• The Journal of Asian Finance, Economics and Business
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• v.7 no.5
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• pp.9-18
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• 2020

The research paper examines the influence of elections on the stock market. The study analyses whether the market reaction would be the same when a party wins and comes to power for the second consecutive time. The study employs Market Model Event study methodology. The sample period taken for the study is 2014 to 2019. A sample of 31 companies listed in Bombay Stock Exchange is selected at random for the purpose of the study. For the elections held in 2014, an event window of 82 days was taken with 39 days prior to the event and 42 days post event. The event (t0) being the declaration of the election results. For the elections held in 2019 an event window of 83 days was taken with 41 days prior to the event and 41 days post event. The results indicate that the market reacts positively with significantly positive Average Abnormal Returns. The findings of the study reveal that the impact on the market is not the same between any two elections even when the same party comes to power for the second time. The semi-strong form of efficient market hypothesis holds true in the context of emerging markets like India.

• Asian Journal for Public Opinion Research
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• v.11 no.2
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• pp.77-106
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• 2023

The DPP's victory over the KMT in Taiwan's 2020 elections has been interpreted as a triumph for anti-China sentiment. However, the rise of political outsiders and their influence on voting behavior in this election were overlooked and underestimated. In this article, we examined different sources of data and found that supporters of these political outsiders mentioned sovereignty and cross-Strait issues less than the incumbent Tsai Ing-wen. However, when faced with the choice between Tsai and challenger Han Kuo-yu, voters who were concerned about governance chose Tsai, contributing to her winning a record number of votes. This article suggests that economic and governance issues had a considerable role in the election's result and will probably be the main focus of the 2024 presidential election. With the potential for a conflict in the Taiwan Strait increasing, anti-China sentiment is unlikely to be the deciding factor this time around.

• Korean Journal of Legislative Studies
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• v.27 no.3
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• pp.155-188
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• 2021

This study examines dynamics in election news making. It is important to understand when and how news media produce election news in order to grasp news making and voting behavior. The news media sometimes make election news by focusing on issues and policies. Often they frame elections as a game and focus on election strategies while covering elections. This article argues that as time goes by during the election period, the number of policy news tends to decrease while the frequency of strategic news is likely to increase. Also, TV's and newspapers show distinctive patterns of election news making. In order to examine the arguments, this study categorizes election news stories into policy and strategic news stories produced during the 2020 Korean congressional elections and constructs daily time-series data of them. The results of structural break and regression analyses partially support the arguments.

• The Journal of Industrial Distribution & Business
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• v.12 no.12
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• pp.25-33
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• 2021

Purpose: This paper estimates the impact of the epidemic crisis on election outcomes through investigating the effect of Covid19 crisis on election results of 21st General Election held in April 15th 2020 in Korea. Research design, data and methodology: This study employs Ordinary Least Square (OLS) method using district-level data from Seoul and Gyeonggi province available at National election data in Korea. Results: Despite the current crisis in Korea, Covid-19 has had positive effects on voter turnout on average, after controlling for other factors. On the other hand, the effect of Covid-19 on the voter turnout was negative in districts with a larger aging population and higher health insurance premiums. In addition, Covid-19 negatively impacted vote shares for the incumbent party, while its rival party saw gains in their votes. Conclusion: The effect of Covid-19 election outcomes in Korea is distinct from other countries due to the nationwide acknowledgment of the Korean government's achievement in managing the epidemic. This implies that the crisis management ability of a government is crucial in gaining support for an incumbent party in future elections. Countries facing upcoming elections need to implement acceptable Covid-19 restriction policies as well as economic support for compensation to reap similar benefits.

• Asian Journal for Public Opinion Research
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• v.8 no.2
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• pp.67-104
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• 2020

Fake news and disinformation provoked heated arguments during Taiwan's 2018 local election. Most significantly, concerns grew that Beijing was attempting to sway the island's politics armed with a new "Russian-style influence campaign" weapon (Horton, 2018). To investigate the speculated effects of the "onslaught of misinformation," an online survey with 1068 randomly selected voters was conducted immediately after the election. Findings confirmed that false news affected Taiwanese voters' judgment of the news and their voting decisions. More than 50% of the voters cast their votes without knowing the correct campaign news. In particular, politically neutral voters, who were the least able to discern fake news, tended to vote for the China-friendly Kuomintang (KMT) candidates. Demographic analysis further revealed that female voters tended to be more likely to believe fake news during the election period compared to male voters. Younger or lower-income voters had the lowest levels of discernment of fake news. Further analyses and the implications of these findings for international societies are deliberated in the conclusion.

• Journal of Contemporary Eastern Asia
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• v.19 no.1
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• pp.59-83
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• 2020

This paper examines the actions and the factors driving those actions to reduce energy consumption and enhance energy efficiency taken by United States cities. While not much empirical evidence is available on why governments pursue practical sustainability actions, we attempt to shed more light on this important topic by empirically identifying factors that contribute to concrete actions toward sustainability policies. We adopt political market theory as a basic theoretical framework with policy-making applied to city energy consumption. Using the 2010 ICMA (local government sustainability policies and program) data, this study expands the focus of analyses to evaluate the effect of the form of government on energy consumption and energy efficiency by using multiple regression analysis. The findings show that at the city level, the mayor-council form of government are negatively associated with governments' efforts to reduce energy consumption. However, cities with at-large elections and municipal ownership are more likely to adopt sustainability actions. We also find that a large-scale economy has significant effects on the effort to reduce city energy consumption and improve energy efficiency. This shows that environmental policies are directly connected to locally relevant affairs, including housing, energy use, green transportation, and water. Thus, local level administrators could take an executive role to protect the environment, encourage the development of alternative energy, and reduce the use of fossil fuel and coal energy. These efforts can lead to important environmental ramifications and relevant actions by municipal governments.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.301-316
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• 2020

Algorithm journalism refers to the practices of automated news generation using algorithms that generate human sounding narratives. Algorithm journalism is known to have strengths in automating repetitive tasks through rapid and accurate analysis of data, and has been actively used in news domains such as sports and finance. In this paper, we propose an interactive card news system that generates personalized local election articles in 2018. The system consists of modules that collects and analyzes election data, generates texts and images, and allows users to specify their interests in the local elections. When a user selects interested regions, election types, candidate names, and political parties, the system generates card news according to their interest. In the study, we examined how personalized card news are evaluated in comparison with text and card news articles by human journalists, and derived implications on the potential use of algorithm in reporting political events.

• The Journal of the Convergence on Culture Technology
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• v.6 no.1
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• pp.111-120
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• 2020

As an application of big data and artificial intelligence techniques, this study proposes an atypical language-based sentimental opinion poll methodology, unlike conventional opinion poll methodology. An alternative method for the sentimental classification model based on existing statistical analysis was to collect real-time Twitter data related to parliamentary elections and perform empirical analyses on the Polarity and Intensity of public opinion using attribute-based sensitivity analysis. In order to classify the polarity of words used on individual SNS, the polarity of the new Twitter data was estimated using the learned Lasso and Ridge regression models while extracting independent variables that greatly affect the polarity variables. A social network analysis of the relationships of people with friends on SNS suggested a way to identify peer group sensitivity. Based on what voters expressed on social media, political opinion sensitivity analysis was used to predict party approval rating and measure the accuracy of the predictive model polarity analysis, confirming the applicability of the sensitivity analysis methodology in the political field.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• Korean Journal of Legislative Studies
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• v.26 no.2
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• pp.113-140
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• 2020

This study is meaningful in that it is the first analysis of Korean elections using the concept of spatial autocorrelation. Spatial autocorrelation means that an event occurring in one location in space has a high correlation with an event occurring in the surrounding area. The voter turnout rate in the 21^st general election of Seoul area was divided into the early-voting turnout and voting-day turnout, and the spatial pattern of the turnout was examined. Most of the previous studies were based on the unit of the precinct and personal data, but this study analyzed on the basis of the lower unit, Eup-myeon-dong, and analyzed using spatial data and aggregate data. Moran I index showed a fairly high spatial autocorrelation of 0.261 in the voting-day turnout, while the index of the early-voting turnout was low at 0.095, indicating that there was little spatial autocorrelation despite statistical significance. The voting-day turnout, which showed strong spatial autocorrelation, was compared and analyzed using the OLS regression model and the spatial statistics model. In the general regression model, the coefficient of determination R² rose from 0.585261 to 0.656631 in the spatial error model, showing an increase in explanatory power of about 7 percentage points. This means that the spatial statistical model has high explanatory power. The most interesting result is the relationship between the early-voting turnout and the voting-day turnout. The higher the early-voting turnout is, the lower the voting-day turnout is. When the early-voing turnout increases by about 2%, the voting-day turnout drops by about 1%. In this study, the variables affecting the early-voting turnout and the voting-day turnout are very different. This finding is different from the previous researches.