• Journal of Information Science Theory and Practice
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• v.5 no.4
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• pp.26-38
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• 2017

Even through many professional organizations increasingly use Q&A sites in their online communities for information sharing, there are few studies which examine what is really going on in the Q&A activities in professional online communities (POC). This study aims to examine the interaction patterns and contents posted in the Q&A site of a POC, KOSEN, a science and technology online community in South Korea, focusing on how actively scientific information and knowledge are shared. The interaction patterns among the participants were identified through social network analysis (SNA) and the contents in the Q&As were examined by content analysis. The results show that the overall network indicated a moderate level of participation and connection and answerers especially tended to be active. Also, there are different interaction patterns depending on academic fields. Relatively few participants were posting leaders who seemed to steer the overall interactions. Furthermore, some content related to manipulation and explanation for experiments, which are in urgent need, seem to be posted in the sites more frequently with more amounts. Combining both SNA and content analysis, this study demonstrated how actively information and knowledge is shared and what types of contents are exchanged. The findings have practical implications for POC managers and practitioners.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.483-490
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• 2021

The COVID-19 pandemic has posed significant community challenges towards higher education around the world. The urgent and unexpected request for full-time university courses to switch over to online teaching was a particular challenge. Online learning and learning imply a certain pedagogical knowledge content (PKC), mainly related to the design and organization for better learning and the creation of unique learning environments using digital technologies. With the help of the present academic paper, we provide some expert opinion on the PKC connected with online learning with the aim of helping non-university professionals (that is, those with lack of online learning experience) navigate these challenging times. Our findings point to the planning of learning activities with certain features, a combination of three types of presence (social, cognitive and facilitative) and the need to adapt the assessment system to new learning requirements. We will conclude by contemplating on how responding to a crisis can improve teaching and learning practices in the post-digital era.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.70-76
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• 2021

The appearance of COVID-19 virus has affected many aspects of our life. These include and not limited to social, financial and economic changes. One of the most important impacts is the economic effects. Many countries have taken actions to continue the teaching process through online teaching platforms. The students are expected to graduate during the next few semesters with certificates that include some online-completed courses and their graduation certificates are called mixed certificates. This paper considers graduation mixed certificates with some online courses and its impact on graduates seeking jobs. First, we study how well the mixed certificates are accepted by job market. In other words, how different companies, organizations and even governmental entities would accept such certificates when hiring. We study the perception of job market for such certificates for different learning fields. Secondly, we study how well the online courses are accepted by the students keeping in mind that these students are used to traditional face to face teaching. Finally, we paper our results and recommendations according to the collected data from the surveys. Some of the results show that about 60% of companies don't have policies to encourage hiring graduates with mixed certificates. Also, colleges are almost divided evenly between preferring face to face and preferring online teaching.

• Knowledge Management Research
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• v.17 no.4
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• pp.191-213
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• 2016

Since the smartphone era has spurred world-over, social network services have become a part of people's daily lives. However, this relatively new phenomenon of technology development raises several negative side effects such as cyberbullying. One of the representative cases of cyberbullying is posting malicious comments online. Multiple social issues arising from this have given impetus to the "benevolent comments campaign" in order to restrain the diffusion of malicious comments. Benevolent comments have advantages that generate positive externalities such as inspiring ethics for an appropriate internet culture, but there is a lack of theoretical research on the deeper understanding of posting benevolent comments. This study thus aims to extract the motivations behind posting benevolent comments through in-depth interviews and suggest alternatives for relative issues through the causal relationship diagram of the system dynamics methodology. This work contributes to our understanding of the factors that affect the increase and decrease in benevolent comments in distinct structural frameworks.

• Asia Marketing Journal
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• v.22 no.3
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• pp.71-85
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• 2020

Social media has emerged as a major hub of engagement between brands and consumers in recent years, and allows user-generated content to serve as a powerful means of encouraging communication between the sides. However, it is challenging to negotiate user-generated content owing to its lack of structure and the enormous amount generated. This study focuses on the hashtag, a metadata tag that reflects customers' brand perception through social media platforms. Online users share their knowledge and impressions using a wide variety of hashtags. We examine hashtags that co-occur with particular branded hashtags on the social media platform, Instagram, to derive insights about brand perception. We apply text mining technology and network analysis to identify the perceptions of brand images among consumers on the site, where this helps distinguish among the diverse personalities of the brands. This study contributes to highlighting the value of hashtags in constructing brand personality in the context of online marketing.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.987-999
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• 2017

Extraction of influential people from their respective domains has attained the attention of scholastic community during current epoch. This study introduces an innovative interaction strength metric for retrieval of the most influential users in the online social network. The interactive strength is measured by three factors, namely re-tweet strength, commencing intensity and mentioning density. In this article, we design a novel algorithm called IPRank that considers the communications from perspectives of followers and followees in order to mine and rank the most influential people based on proposed interaction strength metric. We conducted extensive experiments to evaluate the strength and rank of each user in the micro-blog network. The comparative analysis validates that IPRank discovered high ranked people in terms of interaction strength. While the prior algorithm placed some low influenced people at high rank. The proposed model uncovers influential people due to inclusion of a novel interaction strength metric that improves results significantly in contrast with prior algorithm.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• Journal of KIISE:Databases
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• v.37 no.4
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• pp.209-219
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• 2010

Online social network services that are rapidly growing recently store tremendous data and analyze them for many research areas. To enhance the effectiveness of information, companies or public institutions publish their data and utilize the published data for many purposes. However, a social network containing information of individuals may cause a privacy disclosure problem. Eliminating identifiers such as names is not effective for the privacy protection, since private information can be inferred through the structural information of a social network. In this paper, we consider a new complex attack type that uses both the content and structure information, and propose a model, $\ell$-degree diversity, for the privacy preserving publication of the social network data against such attacks. $\ell$-degree diversity is the first model for applying $\ell$-diversity to social network data publication and through the experiments it shows high data preservation rate.

• The KIPS Transactions:PartC
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• v.17C no.2
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• pp.215-222
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• 2010

IEEE 802.15 WPAN(Wireless personal area network) technologies represented by Bluetooth are prevalent these days. However, they have short radio coverage and thus cannot be used to build a SNS for servicing users spread over a wide area. In this paper, we develop a mobile SNS(Social Network System) that does not necessitate any infrastructure support using the Bluetooth technology and the mobility based information dissemination technique. Using so-called mobility assisted data dissemination technique we overcome the short radio range restriction of WPAN technologies. We develop Blue Market, a free online marketplace program, as a proof of concept.

• Journal of the Korea Society of Computer and Information
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• v.20 no.6
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• pp.127-140
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• 2015

In this study, we aim to analyze the relationship between the centrality in the social network and the sentiment of medial web forum users. In recent, many people use online resources to obtain health and wellness information especially social media resources. In the medial web forum, people give and receive informational supports and emotional supports and this interaction forms the social network. We analyze the social network, derive node characteristics in terms of centrality and compare the centrality index and the sentiment score derived from users' messages. We found that as more people express their emotion, they possess higher central position in the network. Further, people who express positive emotion in their messages have higher central position in the network than people who have negative emotion. This study will help to identify influentials of emotional supports to others and finally to control the depression of Alzheimer's disease patients and their related ones.