Brand-Related Posts on Social Media: The Effect of Product Type on Consumers’ UGC
Keywords:
UGC, social media, platform differences, utilitarian, hedonic
Abstract
This study examines the role of user-generated content (UGC) in shaping brand engagement across two social media platforms, Instagram and X, with a focus on the interplay between product types (utilitarian vs. hedonic) and platform-specific characteristics. Using Latent Dirichlet Allocation (LDA) for text analysis, the research explores UGC terminology, sentiment, and platform-specific differences to identify key patterns in consumer behavior. Findings indicate that utilitarian products generate more functional UGC, particularly on X, while hedonic products elicit more symbolic and self-expressive content, predominantly on Instagram. Additionally, sentiment analysis reveals a general positivity bias in UGC, with hedonic products receiving more positive sentiment on Instagram than on X. We provide theoretical contributions by linking product type, platform features, and consumer sentiment while offering actionable insights for marketers on tailoring communication strategies to specific platforms and product categories. These results underscore the importance of platform segmentation, sentiment monitoring, and targeted campaigns to optimize consumer engagement in the evolving landscape of social media marketing.
References
[1] Anwar, K., & Mujib, A. (2022). Islamic Faith-Based Content and Religious Social-media Motives. Journal of Ethnic and Cultural Studies, 9(1). https://doi.org/10.29333/ejecs/1024
[2] Basile, V., Brandão, A., & Ferreira, M. (2024). Does user-generated content influence value co-creation in the context of luxury fashion brand communities? Matching inclusivity and exclusivity. Italian Journal of Marketing, 2024(4), 419–444. https://doi.org/10.1007/s43039-024-00103-9
[3] Batchelor, J. (2023). Just another clickbait title: A corpus-driven investigation of negative attitudes toward science on Reddit. Public Understanding of Science, 32(5). https://doi.org/10.1177/09636625221146453
[4] Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993–1022.
[5] Bronner, F., & de Hoog, R. (2019). Comparing conspicuous consumption across different experiential products: Culture and leisure. International Journal of Market Research, 61(4), 430–446. https://doi.org/10.1177/1470785318799898
[6] Burnell, K., George, M. J., Kurup, A. R., & Underwood, M. K. (2021). “Ur a freakin goddess!”: Examining appearance commentary on Instagram. Psychology of Popular Media, 10(4). https://doi.org/10.1037/ppm0000341
[7] Buzeta, C., De Pelsmacker, P., & Dens, N. (2024). Social Media Characteristics and Consumers’ Online Brand-Related Activities (COBRAs): The Mediating Role of Time Spent and Emotional Connectedness. Journal of Interactive Advertising, 24(4). https://doi.org/10.1080/15252019.2024.2411243
[8] Chaudhuri, A., & Ligas, M. (2006). The role of emotion and reason in brand attitude formation. 2006 American Marketing Association Winter Educators’ Conference Proceedings, 17, 195–201.
[9] Chen, R., Xu, T., & Guo, Y. (2024). Filtering a beauty justification: The effect of filtered selfies on preferences for hedonic versus utilitarian products. Marketing Letters, 1–17.
[10] Choi, Y.-H., Yoon, S., Xuan, B., Lee, S.-Y. T., & Lee, K.-H. (2021). Fashion informatics of the Big 4 Fashion Weeks using topic modeling and sentiment analysis. Fashion and Textiles, 8(1). https://doi.org/10.1186/s40691-021-00265-6
[11] Christodoulides, G., Jevons, C., & Bonhomme, J. (2012). Memo to Marketers: Quantitative Evidence for Change: How User-Generated Content Really Affects Brands. Journal of Advertising Research, 52(1), 53–64. https://doi.org/10.2501/JAR-52-1-053-064
[12] Chung, A. Y., Woo, H., & Lee, K. (2020). Understanding the information diffusion of tweets of a non-profit organization that targets female audiences: An examination of Women Who Code’s tweets. Journal of Communication Management, 25(1). https://doi.org/10.1108/jcom-05-2020-0036
[13] Davenport, T. H., & Beck, J. C. (2018). The Attention economy. Ubiquity, 2001(May), 1-es. https://doi.org/10.1145/376625.376626
[14] Díaz, I. A., Kopecký, K., Szotkowski, R., & Rodríguez, J. M. R. (2021). Self-Expression and Addiction: Instagram Use by Czech and Spanish University Students. Plaridel. https://doi.org/10.52518/2020-07azkaro
[15] Esch, F., Langner, T., Schmitt, B. H., & Geus, P. (2006). Are brands forever? How brand knowledge and relationships affect current and future purchases. Journal of Product & Brand Management, 15(2), 98–105. https://doi.org/10.1108/10610420610658938
[16] Garbarino, E. C., & Edell, J. A. (1997). Cognitive Effort, Affect, and Choice. Journal of Consumer Research, 24(2), 147–158. https://doi.org/10.1086/209500
[17] Glatz, T., & Lippold, M. A. (2023). Is more information always better? Associations among parents’ online information searching, information overload, and self-efficacy. International Journal of Behavioral Development, 47(5), 444–453. https://doi.org/10.1177/01650254231190883
[18] Hammad, R., Hardita, V. C., & Amrullah, A. Z. (2022). Topic modeling and sentiment analysis about Mandalika on social media using the latent Dirichlet allocation method. MATRIX : Jurnal Manajemen Teknologi Dan Informatika, 12(3). https://doi.org/10.31940/matrix.v12i3.109-116
[19] Helmi, A., Komaladewi, R., Sarasi, V., & Yolanda, L. (2023). Characterizing Young Consumer Online Shopping Style: Indonesian Evidence. Sustainability, 15(5). https://doi.org/10.3390/su15053988
[20] Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic Consumption: Emerging Concepts, Methods and Propositions. Journal of Marketing, 46(3), 92–101. https://doi.org/10.2307/1251707
[21] Holbrook, M. B. (1986). Aims, concepts, and methods for the representation of individual differences in esthetic responses to design features. Journal of Consumer Research, 13(3), 337–347.
[22] Holbrook, M. B., & Moore, W. L. (1981). Feature Interactions in Consumer Judgments of Verbal Versus Pictorial Presentations. Journal of Consumer Research, 8(1), 103–113. https://doi.org/10.1086/208846
[23] Hu, Y.-C., Tang, Q., Wang, X., & Ali, S. (2022). Determining the critical factors of eWOM about corporate social responsibility on social networking sites: End users’ perspective. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.894505
[24] Jockers, M. (2023). syuzhet: Extracts Sentiment and Sentiment-Derived Plot Arcs from Text (Version 1.0.7) [Computer software]. https://cran.r-project.org/web/packages/syuzhet/index.html
[25] Keller, K. L. (1993). Conceptualizing, Measuring, and Managing Customer-Based Brand Equity. Journal of Marketing, 57(1), 1–22. https://doi.org/10.1177/002224299305700101
[26] Kempf, D. S. (1999). Attitude formation from product trial: Distinct roles of cognition and affect for hedonic and functional products. Psychology & Marketing, 16(1), 35–50. https://doi.org/10.1002/(SICI)1520-6793(199901)16:1<35::AID-MAR3>3.0.CO;2-U
[27] Khalil, E. L. (2000). Symbolic Products: Prestige, Pride and Identity Goods. Theory and Decision, 49(1), 53–77. https://doi.org/10.1023/A:1005223607947
[28] Khan, S. W., & Raza, D. S. (2024). Luxury Brands and Social Media Drivers and Outcomes of Consumer Engagement on Instagram. iRAPA International Journal of Business Studies, 1(1). https://doi.org/10.48112/iijbs.v1i1.776
[29] Kırcaburun, K., Alhabash, S., Tosuntaş, Ş. B., & Griffiths, M. D. (2018). Uses and Gratifications of Problematic Social Media Use Among University Students: A Simultaneous Examination of the Big Five of Personality Traits, Social Media Platforms, and Social Media Use Motives. International Journal of Mental Health and Addiction, 18(3). https://doi.org/10.1007/s11469-018-9940-6
[30] Kovacheva, A., Wiener, H. J. D., Kareklas, I., & Muehling, D. D. (2022). Online Engagement with Memes and Comments about Climate Change. Sustainability, 14(14). https://doi.org/10.3390/su14148900
[31] Krishen, A. S., Berezan, O., & Raab, C. (2019). Feelings and functionality in social networking communities: A regulatory focus perspective. Psychology &Amp; Marketing, 36(7). https://doi.org/10.1002/mar.21204
[32] Kuo, F.-Y., Hsu, C.-W., & Day, R.-F. (2009). An exploratory study of cognitive effort involved in decision under Framing—An application of the eye-tracking technology. Decision Support Systems, 48(1), 81–91. https://doi.org/10.1016/j.dss.2009.06.011
[33] Liu, X., Burns, A. C., & Hou, Y. (2017). An Investigation of Brand-Related User-Generated Content on Twitter. Journal of Advertising, 46(2), 236–247. https://doi.org/10.1080/00913367.2017.1297273
[34] Malhotra, A., Malhotra, C. K., & See, A. (2011). How to Get Your Messages Retweeted. MIT Sloan Management Review. https://sloanreview.mit.edu/article/how-to-get-your-messages-retweeted/
[35] Mano, H., & Oliver, R. L. (1993). Assessing the Dimensionality and Structure of the Consumption Experience: Evaluation, Feeling, and Satisfaction. Journal of Consumer Research, 20(3), 451–466. https://doi.org/10.1086/209361
[36] Maulana, A. S., Andhika F, A., Savina, B., Sayyidul L, F. ti A., & Kurniawan, F. (2022). Instagram Performance Analysis of Micro, Small, and Medium Enterprises: A Case Study in the Fashion Sector. Bulletin of Social Informatics Theory and Application, 6(1). https://doi.org/10.31763/businta.v6i1.439
[37] Micu, C. C., Chowdhury, T., Micu, A., & Chaudhuri, A. (2017). What do Consumers Tweet about? The Role of Product Type and Brand Belief Type on User-Generated Content. 6(2).
[38] Miles, J. (2013). Instagram power: Build your brand and reach more customers with the power of pictures. McGraw Hill Professional.
[39] Min, K. B., Song, S. H., & Min, J. Y. (2020). Topic Modeling of Social Networking Service Data on Occupational Accidents in Korea: Latent Dirichlet Allocation Analysis. Journal of Medical Internet Research, 22(8). https://doi.org/10.2196/19222
[40] Mishra, A., & Maheswarappa, S. S. (2019). How Content Valence and Online Impression Influence Goal Achievement in Social Media Environment? Global Business Review, 20(5), 1267–1281. https://doi.org/10.1177/0972150918793964
[41] Muda, M., & Hamzah, M. I. (2021). Should I suggest this YouTube clip? The impact of UGC source credibility on eWOM and purchase intention. Journal of Research in Interactive Marketing, 15(3). https://doi.org/10.1108/jrim-04-2020-0072
[42] Nandwani, P., & Verma, R. (2021). A review on sentiment analysis and emotion detection from text. Social Network Analysis and Mining, 11(1), 81. https://doi.org/10.1007/s13278-021-00776-6
[43] Neher, K. (2013). Visual social media marketing: Harnessing images, Instagram, Infographics and Pinterest to grow your business online. Boot Camp Pub.
[44] Nikjoo, A., Shabani, B., & Hernández-Lara, A. B. (2021). What do people share from quarantine? Current Issues in Tourism, 24(14), 1965–1969. https://doi.org/10.1080/13683500.2020.1808598
[45] Rietveld, R., Dolen, W. van, Mazloom, M., & Worring, M. (2020). What you Feel, Is what you like Influence of Message Appeals on Customer Engagement on Instagram. Journal of Interactive Marketing, 49(1). https://doi.org/10.1016/j.intmar.2019.06.003
[46] Saini, V., Liang, L.-L., Yang, Y.-C., Huong, L. M., & Wu, C. Y. (2022). The Association Between Dissemination and Characteristics of Pro-/Anti-COVID-19 Vaccine Messages on Twitter: Application of the Elaboration Likelihood Model. JMIR Infodemiology, 2(1). https://doi.org/10.2196/37077
[47] Sajedikhah, A., Rezaei Dolatabadi, H., & Shahin, A. (2023). Historical effect of verified purchases and ordinary users’ testimonials on the sales rank of experience and search goods. Competitiveness Review: An International Business Journal, 34(4). https://doi.org/10.1108/cr-11-2022-0173
[48] Schanke, S., Ray, G., Adomavicius, G., & Wagle, M. (2023). Content and Style of Firm-Generated Posts on Social Media: A Study of User Engagement on Hedonic and Utilitarian Product Pages on Facebook. Journal of the Association for Information Systems, 25(2), 341–361.
[49] Smith, A. N., Fischer, E., & Yongjian, C. (2012). How Does Brand-related User-generated Content Differ across YouTube, Facebook, and Twitter? Journal of Interactive Marketing, 26(2), 102–113. https://doi.org/10.1016/j.intmar.2012.01.002
[50] Soonius, G. (2012). Facebook strategies: How to measure campaign success. Erasmus University, Rotterdam. https://clementlevallois.net/download/2012_01.pdf
[51] Strahilevitz, M., & Myers, J. G. (1998). Donations to Charity as Purchase Incentives: How Well They Work May Depend on What You Are Trying to Sell. Journal of Consumer Research, 24(4), 434–446. https://doi.org/10.1086/209519
[52] Sukmana, R., & Rusydiana, A. S. (2023). Social Media Sentiment Analysis on Waqf and Education. Islamic Marketing Review, 2(2). https://doi.org/10.58968/imr.v2i2.325
[53] Susanti, F., & Rodhiah. (2023). Customer Loyalty to “X” Brand Instagram Accounts. International Journal of Academic Research in Accounting, Finance and Management Sciences, 13(1). https://doi.org/10.6007/ijarafms/v13-i1/16680
[54] Team, R. C. (2013). R: A language and environment for statistical computing. Foundation for Statistical Computing, Vienna, Austria.
[55] Vinuales, G., & Thomas, V. L. (2020). Not so social: When social media increases perceptions of exclusions and negatively affects attitudes toward content. Psychology &Amp; Marketing, 38(2). https://doi.org/10.1002/mar.21339
[56] We Are Social, & DataReportal, & Meltwater. (2024, April). Most popular social networks worldwide as of April 2024, by number of monthly active users. Statista. https://www-statista-com.sacredheart.idm.oclc.org/statistics/272014/global-social-networks-ranked-by-number-of-users/
[57] Wei, L. (2024). User-Generate-Content Behaviours Shape Brand Loyalty: A Self-determination Theory Perspective. Journal of Education, Humanities and Social Sciences, 27, 287–292. https://doi.org/10.54097/mzf86j96
[58] Wicaksono, T. B. (2024). From Likes to Purchases: Analyzing the Impact of Instagram Marketing on Online Shoppers. Journal of Business, Management, and Social Studies, 3(2). https://doi.org/10.53748/jbms.v3i2.73
[59] Woods, W. A. (1960). Psychological Dimensions of Consumer Decision. Journal of Marketing, 24(3), 15–19. https://doi.org/10.1177/002224296002400303
[60] X Developer Platform. (2023). Counting characters when composing Tweets. https://developer.twitter.com/en/docs/counting-characters
[61] Xue, J., Chen, J., Chen, C., Zheng, C., Li, S., & Zhu, T. (2020). Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter. Plos One, 15(9). https://doi.org/10.1371/journal.pone.0239441
[62] Zhao, Y., Huang, X., & Zhang, C. (2021). Exploring Occupation Differences in Reactions to COVID-19 Pandemic on Twitter. Data and Information Management, 5(1). https://doi.org/10.2478/dim-2020-0032
[63] Zou, X., Yang, J., & Zhang, J. (2018). Microblog sentiment analysis using social and topic context. Plos One, 13(2). https://doi.org/10.1371/journal.pone.0191163
[2] Basile, V., Brandão, A., & Ferreira, M. (2024). Does user-generated content influence value co-creation in the context of luxury fashion brand communities? Matching inclusivity and exclusivity. Italian Journal of Marketing, 2024(4), 419–444. https://doi.org/10.1007/s43039-024-00103-9
[3] Batchelor, J. (2023). Just another clickbait title: A corpus-driven investigation of negative attitudes toward science on Reddit. Public Understanding of Science, 32(5). https://doi.org/10.1177/09636625221146453
[4] Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993–1022.
[5] Bronner, F., & de Hoog, R. (2019). Comparing conspicuous consumption across different experiential products: Culture and leisure. International Journal of Market Research, 61(4), 430–446. https://doi.org/10.1177/1470785318799898
[6] Burnell, K., George, M. J., Kurup, A. R., & Underwood, M. K. (2021). “Ur a freakin goddess!”: Examining appearance commentary on Instagram. Psychology of Popular Media, 10(4). https://doi.org/10.1037/ppm0000341
[7] Buzeta, C., De Pelsmacker, P., & Dens, N. (2024). Social Media Characteristics and Consumers’ Online Brand-Related Activities (COBRAs): The Mediating Role of Time Spent and Emotional Connectedness. Journal of Interactive Advertising, 24(4). https://doi.org/10.1080/15252019.2024.2411243
[8] Chaudhuri, A., & Ligas, M. (2006). The role of emotion and reason in brand attitude formation. 2006 American Marketing Association Winter Educators’ Conference Proceedings, 17, 195–201.
[9] Chen, R., Xu, T., & Guo, Y. (2024). Filtering a beauty justification: The effect of filtered selfies on preferences for hedonic versus utilitarian products. Marketing Letters, 1–17.
[10] Choi, Y.-H., Yoon, S., Xuan, B., Lee, S.-Y. T., & Lee, K.-H. (2021). Fashion informatics of the Big 4 Fashion Weeks using topic modeling and sentiment analysis. Fashion and Textiles, 8(1). https://doi.org/10.1186/s40691-021-00265-6
[11] Christodoulides, G., Jevons, C., & Bonhomme, J. (2012). Memo to Marketers: Quantitative Evidence for Change: How User-Generated Content Really Affects Brands. Journal of Advertising Research, 52(1), 53–64. https://doi.org/10.2501/JAR-52-1-053-064
[12] Chung, A. Y., Woo, H., & Lee, K. (2020). Understanding the information diffusion of tweets of a non-profit organization that targets female audiences: An examination of Women Who Code’s tweets. Journal of Communication Management, 25(1). https://doi.org/10.1108/jcom-05-2020-0036
[13] Davenport, T. H., & Beck, J. C. (2018). The Attention economy. Ubiquity, 2001(May), 1-es. https://doi.org/10.1145/376625.376626
[14] Díaz, I. A., Kopecký, K., Szotkowski, R., & Rodríguez, J. M. R. (2021). Self-Expression and Addiction: Instagram Use by Czech and Spanish University Students. Plaridel. https://doi.org/10.52518/2020-07azkaro
[15] Esch, F., Langner, T., Schmitt, B. H., & Geus, P. (2006). Are brands forever? How brand knowledge and relationships affect current and future purchases. Journal of Product & Brand Management, 15(2), 98–105. https://doi.org/10.1108/10610420610658938
[16] Garbarino, E. C., & Edell, J. A. (1997). Cognitive Effort, Affect, and Choice. Journal of Consumer Research, 24(2), 147–158. https://doi.org/10.1086/209500
[17] Glatz, T., & Lippold, M. A. (2023). Is more information always better? Associations among parents’ online information searching, information overload, and self-efficacy. International Journal of Behavioral Development, 47(5), 444–453. https://doi.org/10.1177/01650254231190883
[18] Hammad, R., Hardita, V. C., & Amrullah, A. Z. (2022). Topic modeling and sentiment analysis about Mandalika on social media using the latent Dirichlet allocation method. MATRIX : Jurnal Manajemen Teknologi Dan Informatika, 12(3). https://doi.org/10.31940/matrix.v12i3.109-116
[19] Helmi, A., Komaladewi, R., Sarasi, V., & Yolanda, L. (2023). Characterizing Young Consumer Online Shopping Style: Indonesian Evidence. Sustainability, 15(5). https://doi.org/10.3390/su15053988
[20] Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic Consumption: Emerging Concepts, Methods and Propositions. Journal of Marketing, 46(3), 92–101. https://doi.org/10.2307/1251707
[21] Holbrook, M. B. (1986). Aims, concepts, and methods for the representation of individual differences in esthetic responses to design features. Journal of Consumer Research, 13(3), 337–347.
[22] Holbrook, M. B., & Moore, W. L. (1981). Feature Interactions in Consumer Judgments of Verbal Versus Pictorial Presentations. Journal of Consumer Research, 8(1), 103–113. https://doi.org/10.1086/208846
[23] Hu, Y.-C., Tang, Q., Wang, X., & Ali, S. (2022). Determining the critical factors of eWOM about corporate social responsibility on social networking sites: End users’ perspective. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.894505
[24] Jockers, M. (2023). syuzhet: Extracts Sentiment and Sentiment-Derived Plot Arcs from Text (Version 1.0.7) [Computer software]. https://cran.r-project.org/web/packages/syuzhet/index.html
[25] Keller, K. L. (1993). Conceptualizing, Measuring, and Managing Customer-Based Brand Equity. Journal of Marketing, 57(1), 1–22. https://doi.org/10.1177/002224299305700101
[26] Kempf, D. S. (1999). Attitude formation from product trial: Distinct roles of cognition and affect for hedonic and functional products. Psychology & Marketing, 16(1), 35–50. https://doi.org/10.1002/(SICI)1520-6793(199901)16:1<35::AID-MAR3>3.0.CO;2-U
[27] Khalil, E. L. (2000). Symbolic Products: Prestige, Pride and Identity Goods. Theory and Decision, 49(1), 53–77. https://doi.org/10.1023/A:1005223607947
[28] Khan, S. W., & Raza, D. S. (2024). Luxury Brands and Social Media Drivers and Outcomes of Consumer Engagement on Instagram. iRAPA International Journal of Business Studies, 1(1). https://doi.org/10.48112/iijbs.v1i1.776
[29] Kırcaburun, K., Alhabash, S., Tosuntaş, Ş. B., & Griffiths, M. D. (2018). Uses and Gratifications of Problematic Social Media Use Among University Students: A Simultaneous Examination of the Big Five of Personality Traits, Social Media Platforms, and Social Media Use Motives. International Journal of Mental Health and Addiction, 18(3). https://doi.org/10.1007/s11469-018-9940-6
[30] Kovacheva, A., Wiener, H. J. D., Kareklas, I., & Muehling, D. D. (2022). Online Engagement with Memes and Comments about Climate Change. Sustainability, 14(14). https://doi.org/10.3390/su14148900
[31] Krishen, A. S., Berezan, O., & Raab, C. (2019). Feelings and functionality in social networking communities: A regulatory focus perspective. Psychology &Amp; Marketing, 36(7). https://doi.org/10.1002/mar.21204
[32] Kuo, F.-Y., Hsu, C.-W., & Day, R.-F. (2009). An exploratory study of cognitive effort involved in decision under Framing—An application of the eye-tracking technology. Decision Support Systems, 48(1), 81–91. https://doi.org/10.1016/j.dss.2009.06.011
[33] Liu, X., Burns, A. C., & Hou, Y. (2017). An Investigation of Brand-Related User-Generated Content on Twitter. Journal of Advertising, 46(2), 236–247. https://doi.org/10.1080/00913367.2017.1297273
[34] Malhotra, A., Malhotra, C. K., & See, A. (2011). How to Get Your Messages Retweeted. MIT Sloan Management Review. https://sloanreview.mit.edu/article/how-to-get-your-messages-retweeted/
[35] Mano, H., & Oliver, R. L. (1993). Assessing the Dimensionality and Structure of the Consumption Experience: Evaluation, Feeling, and Satisfaction. Journal of Consumer Research, 20(3), 451–466. https://doi.org/10.1086/209361
[36] Maulana, A. S., Andhika F, A., Savina, B., Sayyidul L, F. ti A., & Kurniawan, F. (2022). Instagram Performance Analysis of Micro, Small, and Medium Enterprises: A Case Study in the Fashion Sector. Bulletin of Social Informatics Theory and Application, 6(1). https://doi.org/10.31763/businta.v6i1.439
[37] Micu, C. C., Chowdhury, T., Micu, A., & Chaudhuri, A. (2017). What do Consumers Tweet about? The Role of Product Type and Brand Belief Type on User-Generated Content. 6(2).
[38] Miles, J. (2013). Instagram power: Build your brand and reach more customers with the power of pictures. McGraw Hill Professional.
[39] Min, K. B., Song, S. H., & Min, J. Y. (2020). Topic Modeling of Social Networking Service Data on Occupational Accidents in Korea: Latent Dirichlet Allocation Analysis. Journal of Medical Internet Research, 22(8). https://doi.org/10.2196/19222
[40] Mishra, A., & Maheswarappa, S. S. (2019). How Content Valence and Online Impression Influence Goal Achievement in Social Media Environment? Global Business Review, 20(5), 1267–1281. https://doi.org/10.1177/0972150918793964
[41] Muda, M., & Hamzah, M. I. (2021). Should I suggest this YouTube clip? The impact of UGC source credibility on eWOM and purchase intention. Journal of Research in Interactive Marketing, 15(3). https://doi.org/10.1108/jrim-04-2020-0072
[42] Nandwani, P., & Verma, R. (2021). A review on sentiment analysis and emotion detection from text. Social Network Analysis and Mining, 11(1), 81. https://doi.org/10.1007/s13278-021-00776-6
[43] Neher, K. (2013). Visual social media marketing: Harnessing images, Instagram, Infographics and Pinterest to grow your business online. Boot Camp Pub.
[44] Nikjoo, A., Shabani, B., & Hernández-Lara, A. B. (2021). What do people share from quarantine? Current Issues in Tourism, 24(14), 1965–1969. https://doi.org/10.1080/13683500.2020.1808598
[45] Rietveld, R., Dolen, W. van, Mazloom, M., & Worring, M. (2020). What you Feel, Is what you like Influence of Message Appeals on Customer Engagement on Instagram. Journal of Interactive Marketing, 49(1). https://doi.org/10.1016/j.intmar.2019.06.003
[46] Saini, V., Liang, L.-L., Yang, Y.-C., Huong, L. M., & Wu, C. Y. (2022). The Association Between Dissemination and Characteristics of Pro-/Anti-COVID-19 Vaccine Messages on Twitter: Application of the Elaboration Likelihood Model. JMIR Infodemiology, 2(1). https://doi.org/10.2196/37077
[47] Sajedikhah, A., Rezaei Dolatabadi, H., & Shahin, A. (2023). Historical effect of verified purchases and ordinary users’ testimonials on the sales rank of experience and search goods. Competitiveness Review: An International Business Journal, 34(4). https://doi.org/10.1108/cr-11-2022-0173
[48] Schanke, S., Ray, G., Adomavicius, G., & Wagle, M. (2023). Content and Style of Firm-Generated Posts on Social Media: A Study of User Engagement on Hedonic and Utilitarian Product Pages on Facebook. Journal of the Association for Information Systems, 25(2), 341–361.
[49] Smith, A. N., Fischer, E., & Yongjian, C. (2012). How Does Brand-related User-generated Content Differ across YouTube, Facebook, and Twitter? Journal of Interactive Marketing, 26(2), 102–113. https://doi.org/10.1016/j.intmar.2012.01.002
[50] Soonius, G. (2012). Facebook strategies: How to measure campaign success. Erasmus University, Rotterdam. https://clementlevallois.net/download/2012_01.pdf
[51] Strahilevitz, M., & Myers, J. G. (1998). Donations to Charity as Purchase Incentives: How Well They Work May Depend on What You Are Trying to Sell. Journal of Consumer Research, 24(4), 434–446. https://doi.org/10.1086/209519
[52] Sukmana, R., & Rusydiana, A. S. (2023). Social Media Sentiment Analysis on Waqf and Education. Islamic Marketing Review, 2(2). https://doi.org/10.58968/imr.v2i2.325
[53] Susanti, F., & Rodhiah. (2023). Customer Loyalty to “X” Brand Instagram Accounts. International Journal of Academic Research in Accounting, Finance and Management Sciences, 13(1). https://doi.org/10.6007/ijarafms/v13-i1/16680
[54] Team, R. C. (2013). R: A language and environment for statistical computing. Foundation for Statistical Computing, Vienna, Austria.
[55] Vinuales, G., & Thomas, V. L. (2020). Not so social: When social media increases perceptions of exclusions and negatively affects attitudes toward content. Psychology &Amp; Marketing, 38(2). https://doi.org/10.1002/mar.21339
[56] We Are Social, & DataReportal, & Meltwater. (2024, April). Most popular social networks worldwide as of April 2024, by number of monthly active users. Statista. https://www-statista-com.sacredheart.idm.oclc.org/statistics/272014/global-social-networks-ranked-by-number-of-users/
[57] Wei, L. (2024). User-Generate-Content Behaviours Shape Brand Loyalty: A Self-determination Theory Perspective. Journal of Education, Humanities and Social Sciences, 27, 287–292. https://doi.org/10.54097/mzf86j96
[58] Wicaksono, T. B. (2024). From Likes to Purchases: Analyzing the Impact of Instagram Marketing on Online Shoppers. Journal of Business, Management, and Social Studies, 3(2). https://doi.org/10.53748/jbms.v3i2.73
[59] Woods, W. A. (1960). Psychological Dimensions of Consumer Decision. Journal of Marketing, 24(3), 15–19. https://doi.org/10.1177/002224296002400303
[60] X Developer Platform. (2023). Counting characters when composing Tweets. https://developer.twitter.com/en/docs/counting-characters
[61] Xue, J., Chen, J., Chen, C., Zheng, C., Li, S., & Zhu, T. (2020). Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter. Plos One, 15(9). https://doi.org/10.1371/journal.pone.0239441
[62] Zhao, Y., Huang, X., & Zhang, C. (2021). Exploring Occupation Differences in Reactions to COVID-19 Pandemic on Twitter. Data and Information Management, 5(1). https://doi.org/10.2478/dim-2020-0032
[63] Zou, X., Yang, J., & Zhang, J. (2018). Microblog sentiment analysis using social and topic context. Plos One, 13(2). https://doi.org/10.1371/journal.pone.0191163
Published
2025-11-29
How to Cite
Micu, D., Xia, F., & Micu, C. (2025). Brand-Related Posts on Social Media: The Effect of Product Type on Consumers’ UGC. Review of Applied Socio-Economic Research, 30(2), 117-128. https://doi.org/10.54609/reaser.v30i2.837
Section
Articles
Copyright (c) 2025 Review of Applied Socio-Economic Research
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright ©2019 Pro Global Science Association
All rights reserved. No part of this publication may be reproduced, stored, transmitted or disseminated, in any form, or by any means, without prior written permission from Pro Global Science Association, to whom all requests to produce copyright material should be directed, in writing.
