• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.661-664
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• 2017

Recently, during disasters occurrence, dealing with emergencies has been handled well by the early transmission of disaster relating notifications on social media networks (e.g., Twitter or Facebook). Intuitively, with their characteristics (e.g., real-time, mobility) and big communities whose users could be regarded as volunteers, social networks are proved to be a crucial role for disasters response. However, the amount of data transmitted during disasters is an obstacle for filtering informative messages; because the messages are diversity, large and very noise. This large volume of data could be seen as Social Big Data (SBD). In this paper, we proposed a big data platform for collecting and analyzing disasters' data from SBD. Firstly, we designed a collecting module; which could rapidly extract disasters' information from the Twitter; by big data frameworks supporting streaming data on distributed system; such as Kafka and Spark. Secondly, we developed an analyzing module which learned from SBD to distinguish the useful information from the irrelevant one. Finally, we also designed a real-time visualization on the web interface for displaying the results of analysis phase. To show the viability of our platform, we conducted experiments of the collecting and analyzing phases in 10 days for both real-time and historical tweets, which were about disasters happened in South Korea. The results prove that our big data platform could be applied to disaster information based systems, by providing a huge relevant data; which can be used for inferring affected regions and victims in disaster situations, from 21.000 collected tweets.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.960-966
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• 2012

It is prevalent to gather the location information from GPS, WiFi and etc, and therefore LBSNS (Location-Based SNS) has increased rapidly (such as location-augmented Twitter services). The message created from LBSNS include the specific area of interests which the message is created in or mentions. It is easy to propagate the location-based information of LBSNS by adapting the retweet function which is efficient way to propagate the message in tweeter. In this paper, we have defined the smart retweet as a automatic retweet function for efficient propagating the messages which is geo-tagging the location of interests. We have designed the smart retweet system based on the tweeter system. The user could specify the area of interests and build the social networking among the users which have interested in common area. The smart retweet system have been implemented by mesh-up services based on Open-API of trweeter and google map. It is expected that the smart retweet service proposed in this paper makes easy sharing of the location-based interesting information.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.381-390
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• 2022

Influenced by a culture that prefers non-face-to-face activity during the COVID-19 pandemic, chatbot usage is accelerating. Chatbots have been used for various purposes, not only for customer service in businesses and social conversations for fun but also for mental health. Chatbots are a platform where users can easily talk about their depressed moods because anonymity is guaranteed. However, most relevant research has been on social media data, especially Twitter data, and few studies have analyzed the commercially used chatbots data. In this study, we identified the characteristics of depressive discourse in user-chatbot interaction data by analyzing the chats, including the word 'depress,' using the topic modeling algorithm and the text-mining technique. Moreover, we compared its characteristics with those of the depressive moods in the Twitter data. Finally, we draw several design guidelines and suggest avenues for future research based on the study findings.

• Journal of Information Technology Services
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• v.10 no.4
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• pp.33-46
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• 2011

This research focus on the economic activity as consumer and producer traits of future customers in the convergence age. We assess level of convergence for digital devices and services respectively by questionnaire survey and interview for 14 professions. And then, for evaluating convergence level and usage of digital services of each respondents, we conducted the questionnaire survey for 343 samples. Findings of our research hold that the group who showed higher level of convergence tends to use the socialized digital services more. Convergence generation were heavy users in appstore on smart-phone and wireless game and more participating. In digital service area, facebook/cyworld, twitter, UCC, portal, internet community in digital service. Convergence generation are global network communication, buying decision making activity, actively opinion expression, prosumer attitude, dependency on digital device, experience based purchase behavior, enthusiastic information sharing.

• Knowledge Management Research
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• v.16 no.4
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• pp.69-87
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• 2015

Given the huge number of data produced by its users, SNS is a great source of customer insights. Since viral trends in SNS reflect customers' direct feedback, companies can draw out highly meaningful business insights when such data is effectively analyzed and managed. However, while the importance of understanding SNS big data keeps growing, the methods for analyzing atypical data such as SNS postings for business insights over product has not been well studied. This study aims to demonstrate the way to exploit topic modeling method to support marketing strategy generation and therefore leverage business process. First, we conducted topic modeling analysis for twitter data of Apple and Samsung smartphones. Then we comparatively examined the analysis results to draw meaningful market insights about each smartphone product. Finally, we draw out a strategic marketing recommendation for each smartphone brand based on the findings.

• Journal of Information Technology Applications and Management
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• v.16 no.4
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• pp.59-77
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• 2009

Microblog services are such new communication channels with some considerable potential to improve information sharing. The idea of sharing short messages using multiple access points seems to be appealing to people worldwide. Through the lens of flow and social identity, we explored factors that influence information sharing behaviors in microblog services. With an empirical study, we examined enjoyment and concentration(flow) and self-presentation(social identity) in microblog services like twitter can contribute to the user behaviors. Our aim was to gain insight into ways of creating an environment that facilitating voluntary sharing of information. Our findings suggested that enjoyment, concentration, and selfpresentation were crucial determinants of information sharing behaviors in microblog services. This study has important implications for academic researchers and practitioners who seek to understand why microblog service users share their information with other members in microblog services.

• Asia pacific journal of information systems
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• v.26 no.1
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• pp.124-142
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• 2016

Companies operating in social network platforms continue to improve and expand their marketing techniques. This study examines the practice of "gating", which involves virtual barriers between social network users and company content. Gates demand mandatory user endorsements, in the form of a Facebook "Likes", Twitter "retweets" etc., to gain access to company content, such as coupons and rewards,. Gating practices demand a mandatory endorsement before any content consumption takes place. Thus, while user endorsements are assumed to arise voluntarily from trusted known sources, gating practices would appear to violate this assumption. However, whether this violation lessens the effectiveness of gating practices still requires empirical validation. We investigate this question through the use of a unique panel data set that includes data on "like" endorsements obtained from a number of real-world Facebook business pages. Results of the study show that gating practices are effective for endorsement solicitation; however, gates may interfere with more traditional marketing activities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1429-1441
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• 2016

While Facebook and Twitter get worldwide attention, these popular SNSs are not available in China. As the leading local SNS, Sina Weibo has garnered much of the attention in China. The purpose of the study was to explore why Chinese college students use Sina Weibo and if gender differences exist in the motivations for using it. The results from a survey of 360 respondents show that Chinese students used Sina Weibo mainly for information-gathering, followed by accessibility to celebrity, social connection, self-presentation and entertainment. Among them the most dominant reason for using Sina Weibo was found to be information-gathering. This finding suggests that Sina Weibo functions as a platform to search for information on social issues and interests. The study also found that these motivations were significantly different between male and female users. Interestingly, female respondents used Sina Weibo much more broadly than male counterparts, accessing it to satisfy all needs such as information gathering, accessibility to celebrity, social connection, self-presentation and entertainment. Based on these findings limitations and direction for future studies are discussed.

• Knowledge Management Research
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• v.19 no.1
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• pp.19-39
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• 2018

With the development of information technology, social network services (SNS) such as Facebook and Twitter became popular and many users disclose their personal and sensitive information like private story, photographs and location information through posting and sharing. Despite the privacy concerns in SNSs, individuals continue to disclose their identity online. This phenomenon is called 'privacy paradox'. The purpose of this study is to examine the role of collective efficacy on self-disclosure in SNS context and to explain privacy paradox phenomenon. Drawing upon the communication privacy management theory, research model was developed and empirically tested with cross-sectional data from 306 individuals. Results revealed that collective efficacy has a direct positive effect on self-disclosure while privacy risk is negatively related to self-disclosure. However, privacy concern is not directly related to self-disclosure. The relationship between privacy concern and self-disclosure was moderated by collective efficacy.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.196-198
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• 2016

Online social networking sites such as MySpace, Facebook, Twitter are becoming very preeminent, and the quantities of their users are escalating very quickly. Due to the significant escalation of security vulnerabilities in social networks, user's confidentiality, authenticity, and privacy have been affected too. In this paper, a short study of online social network attacks is presented in order to identify the problems and impact of the attacks on World Wide Web (WWW).