This article presents the results of reviewing the predictive capacity of Google trends for national elections in Chile. The electoral results of the elections between Michelle Bachelet and Sebastián Piñera in 2006, Sebastián Piñera and Eduardo Frei in 2010, Michelle Bachelet and Evelyn Matthei in 2013, Sebastián Piñera and Alejandro Guillier in 2017, and Gabriel Boric and José Antonio Kast in 2021 were reviewed. The time series analysed were organised on the basis of relative searches between the candidacies, assisted by R software, mainly with the gtrendsR and forecast libraries. With the series constructed, forecasts were made using the ARIMA technique to check the weight of one presidential option over the other. The ARIMA analyses were performed on 3 ways of organising the data: the linear series, the series transformed by moving average and the series transformed by Hodrick-Prescott. The result indicates that the method offers optimal pre-dictive ability.

This research project examines the influence of propagandistic content on Facebook during the 2023 sectional elections in Imbabura, Ecuador. It draws attention to the strategic communication requirements that align with generational preferences and the distinctive characteristics of each digital platform. The objective is to elucidate the influence of such content on voting decisions and public perceptions of candidates. A case study approach was employed, integrating discourse analysis through Atlas.ti and the application of factor analysis to survey interpretation. The findings revealed that specific content, such as memes, exerts a notable influence on the younger electorate. This influence is reinforced by communication strategies that emphasise the frequency and repetition of this content, although no direct correlation with key demographic variables was found. Furthermore, the study demonstrates that a communication strategy devoid of value con-tent reduces social networks to mere information channels, akin to open television. In conclusion, the study emphasises the necessity for digital communication strategies that prioritise the crea-tion of valuable content for audiences, with the aim of fostering a more discerning and engaged electorate.

Introduction – Elections are a cornerstone of democratic societies, enabling citizens to express their preferences and shape the direction of their governments. Economic conditions, political dynamics, and social factors often play pivotal roles in influencing voter behavior and determining electoral outcomes, making elections a key area of study in understanding governance and policy change. Objectives – This study examines how key economic factors like inflation, unemployment, and poverty influence election results in Ghana. It also looks at how these economic elements shape voter choices and predicts the possible outcomes of the 2024 general elections using past data. Methodology – The study used historical economic and political data from 1992 to 2023, applying statistical models like ARIMA (Auto-Regressive Integrated Moving Average) to forecast future election outcomes. Economic indicators such as inflation and unemployment were included to study their relationship with voter behavior. Findings - The results show that economic factors significantly impact voter turnout and party vote share. High inflation and unemployment rates tend to reduce voter support for incumbent parties. The model also predicts possible outcomes for the 2024 election based on these economic trends. Conclusion and Recommendation – The study concludes that improving economic conditions, such as reducing unemployment, could positively influence electoral outcomes for incumbents. It is recommended that policymakers focus on stabilizing the economy to enhance electoral success.

Background and objectives: In the current context, the 2021 presidential elections in Peru distance from the social objective, not being objectively represented, that is why we analyze their validity, we determine the distances between the parties if they are extreme, the correspondences with the departments and their prospects, in the surveys we propose which departments influence the results. Methods: We use a mixed methodology, qualitative analysis, it will be multidimensional with the support of statistical methods and programs such as R Studio, worddj, Gephi, and Iramuteq. The quantitative analysis will be through factor analysis, correspondence and discriminant analysis to the data of the election Results and conclusions: The textual analysis mentions that there are dimensions such as the social issue, the results of the surveys and democracy that are far apart, regarding the electoral issue. This inculcates to work both on the part of the organisms that carry out these processes, as well as the initiative of the candidates, and the media. Regarding the quantitative analysis, it is detailed that the representative parties must be greater than 4.0 million voters, to make representative parties at the national level. The classification regarding the percentage of votes is given in three groups, highlighting the independence of the Peru Libre political party in these . In the correspondence analysis we can detail that both Fuerza Popular and Peru Libre are extremist parties. The vast majority of Peruvian citizens have an ideological tendency, intermediate between both parties. The prospective projections with a cross section give a victory to the Peru Libre party. In the discriminant analysis, the sample for the surveys focuses on 7 departments that does not include the Peruvian capital.

In this article, we estimate the value of Government Expenditure on Education-GEE in the context of Environmental, Social and Governance-ESG dataset of the World Bank. We use data from 193 countries in the period 2011-2020. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS, and Weighted Least Squares-WLS. Our results show that the value of GEE is positively associated among others to “Case of Death, by communicable disease and maternal, prenatal and nutrition conditions”, and “Unemployment”, and negatively associated among others to “Hospital Beds” and “Government Effectiveness”. Furthermore, we apply the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. Finally, we confront eight machine learning algorithms for the prediction of the future value of GEE. We found that the Polynomial Regression is the best predictive algorithm. The Polynomial Regression predicts an increase in GEE of 7.09% on average for the analysed countries.

In this article, we estimate the role of Political Stability and Absence of Violence and Terrorism-PS in the context of Environmental, Social and Governance-ESG data at world level. We analyse data from 193 countries in the period 2011-2020. We apply Panel Data with Fixed Effects, Panel Data with Random Effects and Pooled Ordinary Least Square-OLS. We found that PS is positively associated, among others, to Population Density and Government Effectiveness, and negatively associated, among others, to Research and Development Expenditure and Maximum 5-day Rainfall. Furthermore, we apply the k-Means algorithm optimized with the application of the Elbow Method and we find the presence of four clusters. Finally, we propose a confrontation among eight different machine-learning algorithms for the prediction of PS and we find that the Polynomial Regression shows the higher performance. The Polynomial Regression predicts an increase in the level of PS of 0.25% on average for the analysed countries.

In this article, I estimate the value of “Individual Using Internet”-IUI in the context of Environmental, Social and Governance-ESG database of the World Bank. I use data from 193 countries for the period 2011-2020. I found that among others the value of IUI is positively associated to “Methane Emissions” and “People Using Safely Managed Sanitation Services” and negatively associated among others to “Fossil Fuel Energy Consumption” and “Renewable Energy Consumption”. I apply the k-Means algorithm for the clusterization optimized with the Elbow Method and we find the presence of three clusters. Finally, I confront eight machine-learning algorithms to predict the future value of IUI. I found that the best predictive algorithm is Linear Regression and that the value of IUI is expected to decrease on average of 0.30% for the analysed countries.

We analyze the question of GDP Growth-GDPG rate in the context of Environmental, Social and Governance-ESG framework. We use World Bank data for 193 countries in the period 2011-2020 using different econometric techniques i.e., Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS. We found that GDPG rate is positively associated, among others, to “Government Effectiveness” and “Prevalence of Undernourishment” and negatively associated among others to “Unemployment” and “Research and Development Expenditure”. Furthermore, we have applied the k-Means algorithm optimized with the Elbow method and we found the presence of four clusters in the sense of GDPG rate. Finally, we confront eight machine learning algorithms to predict the value of GDPG rate and we found that the Polynomial Regression is the best predictor. The Polynomial Regression predicts an increase of GDPG rate equal to 2.88% on average for the analysed countries.

We estimate the determinants of Emissions-COE in the context of Environmental, Social and Governance-ESG model at world level. We use data of the World Bank for 193 countries in the period 2011-2020. We found that the level of COE is positively associated, among others to “Methane Emissions”, “Research and Development Expenditures”, and negatively associated among others to “Renewable Energy Consumption” and “Mean Drought Index”. Furthermore, we have applied a cluster analysis with the k-Means algorithm optimized with the Elbow Method and we find the presence of four cluster. Finally, we apply eight machine-learning algorithms for the prediction of the future value of COE and we find that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predicts a reduction in the level of COE equal to 5.69% on average for the analysed countries.

We estimate the value of Research and Development Expenditures as a percentage of GDP-RDE in the context of Environmental, Social and Governance-ESG model. We use the ESG World Bank database. We analyze data from193 countries in the period 2011-2020. We apply a set of econometric techniques i.e. Pooled Ordinary Least Squares-OLS, Panel Data with Random Effects, Panel Data with Fixed Effects, Weighted Least Squares-WLS. We found that the level of RDE is positively associated, among others, to “Nitrous Oxide Emissions” and “Scientific and Technical Journal Articles”, and negatively associated, among others to “Heat Index 35”, “Maximum 5-day Rainfall”. Furthermore, we perform a cluster analysis with the application of the k-Means algorithm optimized with the Elbow Method. The results show the presence of four clusters. Finally, we confront eight different machine-learning algorithms to predict the future value of RDE. We find that Linear Regression is the best predictive algorithms. RDE is expected to growth on average of 0.07% for the analysed countries.

In this article, we analyse the determinants of Regulatory Quality-RQ for 193 countries in the period 2011-2020. We use a database from ESG-Environment Social Governance of the World Bank. We apply OLS, Panel Data with Fixed Effects and Panel Data with Random Effects. We found that the variables that have the most positive impact on RQ, among others, are “GHG Net Emission”, “Mean Drought Index”, and “Heat Index”. We also found that the variables that have the most negative impact on RQ are among others “Renewable Energy Consumption”, “Voice and Accountability” and “Rule of Law”. Furthermore, we have applied the k-Means algorithm optimized with the Elbow Method and we find the presence of five clusters. In adjunct, we confront eight machine learning algorithms to predict the value of RQ and we found that the best predictor is Polynomial Regression. The predictive level of RQ for the analysed countries is expected to diminish of -1,29%. In the end, we present a network analysis with the Euclidean distance and we found the presence of a structure of seven networks using augmented data.

In this article we estimate the level of Government Effectiveness-GE in 193 countries in the period 2011-2020 using data of the ESG World Bank Database. Different econometric techniques are used i.e. Panel Data with Random Effects, Panel Data with Fixed Effects, and Pooled OLS. Results show that GE is positively related among others to “Control of Corruption”, “Political Stability and Absence of Violence/Terrorism”, and negatively associated with “Percentage Annual GDP Growth”. We perform a cluster analysis with the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. Finally, we confront eight machine learning algorithms for the prediction of GE. Results show that the Polynomial Regression is the best predictive algorithm. The value of GE is expected to growth on average by 15.97%.