uses them accordingly. We evaluate our proposed methods with the data collected for the presidential election of Turkey held in 2018. In our extensive evaluation, we show that utilizing domain-specific information and location-based weighted counting is effective in reducing bias. CBWC, CCBP, and UFER methods outperform tweet-counting-based baseline methods. Furthermore, UFER and CCBP outperform almost all traditional polls, suggesting that social media platforms are alternative mediums for conducting election polls.

On the Complexity of Predicting Election Outcomes and Estimating Their Robustness

On the Complexity of Predicting Election Outcomes and Estimating Their Robustness

Теория моральных оснований: современный взгляд на психологические факторы политических убеждений

<p style="text-align: justify;"><strong>Objective. </strong>The research is aimed at analyzing of the main achievements of Moral Foundations Theory in the study of problems of political psychology, as well as at reviewing its criticism and limitations.<br><strong>Background. </strong>In recent years, the problem of the psychological origins of political orientations and views has attracted considerable attention of researchers, due to the practical significance of predicting election results and preventing the manipulation of voters' opinions in democratic countries. One of the most popular and productive modern approaches explaining the psychological factors of political orientations is Moral Foundations Theory. The article discusses the key ideas of this theory in the studies of problems of political psychology, describes the methods developed and the results achieved. Special attention is paid to criticism of Moral Foundations Theory and empirical studies demonstrating its weaknesses and limitations.<br><strong>Conclusions. </strong>The studies show that conservative or liberal political orientations, and right or left views are associated with moral foundations, but these facts are found predominantly in Western countries. This conclusion is well supported by the results of Moral Foundations Questionnaire, however, the assessment of moral foundations using content analysis shows significantly lower correlations. Moral foundations also play an important role for intergroup relations, demonstrating associations with tolerance towards various groups and support for a hard or peaceful foreign policy. The prospects for further research within this approach include the improvement of instruments, the expansion of research in different cultures and a more careful consideration of the context in which the consequences of moral foundations are investigated.</p>

Towards Election Forecasting Using Sentiment Analysis: The Zambia General Elections 2021

Forecasting of election results is one of the key activities prior to elections. In Zambia, like many other countries, opinion polls have been used to predict the outcome of elections since 1999. During the run up to the 2021 general elections, two opinion polls were conducted. One poll suggested that HH would emerge victorious whilst the other predicted that ECL would emerge victorious. Actual results announced on the 16th of August 2021 by the Electoral Commission of Zambia (ECZ) had HH obtaining 59.02% of the votes. The variance in the two opinion polls leaves room for alternative approaches to predicting election results. This study proposes sentiment analysis as part of the initial stage to building an alternative solution to predicting the outcome of an election. The study analysed sentiments shared on social media during the build up to the August 2021 general elections. A total of 3,519 tweets were scrapped from Twitter and sentiment analysis was performed on the tweets. Topic modeling was subsequently also performed on the tweets using BERTopic. The findings of the study reveal that as election day drew closer, there was an exponential increase in the number that were posted on a daily basis. Some of the topics included voter engagement and education, the shutdown of the internet and the election day.

Machine learning for predicting elections in Latin America based on social media engagement and polls

The popularity of contemporary social media (SM) has impacted democratic practices, and the success of presidential campaigns is frequently attributed to SM performance. Within this new scenario, many methodological proposals that use SM data have been put forward for predicting election results. However, the most common approach, based on the volume and sentiment analysis of mentions on Twitter, has been frequently criticized and challenged. Thus, recent surveys have indicated new directions, such as the use of data from more than one SM platform, the adoption of nonlinear machine learning (ML) models, and the validation of methodologies and experiments in different elections. In this context, the present paper proposes SoMEN, the Social Media framework for Election Nowcasting, a framework composed of a process and an ML model for nowcasting election results based on SM performance as features and with offline polls as labeled data. It also defines SoMEN-DC, an execution strategy for SoMEN that enables continuous prediction during the campaign (DC). The proposed metrics and framework were applied to some of the main recent presidential elections in Latin America: Argentina (2019), Brazil (2018), Colombia (2018), and Mexico (2018). More than 65,000 posts were collected from the SM profiles of candidates on Facebook, Twitter, and Instagram with data from 195 presidential polls. Results have demonstrated that it was possible to achieve a high level of accuracy in predicting the final vote share of the candidates and to make daily predictions, providing competitive or better results than the traditional polls. The strategies put forward in this study have attempted to address several of the current challenges in this research area and have indicated a new manner of how to face the problems. They may also be directly used for predicting future elections in similar scenarios.

Assessing the relative accuracy of gridded population sampling: results from an election survey experiment

ABSTRACT International survey researchers are increasingly turning to a geospatial sampling approach known as gridded population sampling for finer resolution and a more updated sampling frame than conventional census-based sampling. To date, there have been no direct comparisons of accuracy between survey results derived from the two methods. This experiment fielded two parallel public opinion surveys concurrently before Uruguay’s 2019 first-round election to compare the accuracy of both probabilistic sampling approaches. Holding other factors constant, we found that the gridded population sample survey performed slightly better than the conventionally drawn census sample survey at predicting election results. The quality of the Uruguayan census frame set a high standard for the gridded population sample, and our experimental results demonstrate that it surpassed it. Given gridded population sampling’s additional demonstrated advantages, particularly time savings, these results suggest that it is a promising approach for face-to-face survey sampling in many lower- and middle-income countries.

Türkiye'deki Seçim Anketlerinin Toplam Anket Hatası Perspektifinden Bir İncelemesi

Election polls are not only essential to measure electorates' political attitudes, preferences, and behavior but may also influence their formation. In this study, we evaluate election polls in Turkey, which have been increasingly popular and criticized by political actors in the last years, from a survey methodology perspective and investigate whether they follow internationally recognized standards. To this end, we collected a comprehensive dataset covering all election polls on eight elections between 2011 and 2019 published at traditional or social media outlets. Our assessment of a total of 295 election polls from the perspective of the "total survey error" paradigm and CNN's transparency guidelines shows that, most election polls in Turkey do not follow scientific standards, especially in terms of representation, and are thus unsuccessful at predicting election outcomes. In the conclusion, we discuss how survey data from public opinion and election polls, and cross-national surveys can be employed in political science research.

Forecasting and Understanding the 2021 Canadian Federal Election Using Twitter Conversations

Previous studies using Twitter data to predict election outcomes have focused on sentiment and/or volume of tweets as predictive features. However, it is difficult to determine whether a tweet is expressing views in favour or counter to a political entity. By contrast, in this study deep learning is used to cluster related tweets together into “conversations”, and then those conversations are classified. Classification is achieved by identifying who is involved in the conversation: if a majority of partisans of one political party are present, then the conversation may be classified as being in favour of that party. The conversation is then predicted to change public opinion, relative to size of the conversation. This method has the added explanatory benefit of linking moments of large public opinion change directly to the source conversation, answering “why” a change happened. Time-series public polling data from the 2021 Canadian federal election is used to validate the model, and the final result of the election was forecast with the model the day before the election. It is observed that changes in public opinion seen in polling data are seen earlier in time in Twitter “conversations”