Understanding Brand Image from Consumer-generated Hashtags
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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.
Abstract
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.
참고문헌 (28)
- Aaker, D. A., "Measuring brand equity across products and markets", California Management Review, 38(3) (1996)
- Akiva, N., "Mining and Visualizing Online Web Content Using BAM: Brand Association Map TM", ~ (2008)
- Blaszka, M., "#WorldSeries: An empirical examination of a Twitter hashtag during a major sporting event", International Journal of Sport Communication, 5(4), pp.435~453 (2012)
- "#Mac #studentkit #eyeshadow #lippencil #eyebrows #powderblush #makeup #products #makeupfashion #loveurself #instagram" (2020)[URL]
- Dave, K, "Mining the peanut gallery:Opinion extraction and semantic classification of product reviews", Proceedings of the 12th international conference on World Wide Web, pp.519~528 (2003)
- "Why fashion and beauty brands love Instagram" (2015)[URL]
- "The influence of mobile on social marketing’s future" (2010)[URL]
- "46 Mind-Blowing Stats About User-Generated Content (2020Edition)" (2020)[URL]
- Ghiassi, M., "Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network", Expert Systems with Applications, 40(16), pp.6266~6282 (2013)
- "The 57 Instagram Statistics You Need to Know in 2020" (2020)[URL]
- "Instagram Brands:Redefining the Beauty Industry" (2020)[URL]
- Hotho, A., "A brief survey of text mining", Ldv Forum, 20(1), pp.19~62 (2005)
- Jackson, S. J., "Hijacking# myNYPD: Social media dissent and networked counterpublics", Journal of Communication, 65(6), pp.932~952 (2015)
- "Top 10 luxury brand social marketers of 2014" (2014)[URL]
- Lee, T. Y., "Automated marketing research using online customer reviews", Journal of Marketing Research, 48(5), pp.881~894 (2011)
- Liu, Y., "Word of mouth for movies: Its dynamics and impact on box office revenue", Journal of Marketing, 70(3), pp.74~89 (2006)
- Mostafa, M. M., "More than words:Social networks’ text mining for consumer brand sentiments", Expert Systems with Applications, 40(10), pp.4241~4251 (2013)
- "55+ stats about the value of UGC for ecommerce" (2016)[URL]
- Nam, H., "The informational value of social tagging networks", Journal of Marketing, 78(4), pp.21~40 (2014)
- Netzer, O., "Mine your own business:Market-structure surveillance through text mining", Marketing Science, 31(3), pp.521~543 (2012)
- Ramos, J., "Using tf-idf to determine word relevance in document queries", Proceedings of the first instructional conference on machine learning, 242, pp.133~142 (2003)
- Shin, J., "The power of e-WOM using the hashtag: focusing on SNS advertising of SPA brands", International Journal of Advertising, 37(1), pp.71~85 (2018)
- Stathopoulou, A., "Consumer branded#hashtag engagement: can creativity in TV advertising influence hashtag engagement?", Psychology & Marketing, 34(4), pp.448~462 (2017)
- Strohmaier, M, "Why do users tag? Detecting users’motivation for tagging in social tagging systems", Fourth International AAAI Conference on Weblogs and Social Media (2010)
- "Promoted trends" (2013)[URL]
- Weinberg, T., "The new community rules:Marketing on the social web" (2009), O’Reilly
- Zain, S. Z. M, "Derivation of Hashtag (#)Factors for Hashtag Marketing Model (HASHMAM) in Social Media Platform", 2019 6th International Conference on Research and Innovation in Information Systems (ICRIIS), pp.1~6 (2019), IEEE
- Zeithaml, V. A., "Services Marketing" (1996), McGraw Hill