Mapping Digital Conversation Networks of Collector Communities on Platform X: A Study of Blind Box Product Information Diffusion
DOI:
https://doi.org/10.58905/apollo.v4i3.732Keywords:
Social Network Analysis, Social Network Analysis (SNA), Blind Box, Information Diffusion, Collector CommunityAbstract
The blind box phenomenon has created a complex digital conversation ecosystem on Platform X (formerly Twitter), where collector communities exchange information, unboxing experiences, and social validation. This study maps the digital conversation network structure of blind box collector communities on Platform X and traces product information diffusion patterns within it. Data were collected via the SocialX platform using the keywords “blind box” AND (“collector” OR “collection” OR “unboxing” OR “trading”), yielding 995 tweets from 716 unique accounts (January 15 to June 14, 2026), predominantly in English (77.8%) and Indonesian (11.3%). Five analytical methods were integrated: Social Network Analysis (SNA), Dynamic Network Analysis, Trend Analysis, Sentiment Analysis, and Text Network Analysis. The findings reveal a highly fragmented mention network divided into numerous small isolated clusters with content-resharing accounts (such as @youtube) and niche marketplace accounts (such as @chimpershq) emerging as the two dominant hubs. Dynamic network analysis confirmed that both hubs persisted consistently over time, indicating centralized information diffusion concentrated among a small number of actors. Trend analysis identified 11 conversation spikes concentrated in January to February 2026, followed by a significant decline in March to April, before recovering in June 2026. Sentiment analysis showed that community conversations were dominated by positive sentiment (42.71%), followed by neutral (37.59%) and negative (19.70%). Text network analysis revealed that unboxing, collection, series, and secret were the central concepts organizing community discourse. These findings contribute empirically to understanding how niche collector communities form and disseminate information on social media, while offering practical implications for digital marketing strategies targeting collectible product communities.
References
Angus, D., Bruns, A., Hurcombe, E., Harrington, S., & (Jane) Tan, X. Y. (2023). Com-putational Communication Methods for Examining Problematic News-Sharing Practices on Facebook at Scale. Social Media + Society, 9(3), 20563051231196880. https://doi.org/10.1177/20563051231196880
Barton, B., Zlatevska, N., & Oppewal, H. (2022). Scarcity tactics in marketing: A meta-analysis of product scarcity effects on consumer purchase intentions. Journal of Retail-ing, 98(4), 741–758. https://doi.org/10.1016/j.jretai.2022.06.003
Błocki, W., Szewczyk, M., & Adamski, A. (2025). Quantifying Information Distribu-tion in Social Networks: The Structural Entropy Index of Community (SEIC) for Twitter Communication Analysis. Entropy, 27(11), 1140. https://doi.org/10.3390/e27111140
Chapman, A., & Dilmperi, A. (2022). Luxury brand value co-creation with online brand communities in the service encounter. Journal of Business Research, 144, 902–921. https://doi.org/10.1016/j.jbusres.2022.01.068
Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2021). The echo chamber effect on social media. Proceedings of the National Acade-my of Sciences, 118(9), e2023301118. https://doi.org/10.1073/pnas.2023301118
Cruz, E. L. L., Ong, A. K. S., & Tomas, D. Q. (2025). Analyzing the causal effects of product uncertainty and product appeal on repurchase intention in blind box toys. Co-gent Business & Management, 12(1), 2506613. https://doi.org/10.1080/23311975.2025.2506613
Diana, & Muhammad Haldy. (2025). Empowering Engagement: Digital Community Marketing Strategies In The Era Of Interactive Platforms. International Journal of Edu-cation Management and Religion, 3(1), 158–177. https://doi.org/10.71305/ijemr.v3i1.1107
Djunaidi, . (2025). Social Media in Forming Public Opinion in the Era of Disruption. KnE Social Sciences, 10(30), 84–100. https://doi.org/10.18502/kss.v10i30.20332
Gliniecka, M. (2023). The Ethics of Publicly Available Data Research: A Situated Eth-ics Framework for Reddit. Social Media + Society, 9(3). https://doi.org/10.1177/20563051231192021
Gondal, N. (2023). Diffusion of innovations through social networks: Determinants and implications. Sociology Compass, 17(5). https://doi.org/10.1111/soc4.13084
Guédé, B. J.-Y. (2023). From Content Analysis to Content Analysis of Digital Social Networks (pp. 235–248). https://doi.org/10.2991/978-2-494069-25-1_23
Hamilton, R. W., & Shaheen Hosany, A. R. (2023). On the strategic use of product scarcity in marketing. Journal of the Academy of Marketing Science, 51(6), 1203–1213. https://doi.org/10.1007/s11747-023-00976-w
He, K., Liao, J., Li, F., & Sun, H. (2023). Understanding the consumers’ multi-competing brand community engagement: A mix method approach. Frontiers in Psy-chology, 13. https://doi.org/10.3389/fpsyg.2022.1088619
Hickey, A., Grant, M., & Woodward, B. (2025). The connected collector: Collecting in a Web 2.0 world. Convergence: The International Journal of Research into New Media Technologies. https://doi.org/10.1177/13548565251338194
Huang, J. (2024). The Impact of Blind Box Economy on Consumer Behavior in Social Media Networks. Highlights in Business, Economics and Management, 41, 613–619. https://doi.org/10.54097/2ahaej61
Jasim, W. A., Hussein, R. A., Basheer, B. M., Algashamy, H. A. A., & Turovsky, O. (2024). Advanced Network Analysis Techniques for Social Media Study: Unveiling Patterns and Influences in Digital Communities. Journal of Ecohumanism, 3(5), 353–364. https://doi.org/10.62754/joe.v3i5.3911
Kanavos, A., Voutos, Y., Grivokostopoulou, F., & Mylonas, P. (2022). Evaluating Methods for Efficient Community Detection in Social Networks. Information, 13(5), 209. https://doi.org/10.3390/info13050209
Leung, F. F., Gu, F. F., Li, Y., Zhang, J. Z., & Palmatier, R. W. (2022). Influencer Mar-keting Effectiveness. Journal of Marketing, 86(6), 93–115. https://doi.org/10.1177/00222429221102889
Pena, C. B., MacCarron, P., & O’Sullivan, D. J. P. (2025). Finding polarized communi-ties and tracking information diffusion on Twitter: a network approach on the Irish Abortion Referendum. Royal Society Open Science, 12(1). https://doi.org/10.1098/rsos.240454
Pop Mart International Group Limited. (2026). Annual Report Pop Mart 2025. https://share.google/VCUmaH12BUHglRcHt
Rava, G. (2023). The ‘dizziness’ of accumulation: how digital collecting is undermin-ing the very meaning of collection. Punctum. International Journal of Semiotics, 09(01), 171–188. https://doi.org/10.18680/hss.2023.0010
Rueger, J., Dolfsma, W., & Aalbers, R. (2023). Mining and analysing online social networks: Studying the dynamics of digital peer support. MethodsX, 10, 102005. https://doi.org/10.1016/j.mex.2023.102005
Santhiya, P., Kogilavani, S. V., & Malliga, S. (2021). Sentiment Analysis Classifiers for Polarity Detection in Social Media Text: A Comparative Study. 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 1407–1411. https://doi.org/10.1109/ICECA52323.2021.9676111
Saputra, M. A. A., Alamsyah, A., Ramadhani, D. P., Siadari, T. S., & Fakhrurroja, H. (2026). SocialX: A Modular Platform for Multi-Source Big Data Research in Indonesia. http://arxiv.org/abs/2603.26253
Srivastava, M., & Manju, M. (2024). Centrality of any node in social network analysis. Proceedings of the 2024 Sixteenth International Conference on Contemporary Compu-ting, 497–501. https://doi.org/10.1145/3675888.3676100
Vaudrey, R. K. (2022). A practice unpacked: Unboxing as a consumption practice. Journal of Business Research, 145, 843–852. https://doi.org/10.1016/j.jbusres.2022.03.021
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731–5780. https://doi.org/10.1007/s10462-022-10144-1
Whyke, T. W., Chen, Z. T., Lopez-Mugica, J., & Wang, A. (2023). Unboxing the Chi-nese Blind Boxes among China’s grown-up missing children: Probabilistic and elastic prosumption through mediated collection, exchange and resale of figurines. Global Media and China, 8(1), 93–111. https://doi.org/10.1177/20594364221140812
Wirakusuma, B., & Wenerda, I. (2023). The use of platform X as an information media (Qualitative descriptive study on account @Infomieayamyk). Symposium of Literature, Culture, and Communication (SYLECTION) 2022, 3(1), 627. https://doi.org/10.12928/sylection.v3i1.14057
Wu, S. (2024). The Rise of Blind Boxes: Cultural, Marketing, and Consumer Trends Behind Bubble Mart’s Global Success. Finance & Economics, 1(10). https://doi.org/10.61173/nn89jm39
Xia, F., Xu, Y., Zhang, H., & Yuan, X. (2025). The effect of doll blind box uncertainty on consumers’ irrational consumption behavior: the role of instant gratification, Gam-bler’s fallacy, and perceived scarcity. BMC Psychology, 13(1), 332. https://doi.org/10.1186/s40359-025-02644-w
Zade, H., Williams, S., Tran, T. T., Smith, C., Venkatagiri, S., Hsieh, G., & Starbird, K. (2024). To Reply or to Quote: Comparing Conversational Framing Strategies on Twit-ter. ACM Journal on Computing and Sustainable Societies, 2(1), 1–27. https://doi.org/10.1145/3625680
Zhang, Y., Zhou, H., & Qin, J. (2022). Research on the effect of uncertain rewards on impulsive purchase intention of blind box products. Frontiers in Behavioral Neurosci-ence, 16, 946337. https://doi.org/10.3389/fnbeh.2022.946337
Zion Market Research. (2026). Blind Boxes Market Size, Global Trends and Outlook 2034. Https://Www.Zionmarketresearch.Com/Report/Blind-Boxes-Market.
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