• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.143-159
• /
• 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
• /
• v.23 no.3
• /
• pp.1-27
• /
• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.

• Korean Security Journal
• /
• no.62
• /
• pp.35-63
• /
• 2020

As the frequency and intensity of catastrophic disasters increase, there is widespread public sentiment that government capacity for disaster response and recovery is fundamentally limited, and that the involvement of civil society and the private sector is ever more vital. That is, in order to strengthen national disaster response capacity, governments need to build disaster systems that are more participatory and function through the channels of civil society, rather than continuing themselves to bear sole responsibility for these "wicked problems." With the advancement of smart mobile technology and social media, government and society as a whole have been called upon to apply these new information and communication technologies to address the current shortcomings of government-led disaster management. As illustrated in such catastrophic disasters as the 2011 Tohoku earthquake and tsunami in Japan, the 2010 Haitian earthquake, and Hurricane Katrina in the United States in 2005, the realization of participatory potential of smart technologies for better disaster response has enabled citizen participation via new smart technologies during disasters and resulted in positive impact on the management of such disasters. In this context, this study focuses on the South Korean context, and aims to analyze Korean government officials' readiness for public participation using smart technologies. On this basis, it aims to offer policy suggestions aimed at promoting smart technology-enabled citizen participation. For this purpose, it proposes a particular model, termed SMART (System, Motivation, Ability, Response, and Technology).

• Journal of The Korean Association For Science Education
• /
• v.37 no.2
• /
• pp.359-370
• /
• 2017

The purpose of this study is to explore the changes caused by using smart technology in school science inquiry. For this, we investigated 12 elementary school teachers' perceptions by using an open-ended questionnaire, group discussions, classroom discussions, and participant interviews. The results of this study indicate that the introduction of technology into classroom inquiry can open up the various possibilities and can cause additional burdens as well. First, teachers explained that smart technology can expand the opportunities to observe natural phenomena such as constellations and changing phases of the moon. However, some teachers insisted that, sometimes, learning how to use new devices disrupts students' concentration on the inquiry process itself. Second, teachers introduced the way of digital measurement using smart phone sensors in inquiry activities. They said that digital measurement is useful in terms of the reduction of errors and of the simplicity to measure. However, other teachers insisted that using new devices in classroom inquiry can entail additional variables and confuse the students' focus of inquiry. Communication about inquiry process can also be improved by using digital media. However, some teachers emphasized that they always talked about both the purpose of using SNS and online etiquettes with their students before using SNS. Based on these results, we discussed the necessity of additional analysis on the various ways of using digital devices depending on teachers' perceptions, the types of digital competency required in science inquiry using smart technology, and the features of norms shaped in inquiry activities using smart technology.

• Journal of Internet Computing and Services
• /
• v.21 no.5
• /
• pp.139-148
• /
• 2020

Infectious diseases have long plagued mankind, and predicting and preventing them has been a big challenge for mankind. For this reasen, various studies have been conducted so far to predict infectious diseases. Most of the early studies relied on epidemiological data from the Centers for Disease Control and Prevention (CDC), and the problem was that the data provided by the CDC was updated only once a week, making it difficult to predict the number of real-time disease outbreaks. However, with the emergence of various Internet media due to the recent development of IT technology, studies have been conducted to predict the occurrence of infectious diseases through web data, and most of the studies we have researched have been using single Web data to predict diseases. However, disease forecasting through a single Web data has the disadvantage of having difficulty collecting large amounts of learning data and making accurate predictions through models for recent outbreaks such as "COVID-19". Thus, we would like to demonstrate through experiments that models that use multiple Web data to predict the occurrence of infectious diseases through LSTM models are more accurate than those that use single Web data and suggest models suitable for predicting infectious diseases. In this experiment, we predicted the occurrence of "Malaria" and "Epidemic-parotitis" using a single web data model and the model we propose. A total of 104 weeks of NEWS, SNS, and search query data were collected, of which 75 weeks were used as learning data and 29 weeks were used as verification data. In the experiment we predicted verification data using our proposed model and single web data, Pearson correlation coefficient for the predicted results of our proposed model showed the highest similarity at 0.94, 0.86, and RMSE was also the lowest at 0.19, 0.07.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.5
• /
• pp.482-488
• /
• 2014

This paper aims to analyze user's emotion automatically by analyzing Twitter, a representative social network service (SNS). In order to create sentiment analysis models by using machine learning techniques, sentiment labels that represent positive/negative emotions are required. However it is very expensive to obtain sentiment labels of tweets. So, in this paper, we propose a sentiment analysis model by using self-training technique in order to utilize "data without sentiment labels" as well as "data with sentiment labels". Self-training technique is that labels of "data without sentiment labels" is determined by utilizing "data with sentiment labels", and then updates models using together with "data with sentiment labels" and newly labeled data. This technique improves the sentiment analysis performance gradually. However, it has a problem that misclassifications of unlabeled data in an early stage affect the model updating through the whole learning process because labels of unlabeled data never changes once those are determined. Thus, labels of "data without sentiment labels" needs to be carefully determined. In this paper, in order to get high performance using self-training technique, we propose 3 policies for updating "data with sentiment labels" and conduct a comparative analysis. The first policy is to select data of which confidence is higher than a given threshold among newly labeled data. The second policy is to choose the same number of the positive and negative data in the newly labeled data in order to avoid the imbalanced class learning problem. The third policy is to choose newly labeled data less than a given maximum number in order to avoid the updates of large amount of data at a time for gradual model updates. Experiments are conducted using Stanford data set and the data set is classified into positive and negative. As a result, the learned model has a high performance than the learned models by using "data with sentiment labels" only and the self-training with a regular model update policy.

• The Journal of the Korea Contents Association
• /
• v.14 no.7
• /
• pp.45-57
• /
• 2014

This study examines subjects' eye movement pattern and surveys their attitudes to the exposed advertisements on the Facebook. Different from the F-shaped pattern of the typical Web pages, users' eye movements on the Facebook have shown a rough H-shape. Even though a large number of users have shown F-shaped pattern on the ordinary Web pages in order to skip the contents of a Web, subjects' eye-movement pattern on the Facebook has H -shaped pattern due to the unique User Interface (UI) of the Facebook. With the right side and vertical arrangement of ads on the Facebook, users skip the page with having a large H-shaped pattern. In addition, this study set four AOIs(Area of Interest) that are advertising sections comprised on the Facebook Web page and measured fixation length within the AOIs then surveyed subjects' attitudes about the exposed ads. Through the experiment and survey, this study offers the optimum advertising position that can attract Facebook users' attention. As the result of experiment and survey, the second ad has the subjects' highest attitude to advertising and fourth ad is the next effectiveness and first and third ad followed. This study highlights the key implications to provide better user experiences(UX) and marketing strategies to users who are the consumers of companies and organizations which have a plan to put their advertising on the Facebook.

• The Journal of the Korea Contents Association
• /
• v.12 no.5
• /
• pp.103-112
• /
• 2012

This paper is based on the perspective that the YTN Science, a science channel in Korea which plays a key role in the popularization of science, is suffering from low viewing ratings, insufficient content, and shortage of production. First, this study employs an exploratory method to identify current status of programming and operation of the Korean science channel along with its promotional strategy. Second, it aims to conduct an analysis on the science channels, specifically some major programs, of other nations including the U.S. (Discovery Channel, National Geographic Channel), United Kingdom (BBC Knowledge), Japan (Science Channel), and China (CCTV 10), seeking the possibility to apply and combine them afterward to the Korean TV channels specialized in science. A number of implications are derived from our diagnosis of present situation and analysis of abroad cases, and this helps us suggest the content development and promotional strategies as follows: First, due to the rising need for change in the programming strategy to enhance the value of the content, it is required to rearrange the programming in terms of the target audience and the viewer lifestyle, adopt a new strategy for building up the viewers' watching habit through 'stripping', and place strategic programs in prime-time. Second, as for the specific schemes of content production and application, it is recommended to establish a dual strategy in creating the content (one for conveying knowledge, the other for delivering fun), plan and use a representative character of the program, select scientific and technological topics with more Korean backgrounds, attempt strategic ties with SNS, deepen and diversify the material for programs, and implement a strategy to boost the OSMU. Finally, with regard to the promotional strategy, a constructive proposal may include raising channel awareness through science-related events and live broadcasting, performing promotional strategies by way of expanding to printed media like magazine and book publications, and intensifying online and mobile promotional strategy.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.39 no.3
• /
• pp.337-352
• /
• 2015

This study selected a case study method conforming to qualitative research in order to analyzed how each type of fashion enterprises uses SNS in-side of fashion marketing based on content the researcher monitored and selected from the Facebook website for each enterprise. The standard to select fashion brands in this study is a graded list made based on ranking 5 analysis sites on social media (Socialbakers, socialDigm, Pulse K, BigFoot9, L2). The researcher sorted and then analyzed 2 brands that have many fan joiners and followers among fashion brands that were ranked top of the graded list, work in Facebook animatedly, and had representativeness in each type (SPA, Sports Brands). The study was conducted from January 2013 to March 2013, and the index of Facebook consisted of 3 kinds of elements (comment, like, and share). Each numerical value was counted to decide the monthly ranking. Content analysis was divided into public relations about brand, public relations about products, and customer participation and activities; consequently, the researcher investigated which content was post conforming to the ranking. The study analyzed the analysis results of each brand derived through the method of study compared to other brands. The results are as follows. In case of SPA brands, the category accounting for high rank in index analysis are public relation events to attract customer attention and products and offering information. The results of the monthly trend about whole post category were also similarly analyzed so the promotion goal that the brand wants to seek in priority coincides with the customer compliance rate. Next, in the case of sports brand, public relations for products offering information, event for arousing customer concern, and participation activities accounted for a high rank in the index analysis and posted the most in the analysis of the monthly trend for whole post category. The researcher came to understand that the direction of content for brands shown through Facebook coincides with customer sympathy.

• The Journal of the Korea Contents Association
• /
• v.10 no.3
• /
• pp.92-100
• /
• 2010

Due to the development of digital appliance, role of TV causes both-way by introducing IPTV, and SNS service causes big change of watching environment and residence environment. There are good conditions on the role of integrated control because it is arranged in the living room which secures movement most effectively and because family members can easily use, and the degree of use is high. Therefore, we infer user's needs by analyzing user scenario and current role of TV in home network environment. Primarily, we collect surveys of development scenario and technology which companies suggest TV applied by home network service, and secondly, we comparatively study scenario which the companies mentioned above suggest through observing user scenario, and study the role of IPTV in the future through actual scenario-based experiment by ethnography. After analyzing user scenario through case study and experiment, there are integrated device studies mainly in company study because it can be made up inside home, security and entertainment. On the other hand, there are patterns of user behavior by scenario experiment mainly in auto-tainment, security, and it showed that it is insufficient for interaction between TV and home media peripheral. Through this paper, we analyze context of home user, and based on this, we could suggest effective use of service development. Also after analyzing user form, we could know it also should be considered of ratio between activity inside home and activity outside home.