• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2885-2891
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• 2015

Twitter, one of online social network services, is one of the most popular micro-blogs, which generates a large number of automated programs, known as tweet bots because of the open structure of Twitter. While these tweet bots are categorized to legitimate bots and malicious bots, it is important to detect tweet bots since malicious bots spread spam and malicious contents to human users. In the conventional work, temporal information was utilized for the classficiation of human and bot. In this paper, by utilizing geo-tagged tweets that provide high-precision location information of users, we first identify both Twitter users' exact location and the corresponding timestamp, and then propose an improved two-stage tweet bot detection algorithm by computing an entropy based on spatio-temporal information. As a main result, the proposed algorithm shows superior bot detection and false alarm probabilities over the conventional result which only uses temporal information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.8
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• pp.1865-1872
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• 2013

The social network services are grown rapidly through dissemination of smart devices. Therefore, increasing the data exponentially because many people use web services. Using these big data, it will be needed study of providing customized knowledge. So in this paper, we had collected data of 40 people for implementation of knowledge service using big data during one month. Based on these data, we had inferred information of location and moving type, and evaluated accuracy. Through that we had studied personal experience knowledge evaluation model for knowledge service.

• Asia pacific journal of information systems
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• v.22 no.4
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• pp.7-29
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• 2012

Smartphone and its applications (i.e. apps) are increasingly penetrating consumer markets. According to a recent report from Korea Communications Commission, nearly 50% of mobile subscribers in South Korea are smartphone users that accounts for over 25 million people. In particular, the importance of smartphone has risen as a geospatially-aware device that provides various location-based services (LBS) equipped with GPS capability. The popular LBS include map and navigation, traffic and transportation updates, shopping and coupon services, and location-sensitive social network services. Overall, the emerging location-based smartphone apps (LBA) offer significant value by providing greater connectivity, personalization, and information and entertainment in a location-specific context. Conversely, the rapid growth of LBA and their benefits have been accompanied by concerns over the collection and dissemination of individual users' personal information through ongoing tracking of their location, identity, preferences, and social behaviors. The majority of LBA users tend to agree and consent to the LBA provider's terms and privacy policy on use of location data to get the immediate services. This tendency further increases the potential risks of unprotected exposure of personal information and serious invasion and breaches of individual privacy. To address the complex issues surrounding LBA particularly from the user's behavioral perspective, this study applied the privacy calculus model (PCM) to explore the factors that influence the adoption of LBA. According to PCM, consumers are engaged in a dynamic adjustment process in which privacy risks are weighted against benefits of information disclosure. Consistent with the principal notion of PCM, we investigated how individual users make a risk-benefit assessment under which personalized service and locatability act as benefit-side factors and information privacy risks act as a risk-side factor accompanying LBA adoption. In addition, we consider the moderating role of trust on the service providers in the prohibiting effects of privacy risks on user intention to adopt LBA. Further we include perceived ease of use and usefulness as additional constructs to examine whether the technology acceptance model (TAM) can be applied in the context of LBA adoption. The research model with ten (10) hypotheses was tested using data gathered from 98 respondents through a quasi-experimental survey method. During the survey, each participant was asked to navigate the website where the experimental simulation of a LBA allows the participant to purchase time-and-location sensitive discounted tickets for nearby stores. Structural equations modeling using partial least square validated the instrument and the proposed model. The results showed that six (6) out of ten (10) hypotheses were supported. On the subject of the core PCM, H2 (locatability ${\rightarrow}$ intention to use LBA) and H3 (privacy risks ${\rightarrow}$ intention to use LBA) were supported, while H1 (personalization ${\rightarrow}$ intention to use LBA) was not supported. Further, we could not any interaction effects (personalization X privacy risks, H4 & locatability X privacy risks, H5) on the intention to use LBA. In terms of privacy risks and trust, as mentioned above we found the significant negative influence from privacy risks on intention to use (H3), but positive influence from trust, which supported H6 (trust ${\rightarrow}$ intention to use LBA). The moderating effect of trust on the negative relationship between privacy risks and intention to use LBA was tested and confirmed by supporting H7 (privacy risks X trust ${\rightarrow}$ intention to use LBA). The two hypotheses regarding to the TAM, including H8 (perceived ease of use ${\rightarrow}$ perceived usefulness) and H9 (perceived ease of use ${\rightarrow}$ intention to use LBA) were supported; however, H10 (perceived effectiveness ${\rightarrow}$ intention to use LBA) was not supported. Results of this study offer the following key findings and implications. First the application of PCM was found to be a good analysis framework in the context of LBA adoption. Many of the hypotheses in the model were confirmed and the high value of $R^2$ (i.,e., 51%) indicated a good fit of the model. In particular, locatability and privacy risks are found to be the appropriate PCM-based antecedent variables. Second, the existence of moderating effect of trust on service provider suggests that the same marginal change in the level of privacy risks may differentially influence the intention to use LBA. That is, while the privacy risks increasingly become important social issues and will negatively influence the intention to use LBA, it is critical for LBA providers to build consumer trust and confidence to successfully mitigate this negative impact. Lastly, we could not find sufficient evidence that the intention to use LBA is influenced by perceived usefulness, which has been very well supported in most previous TAM research. This may suggest that more future research should examine the validity of applying TAM and further extend or modify it in the context of LBA or other similar smartphone apps.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.8
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• pp.301-308
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• 2015

SNS is a web-based online platform service supporting the formation of relations between users. SNS users have usually used a desktop or laptop for this purpose so far. However, the number of SNS users is greatly increasing and their access to the web is improving with the spread of smart phones. They share their daily lives with other users through SNSs. We can detect events if we analyze the contents that are left by SNS users, where the individual acts as a sensor. Such analyses have already been attempted by many researchers. In particular, Twitter is used in related spheres in various ways, because it has structural characteristics suitable for detecting events. However, there is a limitation concerning the detection of events and their locations. Thus, we developed a system that can detect the location immediately based on the district mentioned in Twitter. We tested whether the system can function in real time and evaluated its ability to detect events that occurred in reality. We also tried to improve its detection efficiency by removing noise.

• Proceedings of the Korea Contents Association Conference
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• 2013.05a
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• pp.111-112
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• 2013

19대 총선에서 특정 후보를 홍보하기 위한 목적으로 개인 맞춤형 위치기반소셜네트워크서비스(LBSNS, Location Based Social Network Service) 앱(App)을 기획하여 개발하고 선거기간 동안 활용함으로써 선거 전후 해당 후보의 지지율 변화에 어느 정도 기여했는지 그 영향을 분석하였다. 대전광역시 6개 지역구 24명의 후보를 대상으로 개인 맞춤형 LBSNS 앱을 활용한 후보와 그렇지 않은 후보를 구분하여 선거운동 기간 동안 언론에 공개적으로 발표된 지지율과 개표 결과를 바탕으로 개인별 지지율 변화를 비교하였다. 해당 앱을 활용한 3명의 후보는 각각 12.6%, 11.4% 및 11.2%씩 두 자리 수의 지지율 상승이 있었지만 나머지 21명의 후보들은 지지율 변화는 모두 3% 이내로 머물러 개인맞춤형 스마트폰 앱을 활용함으로써 후보 지지율 상승에 상당한 효과가 있었음이 밝혀졌다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.407-413
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• 2020

Participating in exhibitions is one of the major activities for tourists. When selecting their next travel destination after participating in an event, they use map services and social network services, such as blogs, to obtain information about tourist attractions. The map services are location-based recommendations, because they can easily retrieve information regarding nearby places. Blogs contain informative content about tourist attractions, thereby providing content-based recommendations. However, few services consider both location and content. In location-based recommendations, tourist attractions that are not related to the content of the event attended might be recommended. Content-based recommendation has a disadvantage in that events located at a distance might get recommended. We propose an algorithm that considers both location and content, based on information from the Korea Tourism Organization's Linked Open Data (LOD), Wikipedia, and a Korean dictionary. By extracting nouns from the description of a tourist attraction and then comparing them with nouns about other attractions, a content-based relationship is determined. The distance to the event is calculated based on the latitude and longitude of each tourist attraction. A weight selected by the user is used for linear combination with the content-based relationship to determine the preference order of the recommendations.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.25-33
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• 2016

In order to visualize point based Location-Based Social Network Services(LBSNS) data on multi-scaled tile map effectively, it is necessary to apply tile-based clustering method. Then determinating reasonable numbers and size of tiles is required. However, there is no such criteria and the numbers and size of tiles are modified based on data type and the purpose of analysis. In other words, researchers' subjectivity is always involved in this type of study. This is when Modifiable Areal Unit Problem(MAUP) occurs, that affects the results of analysis. Among LBSNS, geotagged Twitter data were chosen to find the influence of MAUP in scale effects perspective. For this purpose, the degree of spatial autocorrelation using spatial error model was altered, and change of distributions was analyzed using Morna's I. As a result, positive spatial autocorrelation showed in the original data and the spatial autocorrelation was decreased as the value of spatial autoregressive coefficient was increasing. Therefore, the intensity of the spatial autocorrelation of Twitter data was adjusted to five levels, and for each level, nine different size of grid was created. For each level and different grid sizes, Moran's I was calculated. It was found that the spatial autocorrelation was increased when the aggregation level was being increased and decreased in a certainpoint. Another tendency was found that the scale effect of MAUP was decreased when the spatial autocorrelation was high.

• Korean Journal of Social Welfare
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• v.51
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• pp.63-91
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• 2002

This article investigates 170 mental health social workers in order to find out the current status and influencing factors on linking services. The social workers of this study work at different kinds of mental health organizations(mental hospitals, mental health centers, mental rehabilitation facilities, mental residential facilities). The result from the research were 1) the degree of linking services is low, and the primary type of coordination is sharing of information and client referrals. The fund-raising and joint project cases are very poor. 2) based on personal and organizational characteristics, there arc significant differences between respondents' sex, education level, job-related status, experience in mental health field, mental health licence(personal factors), location, type of organization, and type of primary client(organizational factors). 3) the factors affecting service coordination among mental health social workers are experience in mental health field, current job-related experience(personal factors), education, location of organization, type of organization, and history of organization(organizational factors). Finally, the research recommends that establishment of cooperation system, service delivery system, and information network is needed in order to enhancing the function of service coordination among mental health social workers. Also, mental health professional associations and agencies should provide opportunity of discussion and training for mental health social workers, and develop the institutional devices to expand the content and type of service coordination.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3836-3854
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• 2022

The student group division is benefit for universities to do the student management based on the group profile. With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location. Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system. However, it is challenge in feature representation due to diverse forms of the student data. To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective. Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN). The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information. Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books. Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups. Comparative experiments have been done on a real-life campus dataset collected from a university. The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division.

• Journal of Fashion Business
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• v.19 no.6
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• pp.82-100
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• 2015

Mobile fashion apps present much opportunity for marketers to engage consumers, however not all apps provide enough functions for their targeted audience. This study aims to determine how mobile fashion apps can be used to build brand community with consumer engagement. Qualitative data on fashion mobile apps were collected from the Apple app store and Android market during the spring and summer of 2015. A total of 110 fashion mobile apps were collected;, 50 apps were identified as apparel brands that either manufacture or sell apparel to consumers, which we categorized as "brand" fashion apps, and the remaining 60 were categorized as "non-brand" fashion apps. The result of the study can be summarized as below. The 60 non-brand fashion apps were grouped into 5 app types: shopping, searching, sharing, organizational, and informational. The main functions are for informational use and shopping needs, since at least half (31 apps) are used for either retrieving information or for shopping. However, in contrast, social networking and location were infrequent and not commonly utilized by these apps. The most common type of non-brand fashion apps available were shopping apps;, many shopping apps enable users to shop from several different websites and save their items into one universal shopping cart so that they only check out once. Most of these apps are informational and help consumers make more informed decisions on purchases;, in addition many offer location services to help consumers find these items in store. While these apps perform several functions, they do not link to social media. The 50 brand apps were grouped into 5 brand types: athletic, casual, fast fashion, luxury, and retailer. These apps were also checked for attributes to determine their functionality. The result shows that the main functions of brand fashion apps are for information (82% of the 50 apps) as well as location searching (72% of 50 apps). Conversely, these apps do not offer any photo sharing, and very few have organizational or community functions. Fashion mobile apps and m-marketing elements: To build brand community, mobile apps can be designed to motivate consumer's engagement with brands. The motivations of fashion mobile apps are useful in developing fashion mobile apps. Entertainment motives can be fulfilled with multimedia attributes, functionality motives are satisfied with organizational and location-based features, information motives with informational service, socialization with community and social network, learning and intellectual stimulation from informational attributes, and trend following through photo sharing. The 8 key attributes of mobile apps can correspond to the 4 m-marketing elements (i.e., Informative content, multimedia, interactions, and product promotions) that are further intertwined with m-branding elements. App Attributes and M-Marketing aim to Build Brand Community;, the eight key attributes can impact on 4 m-branding elements, which further contribute to building brand community by affecting consumers' perceptions of brands preference and advocacy, and their likelihood to be loyal.