社交媒体流媒体中的韩国流行文化分析:来自泰国YouTube频道的证据

Wirapong Chansanam, Kulthida Tuamsuk, Kanyarat Kwiecien, Sam Oh
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引用次数: 3

摘要

本研究旨在研究和分析韩国流行文化(K-pop)对泰国社会的影响和影响。在这项研究中,我们使用社交网络分析(SNA)来分析来自世界各地YouTube频道的各种YouTube频道的流媒体数据,文本分析来分析人口统计学特征,YouTube的演示技术,以及订阅者行为,以及多重相关性分析来分析影响泰国YouTube频道的因素之间的关系。调查结果显示,五个泰国YouTube频道正在影响泰国社会。此外,不喜欢的数量和评论的数量(0.79)以及喜欢和评论的数量(0.65)之间存在显著的正相关。此外,观看次数与不喜欢次数呈正相关,喜欢次数与不喜欢次数呈正相关。未来的研究可以用其他社交媒体来源补充目前的研究结果,以产生更多样化和全面的分析。这些分析可以应用于各种情况,包括企业营销策略、政治活动或医学上的疾病/症状分析。这项研究通过揭示社会网络中的智能趋势扩展到社会计算。
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Korean popular culture analytics in social media streaming: evidence from YouTube channels in Thailand
This research aimed to study and analyze the influence and impact of Korean popular culture (K-pop) on Thai society. In this study, we used Social Network Analysis (SNA) to analyze streaming data obtained from a variety of YouTube channels belonging to YouTubers across the world, text analytics to analyze demographic characteristics, YouTuber's presentation techniques, as well as subscriber behavior, and multiple correlations analysis to analyze the relationship between factors affecting YouTube Channels in Thailand. The findings revealed that five Thai YouTube Channels were influencing Thai society. Furthermore, there were robust positive correlations between the number of dislikes and the number of comments (0.79), and the number of likes and comments (0.65). Additionally, there was a positive correlation between the number of views and the number of dislikes and one between the number of likes and dislikes. Future research can supplement the present findings with other social media sources to yield an even more diverse and comprehensive analysis. These analytics can be applied to various situations, including corporate marketing strategies, political campaigns, or disease/symptom analysis in medicine. This research extends to social computing by revealing intelligent trends in social networks.
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来源期刊
International Journal of Advances in Intelligent Informatics
International Journal of Advances in Intelligent Informatics Computer Science-Computer Vision and Pattern Recognition
CiteScore
3.00
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0.00%
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0
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