• International journal of advanced smart convergence
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• v.13 no.1
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• pp.108-113
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• 2024

The purpose of this study is to analyze user perceptions of tourism platforms through big data. Data were collected from Naver, Daum, and Google as big data analysis channels. Using semantic network analysis with the keyword 'tourism platform,' a total of 29,265 words were collected. The collection period was set for two years, from August 31, 2021, to August 31, 2023. Keywords were analyzed for connected networks using TexTom and Ucinet programs for social network analysis. Keywords perceived by tourism platform users include 'travel,' 'diverse,' 'online,' 'service,' 'tourists,' 'reservation,' 'provision,' and 'region.' CONCOR analysis revealed four groups: 'platform information,' 'tourism information and products,' 'activation strategies for tourism platforms,' and 'tourism destination market.' This study aims to expand and activate services that meet the needs and preferences of users in the tourism field, as well as platforms tailored to the changing market, based on user perception, current status, and trend data on tourism platforms.

• Asian Journal for Public Opinion Research
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• v.8 no.3
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• pp.393-406
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• 2020

The discourse on the lockdown in Indonesia is getting stronger due to the increasing number of positive cases of the coronavirus and the death rate. As of August 12, 2020, the confirmed number of COVID-19 cases in Indonesia reached 130,718. There were 85,798 victims who have recovered and 5,903 who have died. Data show a significant increase in cases of COVID-19 every day. For this reason, there needs to be an evaluation of the government policy of the Republic of Indonesia in dealing with the COVID-19 pandemic in Indonesia. An evaluation of policies for handling the pandemic must include public opinion to determine any weaknesses of this policy. The development of public opinion about the lockdown policy can be understood through social media. During the COVID-19 pandemic, measuring public opinion through traditional methods (surveys) was difficult. For this reason, we utilized big data on social media as research data. The main purpose of this study is to understand public opinion on the lockdown policy in overcoming the COVID-19 pandemic in Indonesia. The things observed included: volume of Twitter users, top influencers, top tweets, and communication networks between Twitter users. For the methodological development of future public opinion research, the researchers outline the obstacles faced in researching public opinion based on big data from Twitter. The research results show that the lockdown policy is an interesting issue, as evidenced by the number of active users (79,502) forming 133,209 networks. Posts about the lockdown on Twitter continued to increase after the implementation of the lockdown policy on April 10, 2020. The lockdown policy has caused various reactions, seen from the word analysis showing 14.8% positive sentiment, 17.5% negative, and 67.67% non-categorized words. Sources of information who have played the roles of top influencers regarding the lockdown policy include: Jokowi (the president of the Republic of Indonesia), online media, television media, government departments, and governors. Based on the analysis of the network structure, it shows that Jokowi has a central role in controlling the lockdown policy. Several challenges were found in this study: 1) choosing keywords for downloading data, 2) categorizing words containing public opinion sentiment, and 3) determining the sample size.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.109-116
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• 2021

Traditionally, the engineering field has been dominated by face-to-face education focused on experimental practice, but demand for online learning has soared due to the rapid development of IT technology and Internet communication networks and recent changes in the social environment such as COVID-19. In order for efficient online education to be conducted in the engineering field, where the proportion of experimental practice is relatively high compared to other fields, virtual laboratory practice content that can replace actual experimental practice is very necessary. In this study, we developed a line tracer model and a virtual experimental software to simulate it for efficient online learning of microprocessor applications that are essential not only in the electric and electronic field but also in the overall engineering field where IT convergence takes place. In the developed line tracer model, the user can set various hardware parameter values in the desired form and write the software in assembly language or C language to test the operation on the computer. The developed line tracer virtual experimental software has been used in actual classes to verify its operation, and is expected to be an efficient virtual experimental practice tool in online non-face-to-face classes.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.1
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• pp.521-540
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• 2021

This research categorizes the factors of digital capital accumulated both in personal and social level into digital access and digital competence, and empirically analyzes how the use of public libraries effects on the formation of digital capital. As a result, it was found that participation in public library programs, the use of information services, and the utilization of digital collections had positive effect on the formation and accumulation of digital capital at the personal and social level. In contrast, it was analyzed that the use of digital collections in public libraries did not significantly affect the creation of digital contents or reciprocal collaboration based on online networks. Therefore, in order for public libraries to fully support the formation of digital capital, they should not only provide knowledge and information which is a traditional role of public libraries, but also play a role as a digital-based cooperative system.

• Smart Media Journal
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• v.10 no.4
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• pp.71-79
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• 2021

One of the social aspects that changes as the use of the Internet becomes widespread is communication in online space. In the past, only one-on-one conversations were possible remotely, except when they were physically in the same space, but nowadays, technology has been developed to enable communication with a large number of people remotely through bulletin boards, communities, and social network services. Due to the development of such information and communication networks, life becomes more convenient, and at the same time, the damage caused by rapid information exchange is also constantly increasing. Recently, cyber crimes such as sending sexual messages or personal attacks to certain people with recognition on the Internet, such as not only entertainers but also influencers, have occurred, and some of those exposed to these cybercrime have committed suicide. In this paper, in order to reduce the damage caused by malicious comments, research a method for improving the performance of discriminate malicious comments through feature extraction based on parts-of-speech.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.179-187
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• 2010

Understanding consumers' preferences and use of media types is imperative for marketing and advertising managers, especially in today's fragmented market. A clear understanding assists managers in making more effective selections of appropriate media outlets, yet individuals' choices of type and use of media are based on a variety of characteristics. This paper examines one personality trait, sensation seeking, which has not appeared in the literature examining "new" media preferences and use. Sensation seeking is a personality trait defined as "the need for varied, novel, and complex sensations and experiences and the willingness to take physical and social risks for the sake of such experiences" (Zuckerman 1979). Six hypotheses were developed from a review of the literature. Particular attention was given to the Uses and Gratification theory (Katz 1959), which explains various reasons why people choose media types and their motivations for using the different types of media. Current theory suggests that High Sensation Seekers (HSS), due to their needs for novelty, arousal and unconventional content and imagery, would exhibit higher frequency of use of new media. Specifically, we hypothesize that HSS will use the internet more than broadcast (H1a) or print media (H1b) and more than low (LSS) (H2a) or medium sensation seekers (MSS) (H2b). In addition, HSS have been found to be more social and have higher numbers of friends therefore are expected to use social networking websites such as Facebook/MySpace (H3) and chat rooms (H4) more than LSS (a) and MSS (b). Sensation seekers can manifest into a range of behaviors including disinhibition,. It is expected that alternative social networks such as Facebook/MySpace (H5) and chat rooms (H6) will be used more often for those who have higher levels of disinhibition than low (a) or medium (b) levels. Data were collected using an online survey of participants in extreme sports. In order to reach this group, an improved version of a snowball sampling technique, chain-referral method, was used to select respondents for this study. This method was chosen as it is regarded as being effective to reach otherwise hidden population groups (Heckathorn, 1997). A final usable sample of 1108 respondents, which was mainly young (56.36% under 34), male (86.1%) and middle class (58.7% with household incomes over USD 50,000) was consistent with previous studies on sensation seeking. Sensation seeking was captured using an existing measure, the Brief Sensation Seeking Scale (Hoyle et al., 2002). Media usage was captured by measuring the self reported usage of various media types. Results did not support H1a and b. HSS did not show higher levels of usage of alternative media such as the internet showing in fact lower mean levels of usage than all the other types of media. The highest media type used by HSS was print media, suggesting that there is a revolt against the mainstream. Results support H2a and b that HSS are more frequent users of the internet than LSS or MSS. Further analysis revealed that there are significant differences in the use of print media between HSS and LSS, suggesting that HSS may seek out more specialized print publications in their respective extreme sport activity. Hypothesis 3a and b showed that HSS use Facebook/MySpace more frequently than either LSS or MSS. There were no significant differences in the use of chat rooms between LSS and HSS, so as a consequence no support for H4a, although significant for MSS H4b. Respondents with varying levels of disinhibition were expected to have different levels of use of Facebook/MySpace and chat-rooms. There was support for the higher levels of use of Facebook/MySpace for those with high levels of disinhibition than low or medium levels, supporting H5a and b. Similarly there was support for H6b, Those with high levels of disinhibition use chat-rooms significantly more than those with medium levels but not for low levels (H6a). The findings are counterintuitive and give some interesting insights for managers. First, although HSS use online media more frequently than LSS or MSS, this groups use of online media is less than either print or broadcast media. The advertising executive should not place too much emphasis on online media for this important market segment. Second, social media, such as facebook/Myspace and chatrooms should be examined by managers as potential ways to reach this group. Finally, there is some implication for public policy by the higher levels of use of social media by those who are disinhibited. These individuals are more inclined to engage in more socially risky behavior which may have some dire implications, e.g. by internet predators or future employers. There is a limitation in the study in that only those who engage in extreme sports are included. This is by nature a HSS activity. A broader population is therefore needed to test if these results hold.

• Management & Information Systems Review
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• v.38 no.2
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• pp.209-223
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• 2019

The purpose of this study was to stimulate the online word-of-mouth advertising by developing the concept of word-of-mouth constraint factors and measurement tools in connection with the SNS advertising on social networks. To achieve the objective of this study, this study was conducted in 3 phases. First, the exploratory investigation(target group interview, in-depth interview, and expert interview) was performed to determine the concept and scope of the word-of-mouth constraint based on literature study and qualitative investigation method. Second, the reliability and validity of the measurement questions were verified through the survey in order to refine the developed measurement items. Third, the predictive validity of measurement items was verified by examining the relationship with other major construct concept for which the developed measurement items were different. Based on the results of study, 6 components and a total of 23 measurement questions for those components were derived. Each was called intrapersonal and interpersonal constraint(psychological sensitivity, compensatory sensitivity, and other person assessment), structural constraint(reliability, informativity, and entertainment). We developed the measurement questions related to word-of-mouth constraint based on qualitative study and quantitative study and holistically examined the social and psychological, environmental interruption factors acting as the word-of-mouth constraint factors for SNS advertising in terms of SNS achievements and evaluation from the perspective of word-of-mouth constraint. The results will lead to creation of basic framework for systematic and empirical research on the online word-of-mouth constraint and to achievement of effective SNS word-of-mouth advertising.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.636-644
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• 2019

The purpose of this study is to explore practical intervention strategies by identifying the relationships among digital informatization capacity, level of digital informatization accessability and life satisfaction of disabled people and to determine differences among these relationships depending on perceived level of social support networks. The participants were 1,639 disabled people from the 2017 digital information gap survey and the results, based on structural equation modeling and multi-group analysis, are as follows. First, digital informatization capacity has a positive influence on the level of digital informatization accessability(β=.65), and life satisfaction(β=.08). The level of digital informatization accessability also has positive influence on life satisfaction(β=.44). Second, the analysis result of the mediated effects of digital informatization accessability level between digital informatization capacity and life satisfaction was significant at a level (β=.29) even greater than the direct effect of digital informatization capacity on life satisfaction. Third, digital information capacity and digital informatization accessability have an influence on life satisfaction regardless of their perceived level of social support. The findings suggest that creating online environments where disabled people can enjoy leisure, culture, and social interaction with high accessibility and utility are as important as providing education for improving their digital informatization capacity.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.143-159
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• 2015

Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.