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Finding motivations for customer brand loyalty is one of the most popular academic and practical research fields; in this regard, some scholars have explored motivations in the retail industry. As the concept of private brands has been one of the most widely employed strategies for business success in the industry, comparing private and national brands in terms of customer loyalty is an important topic in the retail industry. Thus, the current research focuses on exploring antecedents of customer loyalty in private and national brands, as well as investigating whether there are notable structural differences between the brands. The results, based on 1,631 responses, indicate that customer perceived service/product quality, satisfaction, trust, and cost are notable determinants of brand loyalty, while the relationship between customer satisfaction and service quality of private brands is not supported. Moreover, both indirect and direct effects of the employed factors on customer brand loyalty are reported.
Syjung Hwang; Minyoung Lee; Eunil Park; Angel P. del Pobil. Determinants of customer brand loyalty in the retail industry: A comparison between national and private brands in South Korea. Journal of Retailing and Consumer Services 2021, 63, 102684 .
AMA StyleSyjung Hwang, Minyoung Lee, Eunil Park, Angel P. del Pobil. Determinants of customer brand loyalty in the retail industry: A comparison between national and private brands in South Korea. Journal of Retailing and Consumer Services. 2021; 63 ():102684.
Chicago/Turabian StyleSyjung Hwang; Minyoung Lee; Eunil Park; Angel P. del Pobil. 2021. "Determinants of customer brand loyalty in the retail industry: A comparison between national and private brands in South Korea." Journal of Retailing and Consumer Services 63, no. : 102684.
As the SARS-CoV-2 (COVID-19) pandemic has run rampant worldwide, the dissemination of misinformation has sown confusion on a global scale. Thus, understanding the propagation of fake news and implementing countermeasures has become exceedingly important to the well-being of society. To assist this cause, we produce a valuable dataset called FibVID (Fake news information-broadcasting dataset of COVID-19), which addresses COVID-19 and non-COVID news from three key angles. First, we provide truth and falsehood (T/F) indicators of news items, as labeled and validated by several fact-checking platforms (e.g., Snopes and Politifact). Second, we collect spurious-claim-related tweets and retweets from Twitter, one of the world’s largest social networks. Third, we provide basic user information, including the terms and characteristics of “heavy fake news” user to present a better understanding of T/F claims in consideration of COVID-19. FibVID provides several significant contributions. It helps to uncover propagation patterns of news items and themes related to identifying their authenticity. It further helps catalog and identify the traits of users who engage in fake news diffusion. We also provide suggestions for future applications of FibVID with a few exploratory analyses to examine the effectiveness of the approaches used.
Jisu Kim; Jihwan Aum; Sangeun Lee; Yeonju Jang; Eunil Park; Daejin Choi. FibVID: Comprehensive fake news diffusion dataset during the COVID-19 period. Telematics and Informatics 2021, 64, 101688 .
AMA StyleJisu Kim, Jihwan Aum, Sangeun Lee, Yeonju Jang, Eunil Park, Daejin Choi. FibVID: Comprehensive fake news diffusion dataset during the COVID-19 period. Telematics and Informatics. 2021; 64 ():101688.
Chicago/Turabian StyleJisu Kim; Jihwan Aum; Sangeun Lee; Yeonju Jang; Eunil Park; Daejin Choi. 2021. "FibVID: Comprehensive fake news diffusion dataset during the COVID-19 period." Telematics and Informatics 64, no. : 101688.
As K-pop industry has been expanded internationally, the strength of the K-pop fan community is under the spotlight. K-pop fans (or a fandom as a community) seek to support their band, and one of their strategies is ‘fandom collaboration’ where different fandoms for different bands mutually support to each other for the success of their bands. This paper investigates the current practice of fandom collaboration in K-pop. We first propose the notion of the ‘fandom collaboration network’ that represents the collaborations between K-pop fandoms. By collecting and analyzing a large-scale fandom activity data, we find that fandom collaboration tends to be reciprocal. We also identify a small number of fandoms who play significant roles in fandom collaboration in K-pop, and show that they are likely to (i) show active community engagement, (ii) be boy bands, (iii) be listed in the top music charts, and (iv) has been active for 3–5 years. Our analysis reveals that the prominent K-pop events such as new album releases, annual music awards, audition shows are associated with active fandom collaboration.
Jiwon Kang; Jina Kim; Migyeong Yang; Eunil Park; Minsam Ko; Munyoung Lee; Jinyoung Han. Behind the scenes of K-pop fandom: unveiling K-pop fandom collaboration network. Quality & Quantity 2021, 1 -22.
AMA StyleJiwon Kang, Jina Kim, Migyeong Yang, Eunil Park, Minsam Ko, Munyoung Lee, Jinyoung Han. Behind the scenes of K-pop fandom: unveiling K-pop fandom collaboration network. Quality & Quantity. 2021; ():1-22.
Chicago/Turabian StyleJiwon Kang; Jina Kim; Migyeong Yang; Eunil Park; Minsam Ko; Munyoung Lee; Jinyoung Han. 2021. "Behind the scenes of K-pop fandom: unveiling K-pop fandom collaboration network." Quality & Quantity , no. : 1-22.
The online version contains supplementary material available at 10.1007/s12652-021-03366-8.
Dogun Kim; Jaeho Choi; Sangyoon Ahn; Eunil Park. A smart home dental care system: integration of deep learning, image sensors, and mobile controller. Journal of Ambient Intelligence and Humanized Computing 2021, 1 -9.
AMA StyleDogun Kim, Jaeho Choi, Sangyoon Ahn, Eunil Park. A smart home dental care system: integration of deep learning, image sensors, and mobile controller. Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-9.
Chicago/Turabian StyleDogun Kim; Jaeho Choi; Sangyoon Ahn; Eunil Park. 2021. "A smart home dental care system: integration of deep learning, image sensors, and mobile controller." Journal of Ambient Intelligence and Humanized Computing , no. : 1-9.
Jina Kim; Daeun Lee; Eunil Park. Authors’ Reply to: Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study”. Journal of Medical Internet Research 2021, 23, e29549 .
AMA StyleJina Kim, Daeun Lee, Eunil Park. Authors’ Reply to: Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study”. Journal of Medical Internet Research. 2021; 23 (6):e29549.
Chicago/Turabian StyleJina Kim; Daeun Lee; Eunil Park. 2021. "Authors’ Reply to: Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study”." Journal of Medical Internet Research 23, no. 6: e29549.
The Academy Awards are one of the most prestigious events in the global movie industry, and thus, the Oscar winner has been one of the most remarkable and hottest topics in society. Due to this, many media companies attempt to predict which nominated film will win the Oscar award (Academy Award for Best Picture). Moreover, public perceptions of each film are among the most important indicators of the Oscar award. In light of this, the present study examines user-created posts about nominated films on a topic-oriented social network service. After sentimental differences in the posts about the Oscar winners and other nominated films are examined, the concept of the adjusted Oscar winner score is proposed, explored, and tested. The results indicate notable sentimental differences, while the score is well computed and effective for computing the 2020 Oscar winner. Based on the results, the implications and key limitations are discussed.
Jisu Kim; Syjung Hwang; Eunil Park. Can we predict the Oscar winner? A machine learning approach with social network services. Entertainment Computing 2021, 39, 100441 .
AMA StyleJisu Kim, Syjung Hwang, Eunil Park. Can we predict the Oscar winner? A machine learning approach with social network services. Entertainment Computing. 2021; 39 ():100441.
Chicago/Turabian StyleJisu Kim; Syjung Hwang; Eunil Park. 2021. "Can we predict the Oscar winner? A machine learning approach with social network services." Entertainment Computing 39, no. : 100441.
UNSTRUCTURED No abstract/not applicable
Jina Kim; Daeun Lee; Eunil Park. Authors’ Reply to: Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study” (Preprint). 2021, 1 .
AMA StyleJina Kim, Daeun Lee, Eunil Park. Authors’ Reply to: Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study” (Preprint). . 2021; ():1.
Chicago/Turabian StyleJina Kim; Daeun Lee; Eunil Park. 2021. "Authors’ Reply to: Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study” (Preprint)." , no. : 1.
Purpose Understanding customers' revisiting behavior is highlighted in the field of service industry and the emergence of online communities has enabled customers to express their prior experience. Thus, purpose of this study is to investigate customers' reviews on an online hotel reservation platform, and explores their postbehaviors from their reviews. Design/methodology/approach The authors employ two different approaches and compare the accuracy of predicting customers' post behavior: (1) using several machine learning classifiers based on sentimental dimensions of customers' reviews and (2) conducting the experiment consisted of two subsections. In the experiment, the first subsection is designed for participants to predict whether customers who wrote reviews would visit the hotel again (referred to as Prediction), while the second subsection examines whether participants want to visit one of the particular hotels when they read other customers' reviews (dubbed as Decision). Findings The accuracy of the machine learning approaches (73.23%) is higher than that of the experimental approach (Prediction: 58.96% and Decision: 64.79%). The key reasons of users' predictions and decisions are identified through qualitative analyses. Originality/value The findings reveal that using machine learning approaches show the higher accuracy of predicting customers' repeat visits only based on employed sentimental features. With the novel approach of integrating customers' decision processes and machine learning classifiers, the authors provide valuable insights for researchers and providers of hospitality services.
Jina Kim; Yeonju Jang; Kunwoo Bae; Soyoung Oh; Nam Jeong Jeong; Eunil Park; Jinyoung Han; Angel P. del Pobil. Between comments and repeat visit: capturing repeat visitors with a hybrid approach. Data Technologies and Applications 2021, ahead-of-p, 1 .
AMA StyleJina Kim, Yeonju Jang, Kunwoo Bae, Soyoung Oh, Nam Jeong Jeong, Eunil Park, Jinyoung Han, Angel P. del Pobil. Between comments and repeat visit: capturing repeat visitors with a hybrid approach. Data Technologies and Applications. 2021; ahead-of-p (ahead-of-p):1.
Chicago/Turabian StyleJina Kim; Yeonju Jang; Kunwoo Bae; Soyoung Oh; Nam Jeong Jeong; Eunil Park; Jinyoung Han; Angel P. del Pobil. 2021. "Between comments and repeat visit: capturing repeat visitors with a hybrid approach." Data Technologies and Applications ahead-of-p, no. ahead-of-p: 1.
The Internet of Things provides access to information anywhere at any time on any device and has changed all domains by addressing a variety of problems in society through real-time information from interconnected devices. Among these domains, smart homes are one of the most important areas that have been significantly affected by the Internet of Things. Smart homes connected to the IoT have led to the creation of the new domain, namely, smart home-Internet of Things. A bibliometric approach was followed in this study to analyze research articles in the smart home-Internet of Things area, by extracting papers presented at notable international conferences and published in respected journals. This study collects 2339 articles from the SCOPUS database, which were published from 2015 to 2019. Publication trends, key areas, influential articles, publication venues, and several notable topics related to smart home-Internet of Things were explored. Moreover, this study confirms that there are notable improvements and developments in the area of smart home-Internet of Things, as well as smart home and Internet of Things. The findings presented herein offer notable insights and emphasize learning points for future directions of smart home-Internet of Things. Moreover, both key trends and knowledge domains of smart home-Internet of Things were presented.
Wonyoung Choi; Jisu Kim; Sangeun Lee; Eunil Park. Smart home and internet of things: A bibliometric study. Journal of Cleaner Production 2021, 301, 126908 .
AMA StyleWonyoung Choi, Jisu Kim, Sangeun Lee, Eunil Park. Smart home and internet of things: A bibliometric study. Journal of Cleaner Production. 2021; 301 ():126908.
Chicago/Turabian StyleWonyoung Choi; Jisu Kim; Sangeun Lee; Eunil Park. 2021. "Smart home and internet of things: A bibliometric study." Journal of Cleaner Production 301, no. : 126908.
Background Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention. Objective We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media. Methods Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described. Results We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with Lecture Notes in Computer Science and Journal of Medical Internet Research as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated. Conclusions The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.
Jina Kim; Daeun Lee; Eunil Park. Machine Learning for Mental Health in Social Media: Bibliometric Study. Journal of Medical Internet Research 2021, 23, e24870 .
AMA StyleJina Kim, Daeun Lee, Eunil Park. Machine Learning for Mental Health in Social Media: Bibliometric Study. Journal of Medical Internet Research. 2021; 23 (3):e24870.
Chicago/Turabian StyleJina Kim; Daeun Lee; Eunil Park. 2021. "Machine Learning for Mental Health in Social Media: Bibliometric Study." Journal of Medical Internet Research 23, no. 3: e24870.
Ever since online games gained popularity in society, it has been used as a means of interaction and facilitating relationships. Online game players tend to actively communicate and collaborate with each other to achieve common goals, and this consequently leads to the formation of strong bonds and attachments with other players of the game. However, a variety of conflicts can sometimes arise during these interactions, which are usually dependent on the players’ characteristics (e.g., their professionalities and roles in the game). However, a variety of conflicts can sometimes arise during these interactions, which are usually dependent on the players’ characteristics (e.g., their professionalities and roles in the game). In light of this trend, the current study seeks to address the effects of players’ in-game conflicts, professionalities, and roles on their outcomes in online games. To this end, a dataset of over 600 players from 291 “League of Legends” games was collected. The statistical results indicate that players without any conflicts exhibit higher winning rates and greater satisfaction than those who have in-game conflicts. Furthermore, players with a higher professionality in in-game conflicts are more likely to report greater satisfaction than those with a lower professionality. Based on the findings of the current study, both academic and practical implications are presented.
So-Jung Shin; Dahye Jeong; Eunil Park. Effects of conflicts on outcomes: The case of multiplayer online games. Entertainment Computing 2021, 38, 100407 .
AMA StyleSo-Jung Shin, Dahye Jeong, Eunil Park. Effects of conflicts on outcomes: The case of multiplayer online games. Entertainment Computing. 2021; 38 ():100407.
Chicago/Turabian StyleSo-Jung Shin; Dahye Jeong; Eunil Park. 2021. "Effects of conflicts on outcomes: The case of multiplayer online games." Entertainment Computing 38, no. : 100407.
In 2011, the Fukushima nuclear accident occurred, and this had a strong effect on public perceptions of energy facilities and services that relate not only to nuclear energy, but also renewable energy resources. Moreover, the accident has also considerably affected national energy plans in both developing and developed countries. In South Korea, several studies have been conducted since the accident to investigate public perspectives toward particular energy technologies; however, few studies have investigated public perceptions of renewable-energy technologies and tracked the transitions. Therefore, this study examines the trend of South Korean public’s perceptions of renewable-energy technologies. Based on data collected in 2016, we validated the structural connections and determined that trust, benefits, risks, and attitude were key determinants of the public’s desire to adopt these technologies; specifically, public attitude was found to be the greatest determinant of this desire. Based on the results, both implications and limitations are examined.
Eunil Park. Social Acceptance of Renewable Energy Technologies in the Post-fukushima Era. Frontiers in Psychology 2021, 11, 1 .
AMA StyleEunil Park. Social Acceptance of Renewable Energy Technologies in the Post-fukushima Era. Frontiers in Psychology. 2021; 11 ():1.
Chicago/Turabian StyleEunil Park. 2021. "Social Acceptance of Renewable Energy Technologies in the Post-fukushima Era." Frontiers in Psychology 11, no. : 1.
Non-communicable diseases (NCDs) are one of the major health threats in the world. Thus, identifying the factors that influence NCDs is crucial to monitor and manage diseases. This study investigates the effects of social-environmental and behavioral risk factors on NCDs as well as the effects of social-environmental factors on behavioral risk factors using an integrated research model. This study used a dataset from the 2017 Korea National Health and Nutrition Examination Survey. After filtering incomplete responses, 5462 valid responses remained. Items including one’s social-environmental factors (household income, education level, and region), behavioral factors (alcohol use, tobacco use, and physical activity), and NCDs histories were used for analyses. To develop a comprehensive index of each factor that allows comparison between different concepts, the researchers assigned scores to indicators of the factors and calculated a ratio of the scores. A series of path analyses were conducted to determine the extent of relationships among NCDs and risk factors. The results showed that social-environmental factors have notable effects on stroke, myocardial infarction, angina, diabetes, and gastric, liver, colon, lung, and thyroid cancers. The results indicate that the effects of social-environmental and behavioral risk factors on NCDs vary across the different types of diseases. The effects of social-environmental factors and behavioral risk factors significantly affected NCDs. However, the effect of social-environmental factors on behavioral risk factors was not supported. Furthermore, social-environmental factors and behavioral risk factors affect NCDs in a similar way. However, the effects of behavioral risk factors were smaller than those of social-environmental factors. The current research suggests taking a comprehensive view of risk factors to further understand the antecedents of NCDs in South Korea.
Nam Jeong Jeong; Eunil Park; Angel P. Del Pobil. Effects of Behavioral Risk Factors and Social-Environmental Factors on Non-Communicable Diseases in South Korea: A National Survey Approach. International Journal of Environmental Research and Public Health 2021, 18, 612 .
AMA StyleNam Jeong Jeong, Eunil Park, Angel P. Del Pobil. Effects of Behavioral Risk Factors and Social-Environmental Factors on Non-Communicable Diseases in South Korea: A National Survey Approach. International Journal of Environmental Research and Public Health. 2021; 18 (2):612.
Chicago/Turabian StyleNam Jeong Jeong; Eunil Park; Angel P. Del Pobil. 2021. "Effects of Behavioral Risk Factors and Social-Environmental Factors on Non-Communicable Diseases in South Korea: A National Survey Approach." International Journal of Environmental Research and Public Health 18, no. 2: 612.
Smartphones have become an integral part of our daily lives, which has led to the rapid growth of the smartphone market. As the global smartphone market tends to remain stable, retaining existing customers has become a challenge for smartphone manufacturers. This study investigates whether a deep hybrid learning approach with various customer-oriented types of data can be useful in exploring customer repurchase behavior of same-brand smartphones. Considering data from more than 74,000 customers, the proposed deep learning approach showed a prediction accuracy higher than 90%. Based on the results of deep hybrid learning models, we aim to provide better understanding on customer behavior, such that it could be used as valuable assets for innovating future marketing strategies.
Jina Kim; Honggeun Ji; Soyoung Oh; Syjung Hwang; Eunil Park; Angel P. del Pobil. A deep hybrid learning model for customer repurchase behavior. Journal of Retailing and Consumer Services 2020, 59, 102381 .
AMA StyleJina Kim, Honggeun Ji, Soyoung Oh, Syjung Hwang, Eunil Park, Angel P. del Pobil. A deep hybrid learning model for customer repurchase behavior. Journal of Retailing and Consumer Services. 2020; 59 ():102381.
Chicago/Turabian StyleJina Kim; Honggeun Ji; Soyoung Oh; Syjung Hwang; Eunil Park; Angel P. del Pobil. 2020. "A deep hybrid learning model for customer repurchase behavior." Journal of Retailing and Consumer Services 59, no. : 102381.
BACKGROUND Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention. OBJECTIVE We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media. METHODS Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described. RESULTS We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with Lecture Notes in Computer Science and Journal of Medical Internet Research as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated. CONCLUSIONS The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.
Jina Kim; Daeun Lee; Eunil Park. Machine Learning for Mental Health in Social Media: Bibliometric Study (Preprint). 2020, 1 .
AMA StyleJina Kim, Daeun Lee, Eunil Park. Machine Learning for Mental Health in Social Media: Bibliometric Study (Preprint). . 2020; ():1.
Chicago/Turabian StyleJina Kim; Daeun Lee; Eunil Park. 2020. "Machine Learning for Mental Health in Social Media: Bibliometric Study (Preprint)." , no. : 1.
People often rely on visual appearance of leaders when evaluating their traits and qualifications. Prior research has demonstrated various effects of thin-slicing inference based on facial appearance in specific events such as elections. By using a machine learning approach, we examine whether the pattern of face-based leadership inference differs in different domains or some facial features are universally preferred across domains. To test the hypothesis, we choose four different domains (business, military, politics, and sports) and analyze facial images of 272 CEOs, 144 4-star generals of U.S. army, 276 U.S. politicians, and 81 head coaches of professional sports teams. By extracting and analyzing facial features, we reveal that facial appearances of leaders are statistically different across the different leadership domains. Based on the identified facial attribute features, we develop a model that can classify the leadership domain, which achieves a high accuracy. The method and model in this paper provide useful resources toward scalable and computational analyses for the studies in social perception.
Jeewoo Yoon; Jungseock Joo; Eunil Park; Jinyoung Han. Cross-Domain Classification of Facial Appearance of Leaders. Lecture Notes in Computer Science 2020, 440 -446.
AMA StyleJeewoo Yoon, Jungseock Joo, Eunil Park, Jinyoung Han. Cross-Domain Classification of Facial Appearance of Leaders. Lecture Notes in Computer Science. 2020; ():440-446.
Chicago/Turabian StyleJeewoo Yoon; Jungseock Joo; Eunil Park; Jinyoung Han. 2020. "Cross-Domain Classification of Facial Appearance of Leaders." Lecture Notes in Computer Science , no. : 440-446.
Jina Kim; Eunil Park. Understanding social resistance to determine the future of Internet of Things (IoT) services. Behaviour & Information Technology 2020, 1 -11.
AMA StyleJina Kim, Eunil Park. Understanding social resistance to determine the future of Internet of Things (IoT) services. Behaviour & Information Technology. 2020; ():1-11.
Chicago/Turabian StyleJina Kim; Eunil Park. 2020. "Understanding social resistance to determine the future of Internet of Things (IoT) services." Behaviour & Information Technology , no. : 1-11.
Given the energy-related accidents and issues in our society, public perceptions of specific energy technologies are a fundamental concern while formulating local and national energy plans. As an approach toward addressing these perceptions, the current study was aimed at collecting user-created contents on renewable energy in a topic-based social network service. A word network model in social network services (SNS) was proposed, and a network analysis was conducted for examining the public perceptions of renewable energy resources. The results obtained indicated that the word network model in SNSs and the employed approaches can extract both frequently mentioned and latent issues pertaining to renewable energy. In addition, they are useful for observing public perspectives toward renewable energy. Based on the results, the implications as well as the limitations of the approach are discussed.
Jisu Kim; Dahye Jeong; Daejin Choi; Eunil Park. Exploring public perceptions of renewable energy: Evidence from a word network model in social network services. Energy Strategy Reviews 2020, 32, 100552 .
AMA StyleJisu Kim, Dahye Jeong, Daejin Choi, Eunil Park. Exploring public perceptions of renewable energy: Evidence from a word network model in social network services. Energy Strategy Reviews. 2020; 32 ():100552.
Chicago/Turabian StyleJisu Kim; Dahye Jeong; Daejin Choi; Eunil Park. 2020. "Exploring public perceptions of renewable energy: Evidence from a word network model in social network services." Energy Strategy Reviews 32, no. : 100552.
Syjung Hwang; Jina Kim; Eunil Park; Sang Jib Kwon. Who will be your next customer: A machine learning approach to customer return visits in airline services. Journal of Business Research 2020, 121, 121 -126.
AMA StyleSyjung Hwang, Jina Kim, Eunil Park, Sang Jib Kwon. Who will be your next customer: A machine learning approach to customer return visits in airline services. Journal of Business Research. 2020; 121 ():121-126.
Chicago/Turabian StyleSyjung Hwang; Jina Kim; Eunil Park; Sang Jib Kwon. 2020. "Who will be your next customer: A machine learning approach to customer return visits in airline services." Journal of Business Research 121, no. : 121-126.
Users of social media often share their feelings or emotional states through their posts. In this study, we developed a deep learning model to identify a user’s mental state based on his/her posting information. To this end, we collected posts from mental health communities in Reddit. By analyzing and learning posting information written by users, our proposed model could accurately identify whether a user’s post belongs to a specific mental disorder, including depression, anxiety, bipolar, borderline personality disorder, schizophrenia, and autism. We believe our model can help identify potential sufferers with mental illness based on their posts. This study further discusses the implication of our proposed model, which can serve as a supplementary tool for monitoring mental health states of individuals who frequently use social media.
Jina Kim; Jieon Lee; Eunil Park; Jinyoung Han. A deep learning model for detecting mental illness from user content on social media. Scientific Reports 2020, 10, 1 -6.
AMA StyleJina Kim, Jieon Lee, Eunil Park, Jinyoung Han. A deep learning model for detecting mental illness from user content on social media. Scientific Reports. 2020; 10 (1):1-6.
Chicago/Turabian StyleJina Kim; Jieon Lee; Eunil Park; Jinyoung Han. 2020. "A deep learning model for detecting mental illness from user content on social media." Scientific Reports 10, no. 1: 1-6.