• Journal of Korean language education
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• v.29 no.4
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• pp.65-92
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• 2018

This study explored how a particular social media can be used to supplement elementary-level Korean language course in the US public university. The researchers administered a survey measuring students' patterns and habits of social media use. Based on the survey results, researchers designed six different types of learning materials and uploaded them regularly to Instagram throughout the semester. At the end of the semester, a survey was conducted to find out students' satisfactory level. From the 44 students' responses, the study found out that using Instagram 1) is more accessible to students than any other learning management system, 2) is fun and students are willing to participate, 3) increased the target language exposure and authentic language use, 4) increased interaction between teachers, students and even other native speakers, and 5) is helpful to improve listening and other language skills. The study closes with the suggestion for further experimental studies.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2026-2033
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• 2023

Due to Thailand's nuclear energy public acceptance problem, the understanding of nuclear energy public perception was the key factor affecting to re-consideration of the nuclear energy program. Thailand Institute of Nuclear Technology and its alliances together developed the classification model for the nuclear energy public perception from the big data comments on social media using Facebook using deep transfer learning. The objective was to insight into the Thailand nuclear energy public perception on Facebook social media platform using sentiment analysis. The supervised learning was used to generate up-to-date classification model with more than 80% accuracy to classify the public perception on nuclear power plant news on Facebook from 2009 to 2022. The majority of neutral sentiments (80%) represented the opportunity for Thailand to convince people to receive a better nuclear perception. Negative sentiments (14%) showed support for other alternative energies due to nuclear accident concerns while positive sentiments (6%) expressed support for innovative nuclear technologies.

• Journal of Korean Library and Information Science Society
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• v.53 no.3
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• pp.263-285
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• 2022

This study presented the factor analysis on constructing the new factors affecting the middle school students' online learning behaviors from the questionnaires employed among middle school students. A total of 204 students participated and the data were collected in South Korea. The sample of middle school ninth-grade students was selected and used through purposive sampling. Findings from the factor analysis provided evidence for the eight-factor solution for the 35-items accounting for 66.15% of the shared variance. A wide range of factors has been considered to identify students' online learning behaviors. The appropriate experience and use of e-learning in the middle school period is also important as it will be a critical stepstone for future education. This research provides information that has been taken into account for advancing online learning to enhance the quality of e-learning systems for middle school students. The study results provided eight new factors affecting the middle school students' online learning behaviors; that is 1) communication using social media as a learning tool, 2) intention to share information using ICT, 3) addiction of technology, 4) adoption of technology, 5) seeking information using ICT, 6) use of social media learning, 7) information search using ICT, and 8) immersion of technology. This study confirmed that middle school students prefer communication using social media as a learning tool, and value intention to share information using ICT for the most part. The data obtained based on factor analysis can highlight the online learning behaviors towards a mixture of social media learning and ICT to ensure a new educational platform for the future of e-learning. This research expects to be useful for both middle schools of online learning to better understand students' online learning behaviors and design online learning environments and information professionals to better assist students who particularly need digital literacy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.51-73
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• 2023

Social media play a significant role in communicating information across the globe, connecting with loved ones, getting the news, communicating ideas, etc. However, a group of people uses social media to spread fake information, which has a bad impact on society. Therefore, minimizing fake news and its detection are the two primary challenges that need to be addressed. This paper presents a multi-modal deep learning technique to address the above challenges. The proposed modal can use and process visual and textual features. Therefore, it has the ability to detect fake information from visual and textual data. We used EfficientNetB0 and a sentence transformer, respectively, for detecting counterfeit images and for textural learning. Feature embedding is performed at individual channels, whilst fusion is done at the last classification layer. The late fusion is applied intentionally to mitigate the noisy data that are generated by multi-modalities. Extensive experiments are conducted, and performance is evaluated against state-of-the-art methods. Three real-world benchmark datasets, such as MediaEval (Twitter), Weibo, and Fakeddit, are used for experimentation. Result reveals that the proposed modal outperformed the state-of-the-art methods and achieved an accuracy of 86.48%, 82.50%, and 88.80%, respectively, for MediaEval (Twitter), Weibo, and Fakeddit datasets.

• Smart Media Journal
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• v.6 no.3
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• pp.41-48
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• 2017

Event detection using social media has been widespread since social network services have been an active communication channel for connecting with others, diffusing news message. Especially, the real-time characteristic of social media has created the opportunity for supporting for real-time applications/systems. Social network such as Twitter is the potential data source to explore useful information by mining messages posted by the user community. This paper proposed a novel system for temporal event detection by analyzing social data. As a result, this information can be used by first responders, decision makers, or news agents to gain insight of the situation. The proposed approach takes advantages of deep learning methods that play core techniques on the main tasks including informative data identifying from a noisy environment and temporal event detection. The former is the responsibility of Convolutional Neural Network model trained from labeled Twitter data. The latter is for event detection supported by Recurrent Neural Network module. We demonstrated our approach and experimental results on the case study of earthquake situations. Our system is more adaptive than other systems used traditional methods since deep learning enables to extract the features of data without spending lots of time constructing feature by hand. This benefit makes our approach adaptive to extend to a new context of practice. Moreover, the proposed system promised to respond to acceptable delay within several minutes that will helpful mean for supporting news channel agents or belief plan in case of disaster events.

• Educational Technology International
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• v.12 no.2
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• pp.95-116
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• 2011

This study examines effects of Facebook on language learning in terms of facilitating interaction and collaboration by applying Facebook in a Korean language class. Forty one exchange students from seventeen countries who participated in the study used Facebook to exchange information and complete group projects. Results show that Facebook was effective in sharing class materials, engaging in class community and collaborating to complete assignments. Students also comment that socializing with peers was helpful, yet more activities and discussion to draw active participation is needed. This study also points out the important role of instructors who implement social media and manage the class.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• International Journal of Knowledge Content Development & Technology
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• v.7 no.4
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• pp.27-50
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• 2017

The impact of social networking is varied from good to bad. Online activities have also been categorized into pros and cons of social networking, either as reported as hiding Internet activities among teenagers or killing loneliness among elderly. In terms of relationships, there have been argument over its closeness and quality of an online relationship in Internet settings. Looking at the contradiction in an innovative interaction between classic community communication and social media, there is an unknown scent of the future struggling and challenging both human communication and relationships in the presence of digital culture. This research uses Diffusion of Innovation to study the wide and continuous spread of digital culture in human communication; and, Media Dependency in learning and structuring the cognitive, affective and behavioral effects of social media on each person uses the media in different ways. This research will be using online survey to gain opinions from a social network site as an update of views and reflection of self-awareness to all levels of people. Social media like Facebook (FB) is perceived as a good tool of communication that it is able to bring closeness among the family members. The results show that social media like FB brings positive impact towards family members; it would help to build a better and harmonic society; and, relationships among family members and communication shall be improved and enhanced to the level of a united society.

• The Journal of Information Systems
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• v.17 no.4
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• pp.233-260
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• 2008

Universities have recognized e-Learning Systems as the critical IT resources which contribute to improving the competitiveness of the universities as well as the quality of the traditional class-based lectures. Instructors deliver the main contents in the class. Other supplementary activities like online discussions, sharing of teaching-learning materials, submission of homeworks, communication among the learners and between the instructors and the learners, and so on can be efficiently facilitated using e-Learning Systems. In other words, e-Learning Systems enable a blended learning combined class-based lectures and e-learning in a variety of ways. Nonetheless, compared to the level of implementation of e-Learning Systems, the usage of both the instructors and the learners is not high. Accordingly, this study examines the determinants to affect on the usage of e-Learning Systems from the learners perspective. To draw the key determinants, we review the IS literatures related to adoption or use of the IS like Media Richness Theory (MRT), Technology Acceptance Model (TAM), Social Influence Model (SIM), and Self-efficacy Model. The variables are drawn out to be expected on the usage of e-Learning like Media Richness, Ease of Use from MRT, TAM and Instructor's Influence, Co-learner's Influence from SIM, and Self-efficacy. To test our model and hypotheses, we have collected data in the class-based lectures using e-Learning System complementary. The results of the test with 192 data are as follows: Firstly, it shows that the Instructor's Influence and the Media Richness are the influential determinants to affect on the Perception of Usefulness of e-Learning Systems. Additionally, the Co-learner's Influence and Ease of Use in order is significant to the Perception of Usefulness. Secondly, as to the degree of use of the e-Learning Systems, the Co-leaner's Influence, the Media Richness, and the Ease of Use are, in that order, the significant determinants. The Perception of Usefulness, also, founded a key factor on increasing the use of e-Learning Systems. On the other hand, the Instructor's Influence is not significant to the use of e-Learning Systems. Finally, it has been found that Self-efficacy is significant to the Perception of Media Richness, Ease of Use, but not significant to the Perception of Usefulness.

• Korean Journal of Culture and Social Issue
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• v.16 no.2
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• pp.179-195
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• 2010

Media reports of suicides has been found to increase suicide cases that were temporally and spacially proximal to the reports, but the psychological mechanisms, social learning, underlying the negative effects was not directly tested. The present study examined the cognitive processes of social learning that media reports of suicides, especially positive contents toward suicides, might change people's perception, memory, and attitudes toward suicides positively and subsequently increase subsequent suicide intentions and behaviors. Through an internet survey, 300 adults reported their perception, memory, and attitudes toward news reports of suicides, and rated whether the suicides were described positively or negatively in the reports. Finally they reported their suicide intentions and behaviors. The results revealed that people tended to remember more the contents of suicide reports suggested to increase copycat suicides. Also, people were found to have an ironic view to suicide reports of media that they acknowledged the dangers of suicides reports and approached the reports with curiosity. More importantly, the perception of the positive reward that suicides might achieved through suicides was related with positive attitudes toward suicides and behavioral intention to suicides. The present findings was discussed in the social learning understanding of copycat suicides and their implications for suicide-prevention strategies.