Hyperlocal Business Patterns in Indonesia: A Hashtag-Based Machine Learning Study of Top Metropolitan Areas
Article Sidebar
Main Article Content
Abstract
This study explores how hyperlocal businesses in Indonesia's metropolitan areas construct their digital identities through hashtags on social media, specifically Instagram. The research aims to identify spatial and sectoral variations of digitally expressed business activity by analyzing the top 30 hashtags from five major cities: Jakarta, Bandung, Surabaya, Yogyakarta, and Medan. Data was collected via Tagsfinder.com and classified using a supervised machine learning model based on the frequency-inverse document frequency (TF-IDF) to distinguish business-related hashtags from non-business ones. The results show that out of 150 total hashtags, 42.7% were classified as business-related, with Bandung showing the highest proportion (56.6%) and Yogyakarta the lowest (20%). Sectorally, Bandung stands out in fashion and apparel, while Medan shows strong presence in the jewellery sector (e.g., #cincinnikah, #cincinmedan) and Surabaya with culinary sector. These findings affirm previous literature on hashtags as proxies for economic activity and address a significant research gap in the Global South context. In conclusion, social media hashtags serve not only as tools for promotion but also as spatial and cultural markers of urban business identity, providing new insights for digitally mapping hyperlocal urban economies in Indonesia.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Castells, M. (2010). The Rise of the Network Society (2nd ed.). Wiley-Blackwell.
Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage Publications.
Da Silva, F. N., & Devezas, T. C. (2019). Mapping Innovation Clusters Using Twitter Data and Hashtag Analysis. Technological Forecasting and Social Change, 145, 155–165. https://doi.org/10.1016/j.techfore.2019.04.016
DataReportal. (2024). Digital 2024: Indonesia. Retrieved from https://datareportal.com/reports/digital-2024-indonesia
Dillahunt, T. R., & Malone, A. R. (2015). The Promise of the Sharing Economy among Disadvantaged Communities. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 2285–2294.
Frias-Martinez, V., & Frias-Martinez, E. (2014). Spectral clustering for sensing urban land use. IEEE Transactions on Big Data, 1(2), 89–98. http://dx.doi.org/10.1016/j.engappai.2014.06.019
Ginting, N., & Sasmoko. (2020). Adoption of e-commerce by SMEs in Indonesia: Influence of external and internal factors. International Journal of Innovation, Creativity and Change, 13(1), 44–58.
González, M. C., Hidalgo, C. A., & Barabási, A.-L. (2020). Understanding individual human mobility patterns using social media data. Nature Communications, 11, 1160. https://doi.org/10.1038/nature06958
Graham, M., Zook, M., & Boulton, A. (2013). Augmented reality in urban places: contested content and the duplicity of code. Transactions of the Institute of British Geographers, 38(3), 464–479. https://doi.org/10.1111/j.1475-5661.2012.00539.x
Hecht, B., & Stephens, M. (2014). A Tale of Cities: Urban Biases in Volunteered Geographic Information. Proceedings of the 8th International AAAI Conference on Weblogs and Social Media, 197–205.
Jurafsky, D., & Martin, J. H. (2020). Speech and Language Processing (3rd ed., draft). Stanford University.
Kıcıman, E., Counts, S., & Gasser, M. (2018). Using Longitudinal Social Media Analysis to Understand the Effects of Early College Alcohol Use. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Paper No. 168.
Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage.
Lim, M. (2020). From Habermas to #Habermas: Public spheres in Indonesia. In The Routledge Companion to Digital Media and Children. Routledge.
Neuman, W. L. (2014). Social Research Methods: Qualitative and Quantitative Approaches (7th ed.). Pearson.
Page, R. (2012). The linguistics of self-branding and micro-celebrity in Twitter: The role of hashtags. Discourse & Communication, 6(2), 181–201.
Ramadhan, F., & Budi, I. (2021). Sentiment Analysis on Indonesian Product Reviews Using Support Vector Machines. Procedia Computer Science, 179, 485–492.
Shelton, T., Poorthuis, A., Graham, M., & Zook, M. (2015). Mapping the Data Shadows of Hurricane Sandy: Uncovering the Sociospatial Dimensions of ‘Big Data’. Geoforum, 52, 167–179.
Sloan, L., & Morgan, J. (2015). Social Media, Politics and the State: Protests, Revolutions, Riots, Crime and Policing in the Age of Facebook, Twitter and YouTube. Routledge.
Tambunan, T. T. H. (2019). Recent evidence of the development of micro, small and medium enterprises in Indonesia. Journal of Global Entrepreneurship Research, 9, 18. https://doi.org/10.1186/s40497-018-0140-4
Townsend, L., & Wallace, C. (2016). Social Media Research: A Guide to Ethics. University of Aberdeen.
Tsur, O., & Rappoport, A. (2012). What's in a hashtag? Content-based prediction of the spread of ideas in microblogging communities. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining (pp. 643–652). https://doi.org/10.1145/2124295.2124320
Tsur, O., & Rappoport, A. (2012). What's in a hashtag? Content-based prediction of the spread of ideas. Proceedings of the 5th ACM International Conference on Web Search and Data Mining.
Zappavigna, M. (2015). Searchable talk: The linguistic functions of hashtags. Social Semiotics, 25(3), 274–291. https://doi.org/10.1080/10350330.2014.996948
Zeng, M., Wang, Y., & Chen, J. (2021). Hyperlocal business ecosystems: A review and research agenda. Journal of Business Research, 133, 113–124. https://doi.org/10.1016/j.jbusres.2021.04.045