• Journal of Information Science Theory and Practice
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• v.6 no.2
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• pp.46-61
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• 2018

The mobile social networking application Instagram is a well-known platform for sharing photos and videos. Since it is folksonomy-oriented, it provides the possibility for image indexing and knowledge representation through the assignment of hashtags to posted content. The purpose of this study is to analyze how Instagram users tag their pictures regarding different kinds of picture and hashtag categories. For such a content analysis, a distinction is made between Food, Pets, Selfies, Friends, Activity, Art, Fashion, Quotes (captioned photos), Landscape, and Architecture image categories as well as Content-relatedness (ofness, aboutness, and iconology), Emotiveness, Isness, Performativeness, Fakeness, "Insta"-Tags, and Sentences as hashtag categories. Altogether, 14,649 hashtags of 1,000 Instagram images were intellectually analyzed (100 pictures for each image category). Research questions are stated as follows: RQ1: Are there any differences in relative frequencies of hashtags in the picture categories? On average the number of hashtags per picture is 15. Lowest average values received the categories Selfie (average 10.9 tags per picture) and Friends (average 11.7 tags per picture); for highest, the categories Pet (average 18.6 tags), Fashion (average 17.6 tags), and Landscape (average 16.8 tags). RQ2: Given a picture category, what is the distribution of hashtag categories; and given a hashtag category, what is the distribution of picture categories? 60.20% of all hashtags were classified into the category Content-relatedness. Categories Emotiveness (about 4.38%) and Sentences (0.99%) were less often frequent. RQ3: Is there any association between image categories and hashtag categories? A statistically significant association between hashtag categories and image categories on Instagram exists, as a chi-square test of independence shows. This study enables a first broad overview on the tagging behavior of Instagram users and is not limited to a specific hashtag or picture motive, like previous studies.

• Journal of Fashion Business
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• v.27 no.3
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• pp.132-149
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• 2023

This study aims to analyze Instagram hashtags based on big data to investigate changes in consumer trends and perceptions of vegan fashion, and to derive strategies for revitalizing vegan fashion brands based on derived results. Among social media, Instagram was selected as a collection channel, and Instagram hashtags for 'Vegan Fashion' were collected from July 1, 2021 to December 31, 2021. After conducting semantic network analysis with the Ucinet 6 program based on the collected data, the CONCOR analysis on vegan fashion showed the following four clusters: 'Veganism practiced with fashion', 'Bag type of vegan fashion brand', 'Sharing vegan fashion', and 'Diversification of eco-friendly products'. Analysis results showed that the Instagram hashtag for vegan fashion confirmed the MZ generation's increased interest in vegan fashion and their thoughts to recommend and share frequently used items or brand products to people around them. CONCOR analysis of vegan fashion brands showed the following four groups: 'Differentiating the material of vegan bags', 'Eco-friendly products of vegan fashion brands', 'Interest in vegan shoes', and 'Donation campaign of vegan fashion brands'. CONCOR analysis on Meaningout showed the following four clusters: 'MZ Generation's Meaningout Start-up', 'Recommendation Platform for Skin Products', 'Value Consumption Trend for Eco-friendly Clothing', and 'Interest in Eco-friendly Packaging'. The results of this study on vegan fashion, a practical eco-friendly movement that can require changes in social responsibility and perception as issues that directly affect animals, the environment, and humans, are expected to provide basic data to help domestic vegan fashion brands develop marketing strategies.

• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.1
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• pp.1-16
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• 2019

This study explores the effect of hashtag type on consumer response via evaluations of SNS fashion influencer and information in the context of fashion product communication. The moderating effect of perceived fit between hashtag content and fashion product image on information evaluation was also investigated. An online survey was conducted, and responses from 152 Instagram users were collected. The results indicated that hashtag type (informative vs storytelling) affects the evaluations of influencer and information. First, the informative hashtag has a positive effect on influencer's expertise and usefulness of information. Meanwhile, storytelling type has a positive effect on the familiarity of the influencer. This study also verified the mediated paths from hashtag type to consumer response through the evaluations of SNS fashion influencer and information. The influencer's expertise and familiarity mediated the effect of hashtag type on consumer response. In the information evaluation, only the mediating effect of usefulness was significant. Finally, the high condition of the perceived fit between hashtag content and fashion product image was found to highly evaluate the trust of information. This study extends the scope of fashion communication research from various angles by examining the role of the hashtag as a new type of information source.

• Journal of Fashion Business
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• v.22 no.4
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• pp.20-29
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• 2018

Today, visitors of art galleries like to share their life in their communities than interacting with artwork. Meantime, image sharing of an exhibition on social media has become more important than actual watching of the artwork. Accordingly, most of the galleries have started paying more attention in organizing an exhibition environment for proof-shots to attract more visitors. We initially conducted research about the internet environment from the late 1990s to the recent years and analyzed the changing watching patterns of the exhibition since the advent of social media. Secondly, for empirical case analysis, we selected 'Plastic Fantastic' held in D-Museum as the target of analysis. The analysis targeted 500 recent postings that were discovered on Instagram on March 4, 2018, as 'Plastic-Fantastic'(in Korean). The methods of analysis included classification types of image, hashtag, and text on Instagram and were arranged in an order of relation to the exhibits. Based on the image analysis, 44.2% of the images involved exhibition displays; the others included a person or other goods. Based on the results of the text and hashtag analysis, only 3.6% of posting included information about the exhibition and 56.4% had non-related inflow hashtags only with image. The behavior of these shares is likely to gradually lose the inherent meaning of the exhibition and to the value rather than imparting the artistic thrill that viewers derive from art. Exhibition should try to seek deep interaction between the display, audience, and social media users, rather than encouraging the visitors to take proof-shots.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.49-56
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• 2019

The purpose of this study is to analyze the characteristics and meanings of Instagram hashtags related to the exhibitions as a online communication platform of museums. At the same time, it focuses on efficiency of hashtags as a reference framework of inferring viewing experiences. We collect and visualize Instagram hashtags of exhibitions held in Korea for the past two years including 'Paper Present (2017)', 'YOUTH (2017)', 'Monet, Draw Light Exhibition (2018)', 'Van Gogh Inside (2016)', 'Drawn by the Wind: Shin yun-bok & Jeong Seon'. To sum up, significant data related to viewing experiences are not derived, and hashtags as a reference framework of inferring viewing experiences are turned out to be inefficient. Meanwhile, we conclude that potential for distributing information about the exhibitions is inherent in hashtags. In terms of informational characteristics, we notice that the influence of hashtags related to regional information is presented more than the response toward the viewing experiences. This result shows that Instagram users in the exhibitions are worthy of place making rather than viewing experiences.

• The Research Journal of the Costume Culture
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• v.29 no.4
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• pp.603-615
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• 2021

The aim of this study was to analyze the semantic network structure of keywords and the visual composition of images extracted from Instagram in relation to the multi-persona phenomenon with in fashion imagery, which has recently been attracting attention. To this end, the concept of a 'secondary character', which forms a separate identity from a 'main character' on various social media platforms as well as on the airwaves, was considered as the spread of multi-persona and #SecondaryCharacter on Instagram was investigated. 3,801 keywords were collected after crawling the data using Python and morphological analysis was undertaken using KoNLP. The semantic network structure was then examined by conducting a CONCOR analysis using UCINET and Netdraw to determine the top 50 keywords. The results were then classified into a total of 6 clusters. In accordance with the meaning and context of the keywords included in each cluster, group names were assigned : virtual characters, relationship with the main character, hobbies, daily record, N-job person, media and marketing. Image analysis considered the technical, compositional, and social styles of the media based on Gillian Rose's visual analysis method. The results determined that Instagram uses fashion images that virtualize one's face to produce multi-persona representation s that show various occupations, describe different types of hobbies, and depict situations pertaining to various social roles.

• The Research Journal of the Costume Culture
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• v.31 no.4
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• pp.467-488
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• 2023

The purpose of this study is to determine the concepts of heterotopic image and fashion space, and the characteristics of fashion space and images from the perspective of fashion brands and users. This study examines the evolution of fashion space and consumers with it, based on Foucault's theory of heterotopia, which refers to spaces that blend contradictory features not typically found within a single physical structure. This is accomplished by employing a single case study of Ader Error's Ader Space, a Seoul-based brand known for its unique approach to presenting and communicating fashion. Based on an analysis of Instagram posts of Ader Error along with the hashtag searches "aderspace" and "adererror", this study categorizes heterotopia from the perspective of fashion brands into three properties: fashion space as a medium for selling fashion products; fashion space as getaway to hybrid fashion practices; and fashion space as an illusionary place to experience fashion. From the user perspective, the heterotopic image of Ader Space portrayed on Instagram is characterized by the image of fashion products in an extraordinary fashion space, the image of a fashion space beyond space and time, and the image of exposing the hidden and the illusion-compensation of fashion space. This study contributes to a heightened understanding of the evolutionary concept of the fashion space.

• Asia Marketing Journal
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• v.22 no.3
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• pp.71-85
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• 2020

Social media has emerged as a major hub of engagement between brands and consumers in recent years, and allows user-generated content to serve as a powerful means of encouraging communication between the sides. However, it is challenging to negotiate user-generated content owing to its lack of structure and the enormous amount generated. This study focuses on the hashtag, a metadata tag that reflects customers' brand perception through social media platforms. Online users share their knowledge and impressions using a wide variety of hashtags. We examine hashtags that co-occur with particular branded hashtags on the social media platform, Instagram, to derive insights about brand perception. We apply text mining technology and network analysis to identify the perceptions of brand images among consumers on the site, where this helps distinguish among the diverse personalities of the brands. This study contributes to highlighting the value of hashtags in constructing brand personality in the context of online marketing.

• Journal of The Geomorphological Association of Korea
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• v.24 no.1
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• pp.93-104
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• 2017

The objective of this study was two fold: firstly, we analyzed how the Geoparks have been run since the first one had been designated on December 31th, 2015. We then investigated how visitors' geographical and geological recognitions on the parks have changes. We visited geosites and investigated how well these sites accorded with the conditions for running Geoparks. In addition, scenery pictures and hashtags uploaded in Instagram between 2015 and 2016 were collected in order to analyze visitors preferences on the geosites along the, Hantan Imjingang River Geopark. Results showed that the hotspots were Bidulginang Waterall, Art Valley, and Jaein Waterfall. Compared to the ratio of geographical and geological references in 2015, the hashtags in all of these three geosites increased. The increases were as much as 3% in Bidulginang Falls, 0.6% in Art Valley, and 5% in Jaein Falls. In labelling the geographical and geological terms in Bidulginang Falls and Jaein Falls, the most frequently mentioned hashtags was "columnar joint", followed by "natural monument", "Geopark", and "basalt canyon". This study includes the study of visitors recognition which is one of the most important, but somehow neglected factor for the geopark's management.

• The Journal of Korean Institute of Information Technology
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• v.17 no.9
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• pp.123-131
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• 2019

Social network service users actively express and share their feelings about social issues and content of interest through postings. As a result, the sharing of emotions among individuals and community members in social network is spreading rapidly. Therefore, resulting in active research of emotion analysis on posting of users. However, There is insufficient research on emotion analysis for postings containing various emotions. In this paper, we propose a method that analyzes the emotions of an Instagram posts using hashtags and images. This method extracts representative emotion from user posts containing multiple emotions with 66.4% accuracy and 81.7% recall, which improves the emotion classification performance compared to the previous method.