• Informatization Policy
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• v.29 no.3
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• pp.82-104
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• 2022

One of the interesting social phenomena in short-form video platforms is the hashtag challenge wherein ordinary users are encouraged to create by imitating short viral videos on a particular theme. Despite the increasing popularity of hashtag challenges, theoretical discussion on related user behavior is still very insufficient. In this study, we attempted to examine the impact of micro-influencers in order to understand users' willingness to participate in hashtag challenges. For this purpose, the para-social interaction theory and imitation behavior literature were adopted as key theoretical basis. In an empirical investigation using 243 survey data from TikTok users, our study found that a user's illusion of intimacy with a micro-influencer (i.e., para-social interaction) had significant positive impact on the intention to participate in a hashtag challenge. This study also showed that the degree of para-social interaction in a short-form video platform was determined by both media content-related factors and media character-related factors (i.e., content attractiveness, physical attractiveness, and attitude homophily). Our work in this study provided significant theoretical and practical implications on how to leverage micro-influencers for the success of hashtag challenges in a short-form video platform.

• Journal of the Korean Society of Food Culture
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• v.23 no.3
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• pp.403-409
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• 2008

Recently, the desire for low body weight, which is an abnormal weight construct along with obesity, has become an evident and serious problem in teenagers. In Korea, the desire for low weight is not perceived as an important problem, but it is rapidly expanding relative to the physical changes and developmental issues teenagers experience. The social atmosphere presented through mass media is the key influencer for the increasing low weight occurrence in teenagers. Because thoughts about beauty have changed among people, and since there is apparent blind interest in slim body shape and appearance, already low-weight individuals are attempting to lose weight along with obese persons. Thus, we consider it necessary to guide teenagers toward having correct perceptions with regard to weight and their own body shape, and that a healthy and appropriate weight is beautiful. Therefore, for this study, we investigated body perception, abnormal weight, attitude toward weight control, and factors related to eating behavior among teenage girls, who are considered the at risk group for overt body weight control behavior. Based on this, we have attempted to set in motion a systematic and active nutrition education program that will allow us to increase body satisfaction by educating on nutritional issues related to development, and ultimately, implant healthy body shape perceptions.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.