• Journal of Digital Convergence
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• v.20 no.4
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• pp.565-573
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• 2022

This study aims to analyze the preference factors of the Korean MZ generation with regards to the online programs developed by museums abroad during the COVID-19 pandemic. World renown museums such as the British Museum, the National Gallery of London, Van Gogh Museum, J. Paul Getty Museum, Hasting Contemporary, Uffizi Gallery, and the Guggenheim Museum tackled the social-distancing situation with various creative online programs and events to continue their role as socially relevant institutions. Ten acclaimed programs conducted by these museums were shown to the Korean MZ generation, the most digitally savvy and frequent visitors to museums, to extract their responses to the various types of programs. The study showed that the Korean MZ generation prefer online programs which most closely reflect the onsite experience of a museum, and online contents with educational elements.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.329-346
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• 2022

Recently, as the market for short form videos (Instagram, TikTok, YouTube) on social media has gradually increased, research using them is actively being conducted in the artificial intelligence field. A representative research field is Video to Shop, which detects fashion products in videos and searches for product images. In such a video-based artificial intelligence model, product features are extracted using convolution operations. However, due to the limitation of computational resources, extracting features using all the frames in the video is practically impossible. For this reason, existing studies have improved the model's performance by sampling only a part of the entire frame or developing a sampling method using the subject's characteristics. In the existing Video to Shop study, when sampling frames, some frames are randomly sampled or sampled at even intervals. However, this sampling method degrades the performance of the fashion product search model while sampling noise frames where the product does not exist. Therefore, this paper proposes a sampling method MF (Missing Fashion items on frame) sampler that removes noise frames and improves the performance of the search model. MF sampler has improved the problem of resource limitations by developing a keyframe mechanism. In addition, the performance of the search model is improved through noise frame removal using the noise detection model. As a result of the experiment, it was confirmed that the proposed method improves the model's performance and helps the model training to be effective.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.25-30
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• 2022

This study is a study on the filter bubble reinforcement phenomenon of SNS recommendation algorithm such as YouTube, which is a characteristic of the Russian-Ukraine war (2022), and the victory or defeat factors of the hybrid war. This war is identified as a hybrid war, and the use of New Media based on the SNS recommendation algorithm is emerging as a factor that determines the outcome of the war beyond political leverage. For this reason, the filter bubble phenomenon goes beyond the dictionary meaning of confirmation bias that limits information exposed to viewers. A YouTube video of Ukrainian President Zelensky encouraging protests in Kyiv garnered 7.02 million views, but Putin's speech only 800,000, which is a evidence that his speech was not exposed to the recommendation algorithm. The war of these SNS recommendation algorithms tends to develop into an algorithm war between the US (YouTube, Twitter, Facebook) and China (TikTok) big tech companies. Influenced by US companies, Ukraine is now able to receive international support, and in Russia, under the influence of Chinese companies, Putin's approval rating is over 80%, resulting in conflicting results. Since this algorithmic empowerment is based on the confirmation bias of public opinion by 'filter bubble', the justification that a new guideline setting for this distortion phenomenon should be presented shortly is drawing attention through this Russia-Ukraine war.

• Trans-
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• v.15
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• pp.57-79
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• 2023

This study aims to explore the strategies and effects of SNS images used by four popular male beauty influencers to gain identification with their female fans. The research selected four male beauty influencers, namely Li Jiaqi, Jeffree Star, James Charles, and Bretman Rock, with a high number of subscribers on Instagram, YouTube, and TikTok as of July 21, 2023. By observing the content they posted on SNS, the study analyzed the types, characteristics, and relevance of male beauty influencer images with their female fans using Lacan's gaze theory. Additionally, concepts related to gaze, such as the mirror stage, the screen, and objet petit a, were supplemented to conduct an in-depth analysis of the characteristics of male beauty influencer images and the motivations of female viewers. The study results suggest that male beauty influencers can maintain an intimate relationship, referred to as 'girl-friendship,' with their female fans through the identification formed by the homogeneity within the feminized mirror images. Furthermore, male beauty influencers can transform female viewers from being seen as objects to seeing them as subjects by presenting images that embrace diversity in gender identity, challenging the traditional notions of societal gender norms. Therefore, the images of male beauty influencers not only challenge gender stereotypes but also cater to the demands for independence and equality of modern young women, promote understanding of feminine gaze, and explore the potential for democratization and inclusivity on social media platforms from a new perspective.