• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.129-148
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• 2023

Bitcoin is a blockchain technology-based digital currency that has been recognized as a representative cryptocurrency and a financial investment asset. Due to its highly volatile nature, Bitcoin has gained a lot of attention from investors and the public. Based on this popularity, numerous studies have been conducted on price and trend prediction using machine learning and deep learning. This study employed LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks), which have shown potential for predictive performance in the finance domain, to enhance the classification accuracy in Bitcoin price trend prediction. XAI(eXplainable Artificial Intelligence) techniques were applied to the predictive model to enhance its explainability and interpretability by providing a comprehensive explanation of the model. In the empirical experiment, CNN was applied to technical indicators and Google trend data to build a Bitcoin price trend prediction model, and the CNN model using both technical indicators and Google trend data clearly outperformed the other models using neural networks, SVM, and LSTM. Then SHAP(Shapley Additive exPlanations) was applied to the predictive model to obtain explanations about the output values. Important prediction drivers in input variables were extracted through global interpretation, and the interpretation of the predictive model's decision process for each instance was suggested through local interpretation. The results show that our proposed research framework demonstrates both improved classification accuracy and explainability by using CNN, Google trend data, and SHAP.

• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.14-28
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• 2015

Numerous research has been conducted using internet search data since the mid-2000s. For example, Google Inc. developed a service predicting influenza patterns using the internet search data. The main objective of this study is to prove the hypothesis that highway traffic indices are similar to the internet search patterns. In order to achieve this objective, a model to predict the number of vehicles entering the expressway and space-mean speed was developed and the goodness-of-fit of the model was assessed. The results revealed several findings. First, it was shown that the Google search traffic was a good predictor for the TCS entering traffic volume model at sites with frequent commute trips, and it had a negative correlation with the TCS entering traffic volume. Second, the Naver search traffic was utilized for the TCS entering traffic volume model at sites with numerous recreational trips, and it was positively correlated with the TCS entering traffic volume. Third, it was uncovered that the VDS speed had a negative relationship with the search traffic on the time series diagram. Lastly, it was concluded that the transfer function noise time series model showed the better goodness-of-fit compared to the other time series model. It is expected that "Big Data" from the internet search data can be extensively applied in the transportation field if the sources of search traffic, time difference and aggregation units are explored in the follow-up studies.

• Review of Culture and Economy
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• v.20 no.2
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• pp.119-147
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• 2017

This study analyzes Big Data to understand the economic influence of K-Beauty which is expected as a fast-growing industry. Because the content of K-beauty is mainly transmitted over the Internet, Big Data about K-Beauty in the database of online services can show interest and engagement in K-Beauty. The export volume of the beauty industry and the number of foreign tourist in Korea were used as dependent variables. The volume of Google search and the volume of YouTube page view were independent variables. According to the result of a multi-regression analysis, the volume of Google search of K-Beauty had a positive influence on both dependent variables, even after controlling for GDP (Gross Domestic Product) and distances between nations. When it comes to the volume of YouTube page view of K-Beauty, it had a positive relationship with the export volume of the beauty industry, whereas there was no significant relationship between the volume of YouTube page view and the number of foreign tourists. The result indicates that the content of K-Beauty has a significant impact on the beauty industry. Moreover, this empirical study shows that web search and YouTube search have a positive relationship with the economical aspect. These results can be used to discuss public relations strategy to promote K-Beauty industry.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.2
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• pp.53-59
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• 2010

Android that was made by Google and Open Handset Alliance is the open source software toolkit for mobile phone. In a few years, Android will be used by millions of Android mobile phones and other mobile devices, and become the main platform of application developers. In this paper, we develop an application contents Booky based on Google Android flatform by using Webview merits and Google search engine. The features of the developed content are as follows. First, a mobile-based Web browser which has an advanced screen resolution and can support more faster viewer than normal web browser as it reduces the amount of data transmission. Second, efficient E-book search and reading functionality. In the performance evaluation, we show the results of simulation using AVD(Android Virture Device).

• Journal of Digital Convergence
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• v.15 no.10
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• pp.93-101
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• 2017

This study investigate the effect of each type of search volume on the number of Facebook fans and the number of tourists. According to the hierarchy effect model, the effect of communication appears to be the sequentiality of cognition-attitude-behavior. Applying this theory, this study predicted that when consumers who have higher involvement and knowledge on specific cities through search behavior, they will be more active in information search through Facebook fan page subscription and will lead to direct tourism behavior. To verify the prediction, we examined the influences among search volume of Seoul shown in Google Trend, the number of fans of official facebook page named 'Seoul Korea', and the number of foreign tourists. As a result, the type of search keyword was divided into four categories: tourism attraction keyword, natural environment keyword, symbolic keyword, and accessibility keyword. The regression analysis showed that tourism attraction keyword and symbolic keyword have influence on Facebook fanpage 'Like'. In addition, facebook fanpage fan size have mediation effect between search volume and number of tourists. All in all, it would be useful to appeal to foreign tourists with a message that emphasizes tourism attraction and Korea-related contents.

• The Journal of Bigdata
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• v.1 no.1
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• pp.67-75
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• 2016

The study analyzed big data extracted from Google and social media to identify factors related to searches on cyber bullying in Korea and America. Korea's cyber bullying analysis was conducted social big data collected from online news sites, blogs, $caf{\acute{e}}s$, social network services and message for between January 1, 2011 and March 31, 2013. Google search trends for the search words of stress, exercise, drinking, and cyber bullying were obtained for January 1, 2004 and December 22, 2013. The main results of this study were as follows: first, the significant factors stress were cyber bullying that Korea more than America. Secondly, a positive relationship was found between stress and drinking, exercise and cyber bullying both Korea and America. Thirdly, significant differences were found all path both Korea and America. The study shows that both adults and teenagers are influenced in Korea. We need to develop online application that if cyber bullying behavior was predicted can intervene in real time because these actual cyber bullying-related exposure to psychological and behavioral characteristic.

• Information Systems Review
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• v.21 no.2
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• pp.51-67
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• 2019

This study examines IPO underpricing and its interaction with investor attention as one of key factors that can affect post-IPO performance in the short- and long-run. With higher investor attention measured as Google searches around IPO dates, our empirical results show that IT firms are underpriced more severely than non-IT firms. We also demonstrate that investor attention is positively associated with IPO performance in the short-run for both IT and non-IT firms. However, the impact of investor attention is more sustained for IT firms over a longer time horizon when coupled with a high level of underpricing while its impact is neutralized for non-IT firms. Given the unique attributes such as network effects embedded in the IT industry, our findings suggest that IPO underpricing and its interplay with investor attention for IT firms play an important role in shaping long-run performance and ultimately affecting fundamental value.