• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.428-435
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• 2016

Hundreds of millions of new posts and information are being uploaded and propagated everyday on Online Social Networks(OSN) like Twitter, Facebook, or Instagram. This paper proposes and implements a GPS-location based SNS data mapping, analysis, and visualization system, called Smart SNS Map, which collects SNS data from Twitter and Instagram using hundreds of PlanetLab nodes distributed across the globe. Like no other previous systems, our system uniquely supports a variety of functions, including GPS-location based mapping of collected tweets and Instagram photos, keyword-based tweet or photo searching, real-time heat-map visualization of tweets and instagram photos, sentiment analysis, word cloud visualization, etc. Overall, a system like this, admittedly still in a prototype phase though, is expected to serve a role as a sort of social weather station sooner or later, which will help people understand what are happening around the SNS users, systems, society, and how they feel about them, as well as how they change over time and/or space.

• Journal of Contemporary Eastern Asia
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• v.14 no.1
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• pp.23-43
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• 2015

In most of the world, the current trend in information technology is for open data movement that promotes transparency and equal access. An opposite trend is observed in China, which has the world's largest Internet population. The country has implemented sophisticated cyber-infrastructure and practices under the name of The Golden Shield Project (commonly referred to as the Great Firewall) to limit access to popular international web services and to filter traffic containing 'undesirable' political content. Increasingly, tech-savvy Chinese bypass this firewall and use Twitter to share knowledge on censorship circumvention and encryption to collectively troubleshoot firewall evasion methods, and even mobilize actions that border on activism. Using a mixed mythological approach, the current study addresses such networked knowledge sharing among citizens in a restricted web ecosystem. On the theoretical front, this study uses webometric approaches to understand change agents and positive deviant in the diffusion of censorship circumvention technology. On policy-level, the study provides insights for Internet regulators and digital rights groups to help best utilize communication networks of positive deviants to counter Internet control.

• Journal of Information Technology Services
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• v.14 no.2
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• pp.143-158
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• 2015

Companies and governments in an era of big data have been tried to create new values with their data resources. Among many data resources, many companies especially pay attention to data which is obtained from Social Network Service (SNS) because it reveals precise opinion of customers and can be used to estimate profiles of them from their social relationships. However, it is not only hard to collect, store, and analyze the data, but system applications are also insufficient. Therefore, this study proposes a S-POS (Social POS) system which consists of three parts; Twitter Side, POS Side and TPAS (Twitter&POS Analysis System). In this system, SNS data and POS data which are collected from Twitter Side and POS Side are stored in Mongo D/B. And it provides several services with POS terminal based on analysis and matching results which are generated from TPAS. Through S-POS system, we expect to efficient and effective store and sales managements of system users. Moreover, they can provide some differentiated services such as cross-selling and personalized recommendation services.

• Journal of KIISE:Databases
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• v.41 no.4
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• pp.217-225
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• 2014

Analysis of social media such as Twitter can yield interesting perspectives to understanding human behavior, detecting hot issues, identifying influential people, or discovering a group and community. However, it is difficult to gather the data relevant to specific topics due to the main characteristics of social media data; data is large, noisy, and dynamic. This paper proposes a new algorithm that dynamically selects the seed nodes to efficiently collect tweets relevant to topics. The algorithm utilizes attributes of users to evaluate the user influence, and dynamically selects the seed nodes during the collection process. We evaluate the proposed algorithm with real tweet data, and get satisfactory performance results.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.609-617
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• 2011

This paper proposes a personalized food recommendation system using user profiles auto-generated from Twitter. The user profiles are generated by extracting nouns from Twitter, and calculating emotional scores according to whether each noun is collocated with emotion words. Representative noun information for each food is constructed by analyzing web pages relevant to foods. Appropriate foods for users can be recommended by calculating similarities among the extracted resources. The proposed system has an advantage in that it can always recommend foods even if a user is a newcomer.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.1-10
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• 2021

Cyberbullying has become a severe issue and brought a powerful impact on the cyber world. Due to the low cost and fast spreading of news, social media has become a tool that helps spread insult, offensive, and hate messages or opinions in a community. Detecting cyberbullying from social media is an intriguing research topic because it is vital for law enforcement agencies to witness how social media broadcast hate messages. Twitter is one of the famous social media and a platform for users to tell stories, give views, express feelings, and even spread news, whether true or false. Hence, it becomes an excellent resource for sentiment analysis. This paper aims to detect cyberbully threats based on Naïve Bayes, support vector machine (SVM), and k-nearest neighbour (k-NN) classifier model. Sentiment analysis will be applied based on people's opinions on social media and distribute polarity to them as positive, neutral, or negative. The accuracy for each classifier will be evaluated.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.358-377
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• 2023

During the initial period of the COVID-19 pandemic, governments around the world implemented non-pharmaceutical interventions. For these policy interventions to be effective, authorities engaged in the political discourse of legitimising their activity to generate positive public attitudes. To understand effective COVID-19 policy, this study investigates public attitudes in South Korea, the United Kingdom, and the United States and how they reflect different legitimisation of policy intervention. We adopt a big data approach to analyse public attitudes, drawing from public comments posted on Twitter during selected periods. We collect the number of tweets related to COVID-19 policy intervention and conduct a sentiment analysis using a deep learning method. Public attitudes and sentiments in the three countries show different patterns according to how policy interventions were implemented. Overall concern about policy intervention is higher in South Korea than in the other two countries. However, public sentiments in all three countries tend to improve following implementation of policy intervention. The findings suggest that governments can achieve policy effectiveness when consistent and transparent communication take place during the initial period of the pandemic. This study contributes to the existing literature by applying big data analysis to explain which policies engender positive public attitudes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.91-94
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• 2011

The LBSNS(Location-Based Social Networking Service) service has been well-received by researchers and end-users, such as Twitter. Location-Based service of Twitter is now structured that users could not subscribe the information of their interesting local area. Those who being following from someone tweet message included information of local area to them just for their own interesting. However, follower may receive that kind of tweet. In order to handle the problem, we propose filtering technique using spatial join. The first work for filtering technique is to add a location information to tweets and users. In this paper, location information is represented by MBR(Minimum Bounding Rectangle). Location information is divided into dynamic property and static property. Suppose that users are continuously moving, that means one of the dynamic property's example. At this time, a massive continous query could cause the problem in server. In this paper, we create Virtual Grid on Google Map for reducing frequency of query, and conclude that it is useful for server.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.2
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• pp.1016-1025
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• 2015

This study tried to identify the influential users of college sports teams' Twitter accounts and categorize them into three groups including an official account, media account, and layperson account. A total of 14 Twitter accounts at NCAA Division 1 universities were selected through convenience sampling method. In men's sports, the greatest number of influential users was layperson account followed by media account and official account. In women's sports, the greatest number of influential users was layperson account followed by official account and media account. The results provided the insight of college sports online social network and will expand the growing literature on social media in sport and offer practical data for marketers to use social media more effectively.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.665-676
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• 2014

As social media draws attention as a business tool, organizations, large or small, are trying to exploit social media in their business. However, lack of understanding the characteristics of each social media led them to develop a naive strategy for dealing with social media. Thus, this study aims to deepen the understanding by comparatively analyzing how social media users perceive (the image of) each social media. Facebook, Twitter, YouTube, Blogs, Communities and Cyworld were chosen for our study and data from 132 respondents were analyzed using multidimensional scaling technique. The results show that there are meaningful differences in users' perception of social media attributes, which are grouped into four; information feature, motivation, promotion tool, usability. It is also analyzed whether such differences can be found between male and female users. (Such differences are also analyzed in both male and female users' perceptions.) Further, we discuss some implications of the research results for both practitioners and researchers.