• 아태비즈니스연구
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• 제11권2호
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• pp.61-79
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• 2020

Purpose - This article empirically investigated the effects of the socio-political factor of censorship preconditioning, and organizational support, mediating performance expectancy of public sector officials' behavioural intention to utilise social media in a post-communist country, Mongolia. Design/methodology/approach - This study collected 212 survey data from public sector organisations in Mongolia. Using the Partial Least Squire (PLS) method, this study analyzed the proposal model grounded on the UTAUT model. Findings - There are still communist footprints in the form of censorship, which remained as a negative precondition factor, and this has an indirect negative influence, and organisational support mediates to enhance performance expectancy. Effort expectancy and social influence factors have direct positive influence on the use of social media systems in the government domain of Mongolia Research implications or Originality - This study empirically investigated the model of public employees' intention to examine the post-communist countries' cultural, social, economic, and political systems, government organisational environment of the former communist sphere. The cultural factors, censorship and organisational support, to the existing IT adoption UTAUT model were also identified to test the situation of a post-communist country, Mongolia. This study contributes to the new theoretical involvement with social media by testing a new social media-based third-party intercommunication channel, including intent to use in the public service for post-communist countries. This study practically provides the guidelines to promote social media usage for public sector in the post-communist situation.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.7-14
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• 2022

COVID-19 has caused a global disturbance, increased anxiety, and panic, eliciting diverse reactions. While its cure has not been discovered, new infection cases and fatalities are being recorded daily. The focus of the present study was to analyze the reaction of gifted undergraduate students on social media during the quarantine period of the COVID-19. A special group of gifted students, who joined the program of attracting and nurturing talents at the University of Jeddah, University students as were the target sample of this study. To analyze online reactions during the pandemic; the choice of university students was arrived at as they are perceived to be gifted academically. Hence, the analysis of the impacts on their behavior on social media use is imperative. This study presented accurate and consistent data on the effects of social media using Twitter platforms on gifted students during the quarantine occasioned by the COVID-19 pandemic. The behavior of learners due to during the use of social media was extensively explored and results analyzed. The study was carried out between April and May 2020 (quarantine period in Saudi Arabia) to establish whether the online behavior of gifted students reflects positive or negative feelings. The methods used in conducting this study the research were online interviews and scraping participants' Twitter accounts (where most of the online activities and studies take place). The study employed the Activity theory to analyze the behavior of gifted students on social media. The sample size used was 60 students, and the analysis of their behavior was based on Activity theory Overall, the results showed proactive, positive behavior for coping with a challenging situation, educating society, and entertaining. Finally, this study recommends investing in gifted students due to their valuable problem-solving skills that can help handle global pandemics efficiently.

• 디지털산업정보학회논문지
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• 제14권4호
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• pp.35-45
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• 2018

Users are sharing many of contents such as text, image, video, and so on in SNS. There are various information as like as personal interesting, opinion, and relationship in social media contents. Therefore, many of recommendation systems or search systems are being developed through analysis of social media contents. In order to extract subject-related topics of social context being collected from social media channels in developing those system, it is necessary to develop ontologies for semantic analysis. However, it is difficult to develop formal ontology because social media contents have the characteristics of non-formal data. Therefore, we develop a social topic system based on semantic and subject correlation. First of all, an extracting system of social topic based on semantic relationship analyzes semantic correlation and then extracts topics expressing semantic information of corresponding social context. Because the possibility of developing formal ontology expressing fully semantic information of various areas is limited, we develop a self-extensible architecture of ontology for semantic correlation. And then, a classifier of social contents and feed back classifies equivalent subject's social contents and feedbacks for extracting social topics according semantic correlation. The result of analyzing social contents and feedbacks extracts subject keyword, and index by measuring the degree of association based on social topic's semantic correlation. Deep Learning is applied into the process of indexing for improving accuracy and performance of mapping analysis of subject's extracting and semantic correlation. We expect that proposed system provides customized contents for users as well as optimized searching results because of analyzing semantic and subject correlation.

• Journal of Contemporary Eastern Asia
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• 제13권1호
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• pp.69-81
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• 2014

While much in the field of public management has emphasized the importance of nonprofit advocacy activities in policy and decision-making procedures, few have considered the relevance and impact of leading actors on structuring diverse patterns of information sharing and communication through social media. Building nonprofit advocacy is a complicated process for a single organization to undertake, but social media applications such as Facebook and Twitter have facilitated nonprofit organizations and stakeholders to effectively share information and communicate with each other for identifying their mission as it relates to environmental issues. By analyzing the Australian Marine Conservation Society's (AMCS) Twitter network data from the period 1 April to 20 April, 2013, this research discovered diverse patterns in nonprofit advocacy by leading actors in building advocacy. Based on the webometrics approach, analysis results show that nonprofit advocacy through social media is structured by dynamic information flows and intercommunications among participants and followers of the AMCS. Also, the findings indicate that the news media and international and domestic nonprofit organizations have a leading role in building nonprofit advocacy by clustering with their followers.

• 한국의류산업학회지
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• 제14권3호
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• pp.381-389
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• 2012

This study presents a marketing communication strategy from the aspect of new consumption orientation. Consumer preference on ads media, on-line ads media, and WOM usage were examined for new consumption orientation groups. This study was executed in a question survey format. A total of 182 questionnaires were obtained and data were analyzed by PASW 18.0 and AMOS 7. The results were as follows. First, 8 types of consumption orientation factors were revealed; 'impulsive purchase', 'promotion oriented', 'social contribution', 'passive conformity', 'innovative', 'conspicuous', 'rational', and 'environmental conservation'. Then 4 groups were formed, 'Rational & Positive', 'Conspicuous Conforming', 'Positive Social Interested' and 'Low Price Oriented'. Second, communication responses were analyzed through consumption orientation groups. The 'Rational & Positive' group responded positively to every type of advertising media (especially new media). The 'Conspicuous Conforming' and 'Positive Social Interested' groups preferred traditional media such as TV, radio, and magazines; in addition, the 'Low Price Oriented' group liked only online banner ads. For WOM preference, the 'Rational & Positive' and 'Positive Social Interested' group preferred verbal consumer information like WOM. In distribution types, the just 'Positive Social Interested' group revealed a significant result for internet shopping malls. The results from this study will help establish marketing communication strategies based on the features of consumption orientation.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1564-1573
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• 2016

With the spread of social media and mobile devices, people spend more time on online than ever before. As more people participate in various online activities, much research has been conducted on how to make use of the time effectively and productively. In this paper, we propose two methods which can be used to extract highlights and make searchable media indexes using online social data. For highlight extraction, we collected the comments from the online baseball broadcasting website. We adopted peak-finding algorithm to analyze the frequency of comments uploaded on the comments section of the website. For each indexes, we collected postings from soap opera forums provided by a popular web service called DCInside. We extracted all the instances when a character's name is mentioned in postings users upload after watching TV, which can be used to create indexes when the character appears on screen for the given episode of the soap opera The evaluation results shows the possibility of the crowdsourcing-based media interaction for both highlight extraction and index building.

• Asia pacific journal of information systems
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• 제30권3호
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• pp.513-531
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• 2020

Online Word-of-Mouth communication, or eWOM, has dramatically changed the way people network, interact, and share knowledge. Studies have examined why consumers choose to share knowledge online, especially online product reviews, as well as the motivations of individuals to share product ideas online. However, the role of gender in shaping the motivation and types of knowledge shared online has been given little consideration. Using concepts from Social Exchange Theory and the Theory of Reasoned Action, we address this research gap by developing and testing a model of gender's influence on knowledge sharing in a social media context. A PLS analysis of survey data from 257 students indicates that reputation, altruism, and subjective norms are key motivators for knowledge sharing intention in social media. More importantly, that gender plays a moderating role within the motivation-knowledge sharing relationship. We also find that subjective norms have a greater impact on knowledge sharing with women than with men. Collectively, our research results highlight individualized factors for improving customer participation in external facing social media for marketing and product innovation.

• 지능정보연구
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• 제20권4호
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• pp.89-105
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• 2014

Web2.0의 등장과 함께 급속히 발전해온 온라인 포럼, 블로그, 트위터, 페이스북과 같은 소셜 미디어 서비스는 소비자와 소비자간의 의사소통을 넘어 이제 기업과 소비자 사이의 새로운 커뮤니케이션 매체로도 인식되고 있다. 때문에 기업뿐만 아니라 수많은 기관, 조직 등에서도 소셜미디어를 활용하여 소비자와 적극적인 의사소통을 전개하고 있으며, 나아가 소셜 미디어 콘텐츠에 담겨있는 소비자 고객들의 의견, 관심, 불만, 평판 등을 분석하고 이해하며 비즈니스에 적용하기 위해 이를 적극 분석하는 단계로 진화하고 있다. 이러한 연구의 한 분야로서 비정형 텍스트 콘텐츠와 같은 빅 데이터에서 저자의 감성이나 의견 등을 추출하는 오피니언 마이닝과 감성분석 기법이 소셜미디어 콘텐츠 분석에도 활발히 이용되고 있으며, 이미 여러 연구에서 이를 위한 방법론, 테크닉, 툴 등을 제시하고 있다. 그러나 아직 대량의 소셜미디어 데이터를 수집하여 언어처리를 거치고 의미를 해석하여 비즈니스 인사이트를 도출하는 전반의 과정을 제시한 연구가 많지 않으며, 그 결과를 의사결정자들이 쉽게 이해할 수 있는 시각화 기법으로 풀어내는 것 또한 드문 실정이다. 그러므로 본 연구에서는 소셜미디어 콘텐츠의 오피니언 마이닝을 위한 실무적인 분석방법을 제시하고 이를 통해 기업의사결정을 지원할 수 있는 시각화된 결과물을 제시하고자 하였다. 이를 위해 한국 인스턴트 식품 1위 기업의 대표 상품인 N-라면을 사례 연구의 대상으로 실제 블로그 데이터와 뉴스를 수집/분석하고 결과를 도출하였다. 또한 이런 과정에서 프리웨어 오픈 소스 R을 이용함으로써 비용부담 없이 어떤 조직에서도 적용할 수 있는 레퍼런스를 구현하였다. 그러므로 저자들은 본 연구의 분석방법과 결과물들이 식품산업뿐만 아니라 타 산업에서도 바로 적용 가능한 실용적 가이드와 참조자료가 될 것으로 기대한다.

• Human Ecology Research
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• 제58권4호
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• pp.613-633
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• 2020

This study identifies consumers' perceived benefits and costs when using Samsung Health (a healthcare app) based on consumer reviews from Google Play Store's app and social media discourse. We examine the differences in the benefits and the costs of Samsung Health using these two sources of data. We conducted text frequency analysis, clustering analysis, and semantic network analysis using R programming. The major findings are as follows. First, consumers experience benefits and costs on several functions of the app, such as step counting, device interlocking, information acquisition, and competition with global consumers. Second, the results of semantic network analysis showed that there were eight benefit factors and three cost factors. We also found that the three costs correspond to the benefits, indicating that some consumers gained benefits from certain functions while others gained costs from the same functions. Third, the comparison between consumer app review and social media discourse showed that the former is appropriate to assess the performance of app functions, while the latter is appropriate to examine how the app is used in daily life and how consumers feel about it. The current study suggests managerial implications to healthcare app service providers regarding what they should strengthen and improve to enhance consumers' satisfaction. It also suggests some implications from the two media, which can be mutually complementary, for researchers who study consumer opinions.

• Journal of Information Science Theory and Practice
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• 제12권1호
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• pp.39-59
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• 2024

An online social network is a platform that is continuously expanding, which enables groups of people to share their views and communicate with one another using the Internet. The social relations among members of the public are significantly improved because of this gesture. Despite these advantages and opportunities, criminals are continuing to broaden their attempts to exploit people by making use of techniques and approaches designed to undermine and exploit their victims for criminal activities. The field of digital forensics, on the other hand, has made significant progress in reducing the impact of this risk. Even though most of these digital forensic investigation techniques are carried out manually, most of these methods are not usually appropriate for use with online social networks due to their complexity, growth in data volumes, and technical issues that are present in these environments. In both civil and criminal cases, including sexual harassment, intellectual property theft, cyberstalking, online terrorism, and cyberbullying, forensic investigations on social media platforms have become more crucial. This study explores the use of machine learning techniques for addressing criminal incidents on social media platforms, particularly during forensic investigations. In addition, it outlines some of the difficulties encountered by forensic investigators while investigating crimes on social networking sites.