• Journal of information and communication convergence engineering
• /
• v.10 no.2
• /
• pp.135-140
• /
• 2012

Massive numbers of users of social networks share various types of information such as opinions, news, and ideas in real time. As a new form of social network, Twitter is a particularly useful information source. Studying influence can help us better understand the role of social networks. The popularity of social networks like Twitter is primarily measured by the number of followers. The number of followers in Twitter and the number of users exposed to news media are important factors in measuring influence. We chose Twitter and the New York Times as representative media to analyze the influence and present an empirical analysis of these datasets. When the correlation between the number of followers in Twitter and the number of users exposed to the New York Times is computed, the result is moderately high. The correlation between the number of users exposed to the New York Times and the number of sections including the users on it, was found to be very high. We measure the normalized influence score using our proposed expression based on the two correlation coefficients.

• Knowledge Management Research
• /
• v.20 no.2
• /
• pp.87-103
• /
• 2019

The purpose of this study was to explore the factors influencing acceptance (e.g., comprehension,) and diffusion of information recipients' by depending on the authenticity of news. Specifically, this study has examined the effects of the news contents(political vs. general), need for cognition(high vs. low) and authenticity of the News(real news vs. fake news) on both acceptance and diffusion of news. Based on previous work, this study has developed a conceptual model to present each research hypothesis and tested it by conducting experiments as the follows. As a result, according to the authenticity of the news and the contents of the news (political and general), the acceptance of political contents was high regardless of the authenticity of the news, and the acceptance of real news was higher than that of fake news. However, in the proliferation (comment), both the political contents and the general contents showed the characteristic of spreading (commenting) fake news rather than real news. contrary to this, the cognitive level did not show any significant difference in acceptance (understanding) and proliferation (comment, sharing, recommendation). This study provides academic implications in that it examines the influences of accepting (comprehension) and diffusion (comment, sharing, recommendation) of real news and fake news. It also provides practical implications for responding to fake news and new marketing strategies in an environment where contents are delivered through diverse social media.

• Journal of Information Science Theory and Practice
• /
• v.10 no.2
• /
• pp.86-101
• /
• 2022

This research aimed at studying the factors that influence new media exposure of political news by youths in Isan society in Thailand. The target group comprised 1,200 individuals, obtained from multi-stage sampling from undergraduate students in Isan's autonomous universities, governmental universities, and private institutions. The data collection tool was a questionnaire, the content of which was validated by experts. The reliability of the tool was tested by the formula for Cronbach's alpha coefficient, which yielded a reliability of 0.83. Multiple regression analysis was applied to analyze the data. The results, regarding factors influencing the channels for political news exposure, showed that channels for political news exposure were mostly influenced by inner drives, followed by importance in political news exposure, influence from social networks, and specific characteristics of the Internet. This could explain the variation of channels for political news exposure at 46.5%. In terms of factors influencing political news selection, it was found that political news selection was influenced mostly from social networks, followed by inner drives, benefits from political news exposure, specific characteristics of the Internet, and the field of study. The variation of the political news selection could be explained at 44.6%. These results elaborate on the current situation in Thailand, especially in Isan region, where youths in higher education are playing an increasing role in demonstrating their political stance through various political activities.

• Journal of Korea Multimedia Society
• /
• v.23 no.8
• /
• pp.1049-1058
• /
• 2020

'Instagrammable' is a new term which means a photo or a series of pictures are worth posting on Instagram. Since Instagram is an image-oriented social media platform, it is important to give users proper awareness through images in order to be an instagrammable post. In this study, we explored the proper delivery method of messages within instagrammable posts through the use of hashtags(#). Specifically, we paid attention to the use of 'Card News', which involves a series of images that form a short narrative. Hashtags play an important role that they often describe sharing intention of the post, and we found analyzing the use of hashtags in Card News posts is a good indicator of users' Instagram activities. Currently, there are more than 580k posts are found with the search keyword Card News, and the number is increasing. In this study, we collected and analyzed more than 50k hashtags on Instagram to explore how news stories are posted from both the general users and news media accounts. Furthermore, we conducted interviews with journalists to analyze how news media are making use of Instagram as a legitimate place to share news stories with impact.

• Journal of Internet Computing and Services
• /
• v.21 no.1
• /
• pp.87-93
• /
• 2020

The popularity of social media has led to a variety of communicative behaviors among users. This study targeted Facebook as a representative social medial platform because it has the most subscribers in order to investigate factors that influence Facebook usage. In particular, because a person's behavior is based on how they are perceived by others, self-conscious behavior was examined in the study. Facebook usage and news consumption were examined to ascertain the effects of self-consciousness. An online survey was conducted to examine how private SC and public SC (SCs), affects Facebook usage (profiles and writing posts) and news consumption (clicking "like" and sharing news). 616 participants completed the survey, and results indicated that public SC was positively related to the degree of profile updating and post writing. On the other hand, private SC was positively related to the degree of news sharing. These results suggest that psychological elements significantly predict a user's behavior on Facebook.

• The Journal of Information Systems
• /
• v.32 no.2
• /
• pp.177-190
• /
• 2023

Purpose Recently, as a new business marketing tool, short form content focused on fun and interest has been shared as hashtags. By extracting positive and negative keywords from media audiences through comment analysis of social media news, various stakeholders aim to quickly and easily grasp users' opinions on major news. Design/methodology/approach YouTube videos were searched using the YouTube Data API and the results were collected. Video comments were crawled and implemented as HTML elements, and the collection results were checked on the web page. The collected data consisted of video thumbnails, titles, contents, and comments. Comments were word tokenized with the R program, comparing positive and negative dictionaries, and then quantifying polarity. In addition, social network analysis was conducted using divided positive and negative comments, and the results of centrality analysis and visualization were confirmed. Findings Social media users' opinions on issue news were confirmed by analyzing and visualizing the centrality of keywords through social network analysis by dividing comments into positive and negative. As a result of the analysis, it was found that negative objective reviews had the highest effect on information usefulness. In this way, previous studies have been reaffirmed that online negative information has a strong effect on personal decision-making. Corporate marketers will analyze user comments on social network services (SNS) to detect negative opinions about products or corporate images, which will serve as an opportunity to satisfy customers' needs.

• Asian Journal for Public Opinion Research
• /
• v.10 no.1
• /
• pp.51-73
• /
• 2022

This study investigated 3.50 million Facebook reactions collected from 9,429 Bangladeshi news items about rape shared on social media from 2016 to 2021. The primary aim of this study was to understand users' different reaction patterns based on the five major Facebook reactions (i.e., love, haha, wow, sad, and angry). Based on the theories of emotion, we quantitatively answer one research question: How do social media users react to rape with the five major Facebook reactions? The results suggest that users are more likely to express disdain toward rape and sympathy toward the victims using Facebook reactions by using the angry button, along with the sad button. In rape news, both reactions are consistent and maintain a strong positive correlation, meaning they increase and decrease together. Although many users tend to mock and laugh at rape incidents and the victims, trend lines suggest that such expressions may not be consistent with time. Despite contextual relevance, we presume that in socially and morally unacceptable events like rape and war, the valences of reactions alter to some extent: angry and sad usually become positive, while love, wow, and haha become negative. Some strengths and limitations of the study are discussed as well.

• Journal of Intelligence and Information Systems
• /
• v.19 no.4
• /
• pp.39-53
• /
• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• Journal of Intelligence and Information Systems
• /
• v.28 no.1
• /
• pp.197-216
• /
• 2022

With the development of Internet and mobile technology and the spread of social media, a large amount of information is being generated and distributed online. Some of them are useful information for the public, but others are misleading information. The misleading information, so-called 'fake news', has been causing great harm to our society in recent years. Since the global spread of COVID-19 in 2020, much of fake news has been distributed online. Unlike other fake news, fake news related to COVID-19 can threaten people's health and even their lives. Therefore, intelligent technology that automatically detects and prevents fake news related to COVID-19 is a meaningful research topic to improve social health. Fake news related to COVID-19 has spread rapidly through social media, however, there have been few studies in Korea that proposed intelligent fake news detection using the information about how the fake news spreads through social media. Under this background, we propose a novel model that uses Graph2vec, one of the graph embedding methods, to effectively detect fake news related to COVID-19. The mainstream approaches of fake news detection have focused on news content, i.e., characteristics of the text, but the proposed model in this study can exploit information transmission relationships in social engagement networks when detecting fake news related to COVID-19. Experiments using a real-world data set have shown that our proposed model outperforms traditional models from the perspectives of prediction accuracy.

• Journal of Korean Academy of Nursing
• /
• v.47 no.6
• /
• pp.806-816
• /
• 2017

Purpose: As comprehensive nursing care service has gradually expanded, it has become necessary to explore the various opinions about it. The purpose of this study is to explore the large amount of text data regarding comprehensive nursing care service extracted from online news and social media by applying a semantic network analysis. Methods: The web pages of the Korean Nurses Association (KNA) News, major daily newspapers, and Twitter were crawled by searching the keyword 'comprehensive nursing care service' using Python. A morphological analysis was performed using KoNLPy. Nodes on a 'comprehensive nursing care service' cluster were selected, and frequency, edge weight, and degree centrality were calculated and visualized with Gephi for the semantic network. Results: A total of 536 news pages and 464 tweets were analyzed. In the KNA News and major daily newspapers, 'nursing workforce' and 'nursing service' were highly rated in frequency, edge weight, and degree centrality. On Twitter, the most frequent nodes were 'National Health Insurance Service' and 'comprehensive nursing care service hospital.' The nodes with the highest edge weight were 'national health insurance,' 'wards without caregiver presence,' and 'caregiving costs.' 'National Health Insurance Service' was highest in degree centrality. Conclusion: This study provides an example of how to use atypical big data for a nursing issue through semantic network analysis to explore diverse perspectives surrounding the nursing community through various media sources. Applying semantic network analysis to online big data to gather information regarding various nursing issues would help to explore opinions for formulating and implementing nursing policies.