• Genomics & Informatics
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• v.18 no.2
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• pp.16.1-16.5
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• 2020

There has been a dramatic increase in the popularity of utilizing social media data for research purposes within the biomedical community. In PubMed alone, there have been nearly 2,500 publication entries since 2014 that deal with analyzing social media data from Twitter and Reddit. However, the vast majority of those works do not share their code or data for replicating their studies. With minimal exceptions, the few that do, place the burden on the researcher to figure out how to fetch the data, how to best format their data, and how to create automatic and manual annotations on the acquired data. In order to address this pressing issue, we introduce the Social Media Mining Toolkit (SMMT), a suite of tools aimed to encapsulate the cumbersome details of acquiring, preprocessing, annotating and standardizing social media data. The purpose of our toolkit is for researchers to focus on answering research questions, and not the technical aspects of using social media data. By using a standard toolkit, researchers will be able to acquire, use, and release data in a consistent way that is transparent for everybody using the toolkit, hence, simplifying research reproducibility and accessibility in the social media domain.

• 전자공학회논문지 IE
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• v.48 no.2
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• pp.71-78
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• 2011

The purpose of the present study is to examine the causal relationship among self-regulated learning, social presence, learning flow and satisfaction in cyber education utilizing electronic media. For this study, 304 students at W cyber university in Korea completed surveys in the fall semester of 2010. The result of this study indicated that there was a meaningful effect of self-regulated learning on learning flow and satisfaction. In addition, we founded learning flow has an intermediating effect between self-regulated learning and satisfaction. Based on these results, this study propose strategies to raise satisfaction by improving students' leading role in their learning.

• Fashion, Industry and Education
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• v.15 no.1
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• pp.44-58
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• 2017

The purpose of this research is to explore how Millennials mitigate perceived risks that occur during online apparel purchasing decisions through social interactions based on social learning theory. Also, this research investigates concerns generated from interactions with others when consuming apparel online. An exploratory investigation was undertaken with 78 undergraduate students using an online survey that included open and closed questions. Qualitative data revealed positive relationships between consumers' social interactions and purchases of apparel products online. Specifically, information searches through social interactions with trusted individuals utilizing online channels were found to validate purchasing decisions and alleviate perceived risks with purchasing apparel products online. However, consumers were also concerned with certain interactions due to the lack of credibility regarding reviewers, channels, and conflicting information. These findings provide an insight into millennial consumers' learning processes through consumer-to-consumer interactions in social media environments for apparel purchases. As online and mobile shopping along with consumers' social media usage for interacting continue to increase, these research findings guide retailers how to turn their attention to investing and utilizing these channels to enhance millennial consumers' positive purchasing experiences online.

• Journal of Information Technology Applications and Management
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• v.17 no.2
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• pp.155-173
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• 2010

Although online education service markets are growing fast, previous studies have been limited to the studies on media types or system qualities of online education. In order to provide timely implications for online education service providers to maintain and increase the number of users, other factors such as interactivity and community perspectives should be considered. In this study, social capital and service quality were adopted as antecedents of learning motivation. Also, service quality dimensions, as well as learning motivation, were chosen to examine its impact on intention to repurchase of online education services. Research findings show that structural and cognitive dimensions of social capital are proved as antecedents of relational capital; structural and relational social capital positively influence on learning motivation; tangibility positively makes impact on learning motivation; and intention to repurchase is positively influenced by responsiveness and learning motivation. Practical implications based on the research findings are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1377-1393
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• 2023

Social media is used for various purposes including entertainment, communication, information search, and voicing their thoughts and concerns about a service, product, or issue. The social media data can be used for information mining and getting insights from it. The World Health Organization has listed COVID-19 as a global epidemic since 2020. People from every aspect of life as well as the entire health system have been severely impacted by this pandemic. Even now, after almost three years of the pandemic declaration, the fear caused by the COVID-19 virus leading to higher depression, stress, and anxiety levels has not been fully overcome. This has also triggered numerous kinds of discussions covering various aspects of the pandemic on the social media platforms. Among these aspects is the part focused on vaccines developed by different countries, their features and the advantages and disadvantages associated with each vaccine. Social media users often share their thoughts about vaccinations and vaccines. This data can be used to determine the popularity levels of vaccines, which can provide the producers with some insight for future decision making about their product. In this article, we used Twitter data for the vaccine popularity detection. We gathered data by scraping tweets about various vaccines from different countries. After that, various machine learning and deep learning models, i.e., naive bayes, decision tree, support vector machines, k-nearest neighbor, and deep neural network are used for sentiment analysis to determine the popularity of each vaccine. The results of experiments show that the proposed deep neural network model outperforms the other models by achieving 97.87% accuracy.

• Journal of Fashion Business
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• v.16 no.2
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• pp.1-11
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• 2012

The purpose of this study was to examine the effects of UBL (Ubiquitous basedlearning) on fashion and consumer behavior course. Thirty-one undergraduate university students completed a 15-week capstone course in a clothing and textiles department. About sixteen percent students were majoring in liberal arts and sixty-three percent of the participants were majoring in the clothing and textiles. Mainly, the participants were junior and senior undergraduate students. The participants demonstrated positive attitude toward the UBL (Ubiquitous based-learning) on fashion and consumer behavior course. The results showed that seventy-seven percent of the participants have more opportunities to handle multi-media resources using social network and social media. Eighty percent of the participants have been developed of communication skills. Seventy-one percent of the participants were helped to learn foreign language skills. Overall, most of the participants were satisfied that their presentation skill was improved in class and they had willing to recommend the class to other students for the future.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.1-5
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• 2021

Cyberbullying is a problem that is faced in many cultures. Due to their popularity and interactive nature, social media platforms have also been affected by cyberbullying. Social media users from Arab countries have also reported being a target of cyberbullying. Machine learning techniques have been a prominent approach used by scientists to detect and battle this phenomenon. In this paper, we compare different machine learning algorithms for their performance in cyberbullying detection based on a labeled dataset of Arabic YouTube comments. Three machine learning models are considered, namely: Multinomial Naïve Bayes (MNB), Complement Naïve Bayes (CNB), and Linear Regression (LR). In addition, we experiment with two feature extraction methods, namely: Count Vectorizer and Tfidf Vectorizer. Our results show that, using count vectroizer feature extraction, the Logistic Regression model can outperform both Multinomial and Complement Naïve Bayes models. However, when using Tfidf vectorizer feature extraction, Complement Naive Bayes model can outperform the other two models.

• Journal of the Korean Society for Library and Information Science
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• v.48 no.4
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• pp.267-295
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• 2014

The purpose of this study is to analyze empirically relationships between learners' psychological factors, knowledge sourcing behavior and knowledge utilization outcomes and to analyze the mediation effect of social learning and relationships among learners. Another purpose is to understand learners' attitude on social learning and knowledge sourcing behavior. The main results of this study are as follows: First, regression results on relationships among learners' psychological factors, knowledge sourcing behavior, knowledge utilization outcomes show that learners' self-efficacy has a positive effect on social learning activity participation, and goal orientation has a positive influence on group knowledge sourcing and social learning activity participation. Users' experiences of social media has a positive effect on group knowledge sourcing, social learning activity participation and social learning interaction. From a knowledge utilization perspective, published knowledge sourcing positively affects knowledge reuse, knowledge application and knowledge innovation. Dyadic knowledge sourcing has positive influence on knowledge reuse. Group knowledge sourcing affects positively knowledge application and knowledge innovation. Second, social learning activity participation factor has full mediation effect on relationship between learners' goal orientation and group knowledge sourcing, and the relationship between users' experiences of social media and group knowledge sourcing. A relationship among members factor has full mediation effect on the relationship between published knowledge sourcing and knowledge reuse, and relationship between published knowledge sourcing and knowledge innovation. Third, the results of in-depth interview show that learners trust and easily collect knowledge from social network services in general. Also, they get a variety of idea for solving information problem from interaction among members in social learning community.

• International Journal of Knowledge Content Development & Technology
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• v.6 no.2
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• pp.43-66
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• 2016

The primary objective of this study was to investigate the perception of informational professionals' knowledge sharing practices in social media platforms. The specific objectives of the study included learning professionals' perceptions and awareness of knowledge sharing using social media, understanding their opinions and beliefs, and gaining familiarity with and reasons for using these tools. Open & close ended web-based questions were sent out by email to the international training program (ITP) participants. Findings indicated that most of the respondents' were aware of using social media and that they used social media for knowledge sharing. Speed and ease of use, managing personal knowledge, easier communication with users and colleagues and powerful communication tool are the areas that motivated them to use it. It also stated some barriers like lack of support, familiarity, trust, unfiltered information and fear of providing information. The study was limited to the perceptual aspect of the issue, specifically from the individuals' opinions and sentiments.

• The Journal of Bigdata
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• v.4 no.1
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• pp.99-118
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• 2019

This study used network analysis and clustering techniques to analyze studies on social media participation. As a result of the main path analysis, 37 major studies were extracted and divided into two networks: community-related networks and new media-related. Network analysis and clustering result in four clusters. This study has the academic significance of using academic data to grasp research trends at a macro level and using network analysis and machine learning as a methodology.