• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.207-226
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• 2019

Depression is a serious psychological disease that is expected to afflict an increasing number of people. And studies on depression have been conducted in the context of social media because social media is a platform through which users often frankly express their emotions and often reveal their mental states. In this study, large amounts of Korean text were collected and analyzed to determine whether such data could be used to detect depression in users. This study analyzed data collected from Twitter users who had and did not have depressive tendencies between January 2016 and February 2019. The data for each user was separately analyzed before and after the appearance of depressive tendencies to see how their expression changed. In this study the data were analyzed through co-occurrence word analysis, topic modeling, and sentiment analysis. This study's automated data collection method enabled analyses of data collected over a relatively long period of time. Also it compared the textual characteristics of users with depressive tendencies to those without depressive tendencies.

• Knowledge Management Research
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• v.18 no.4
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• pp.137-157
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• 2017

With the rapid growth of the internet, Social Network Services (SNSs) have played an important role. As the use of SNSs increases, users experience a sense of responsibility to respond to other people's messages or requests, and consequently experience an social overload, feeling too much social support for other users. In this study, we examined the effects of social overload on loneliness and SNS discontinuous usage intention. To verify the research model, data were collected from 83 SNS users and analyzed using SmartPLS, a structural equation modeling tool. The results of this study showed that the communal orientation and the degree of use of SNS influenced the social overload, and the social overload had a significant effect on loneliness and SNS discontinuous usage intention. The findings of this study are expected to help understand the social overload and loneliness in the use of SNS, and may also provide a strategic direction for SNS service providers.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.317-340
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• 2023

In a business context, ESG is defined as the use of environmental, social, and governance factors to assess a firm's progress in terms of sustainability. Social media has enabled the public to actively share firms' good and/or bad deeds, increasing public interest in ESG management. Therefore, this study aimed to investigate the association of firm performances with the respective sentiments towards each of environmental, social, and governance activities, as well as comprehensive ESG sentiments, which encompass all environmental, social, and governance sentiments. This study used panel regression models to examine the relationship between social media ESG sentiment and the Return on Assets (ROA) and Return on Equity (ROE) of 143 companies listed on the KOSPI 200. We collected data from 2018 to 2021, including sentiment data from a variety of social media channels, such as online communities, Instagram, blogs, Twitter, and other news. The results indicated that firm performance is significantly related to respective ESG and comprehensive ESG sentiments. This study has several implications. By using data from various social media channels, it presents an unbiased view of public ESG sentiment, rather than relying on ESG ratings, which may be influenced by rating agencies. Furthermore, the findings can be used to help firms determine the direction of their ESG management. Therefore, this study provides theoretical and practical insights for researchers and firms interested in ESG management.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.153-158
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• 2016

As the number of social network users increases, the information on event such as social issues and disasters receiving attention in each region is promptly posted by the bucket through social media site in real time, and its social ripple effect becomes huge. This study proposes a detection method of events that draw attention from users in specific region at specific time by using twitter data with regional information. In order to collect Twitter data, we use Twitter Streaming API. After collecting data, We implemented event detection system by analyze the frequency of a keyword which contained in a twit in a particular time and clustering the keywords that describes same event by exploiting keywords' co-occurrence graph. Finally, we evaluates the validity of our method through experiments.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.59-69
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• 2017

The best 'hub' to communicate with the citizen is using social media to marketing the business. However, there has several issued and the most common issue that face in critical is a capital issue. This issue is always highlight because most of automatic sentiment detection tool for Facebook or any other social media price is expensive and they lack of technical skills in order to control the tool. Therefore, in directly they have some obstacle to get faster product's feedback from customers. Thus, the personal online retailing need to struggle to stay in market because they need to compete with successful online company such as G-market. Sentiment analysis also known as opinion mining. Aim of this research is develop the tool that allow user to automatic detect the sentiment comment on social media account. RAD model methodology is chosen since its have several phases could produce more activities and output. Soppy tool will be develop using Microsoft Visual. In order to generate an accurate sentiment detection, the functionality testing will be use to find the effectiveness of this Soppy tool. This proposed automated Soppy Tool would be able to provide a platform to measure the impact of the customer sentiment over the postings on their social media site. The results and findings from the impact measurement could then be use as a recommendation in the developing or reviewing to enhance the capability and the profit to their personal online retailing company.

• Journal of Information Technology Applications and Management
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• v.26 no.6
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• pp.13-27
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• 2019

Social media are used as a tool which is suitable for delivering various images emotionally in the area of fashion. How deeply consumers are led by the brands to be engaged in the brands' SNS, how often they visit SNS and gain information, how much empathy they elicit from visitors with their contents and how continuously brands provide up-to-date information are the important factors to raise consumers' fashion consciousness and draw out their fashion consumption to express themselves. Therefore, this study aims to explore the effect of social media engagement and contents characteristics on fashion consumption tendency and purchase intention. In order to verify the research question, study makes analysis centering on the 2 × 2 × 2 MANCOVA model to draw out results of the differences among groups. As a result of analysis, this study verifies the difference between the effect of social media engagement on purchase intention and the effect of interaction of three variables on fashion consumption propensity and purchase intention and summarizes the implications.

• The Journal of Information Systems
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• v.23 no.4
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• pp.1-24
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• 2014

This study is intended to examine two issues related with social media messages. At first, the authors investigate that how they can categorize messages in the social media and how corporate twitters and brand twitters communicate with consumers. Secondly, after dividing messages in the social media into several groups, the authors investigate how each type of messages differ one another in terms of the consumer response. For examining these research issues, the authors gather twitter message data of global top 100 brands and categorize messages into 5 types (i.e., interactivity, diversion, information sharing, promotional, content) based on the motivation of communication and the format of the messages. Especially, the authors use content analysis methodology, which is normally used as the qualitative approach, in order to identify the type of messages. Furthermore, the authors present interactivity type of messages can communicate better with consumers and induce more favorable responses from consumers in the social media than any other type of messages. This research can provide implications in terms of theoretical, methodological, and managerial perspective.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.381-391
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• 2020

This research article investigates the way eWOM in social media influences the formation of destination image through development of trust and satisfaction for the potential tourist. The research involved administering an 18-point questionnaire taking online reviews, tourist involvement, and eWOM, destination image components of trust and satisfaction as variables. Data was collected from 554 individuals forming a cross-section of social media users and analyzed using multi-variate techniques (Reliability, CFA, and SEM). Results indicate a positive and significant relationship between all except online review and destination trust and satisfaction. Indirect and direct effects indicate that eWOM fully mediates the relationship between destination satisfaction and involvement and partially mediates the relationship between destination trust and involvement. In the case of online reviews, eWOM acts as a full mediator between destination trust and destination satisfaction for the future traveler using social media. The study proposes that components of image vary depending upon the degree of involvement, volume online reviews and eWOM generated also termed as 'virality' and these in turn influence the intention to revisit or recommend a destination. The study highlights its utility for National Tourist Organizations (NTOs) and online travel intermediaries to enhance destination marketing efforts.

• Journal of Distribution Science
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• v.20 no.3
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• pp.33-43
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• 2022

Purpose: More and more marketers use social media platforms to create and spread information called Marketer Generated Content (MGC) to inform consumers of products. MGC often embeds product purchase links, thus directing consumers to online distribution channels for online purchases. This study examined the effect of social media MGC on consumers' willingness to buy online in the anchor of consumers' perspective to answer the question of "how social media generated content support corporate online distribution". Research design, data, and methodology: According to the means-end-chain theory, we introduce perceived value and continuous following intention as chain mediators to explain the mechanism of MGC influence on consumers' online purchase intention and consider product type to discuss boundary conditions. Two experiments were designed to test hypothesizes. Results and Conclusion: First, emotional MGC (vs. informational MGC) has lower (higher) perceived utility (hedonic) value. Second, perceived value has a significant mediate role in the effect of MGC on continuous following intention. Third, perceived value and continuous following intention significantly and sequentially mediated the effect of MGC on online purchase intention. Through the sequential mediations of perceived utility value and continuous following intention, Informational MGC of search products significantly increase online purchase intentions. Another parallel sequential mediation, including perceived hedonic, emotional MGC of experience products, partially enhanced online purchase intentions. Finally, this study gives implications for how corporates can use social media MGC to promote product sales online.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.1-12
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• 2022

The purpose of this study is to identify the social perception characteristics of young children with disabilities over the past decade. For this purpose, Textom, an Internet-based big data analysis system was used to collect data related to young children with disabilities posted on social media. 50 keywords were selected in the order of high frequency through the data cleaning process. For semantic network analysis, centrality analysis and CONCOR analysis were performed with UCINET6, and the analyzed data were visualized using NetDraw. As a result, the keywords such as 'education, needs, parents, and inclusion' ranked high in frequency, degree, and eigenvector centrality. In addition, the keywords of 'parent, teacher, problem, program, and counseling' ranked high in betweenness centrality. In CONCOR analysis, four clusters were formed centered on the keywords of 'disabilities, young child, diagnosis, and programs'. Based on these research results, the topics on social perception of young children with disabilities were investigated, and implications for each topic were discussed.