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Analyzing Entrepreneurial Social Networks with Big Data

Author

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  • Feng Wang
  • Elizabeth A. Mack
  • Ross Maciewjewski

Abstract

As we begin to understand who uses particular social media platforms, this user information represents a way forward for understanding the types of research questions for which big data might prove valuable. In this respect, the use of social media data for analyzing entrepreneurial networks represents a promising research domain. Not only does the user profile of social media users overlap substantially with the profile of entrepreneurs, but research highlights that the entrepreneurial process is a fundamentally networked activity. Given this research promise, this study analyzes digitally mediated interactions using Twitter data collected about a variety of actors engaged in entrepreneurial networks for the United States over an eighteen-month period. Analytical results reveal that the hashtags used in this analysis (#smallbiz and #entrepreneur) do capture (albeit not exhaustively) well-known actors in entrepreneurial networks, as well as important subtleties in the geography of locales engaged in these networks. The article closes with an agenda for big data research on entrepreneurship that highlights the important role of geographers in unraveling these networked geographies given the complexities of ground-truthing geographic information from big data sources.

Suggested Citation

  • Feng Wang & Elizabeth A. Mack & Ross Maciewjewski, 2017. "Analyzing Entrepreneurial Social Networks with Big Data," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(1), pages 130-150, January.
    • Handle: RePEc:taf:raagxx:v:107:y:2017:i:1:p:130-150
    DOI: 10.1080/24694452.2016.1222263
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    Cited by:

    1. Carlo Corradini & Emma Folmer & Anna Rebmann, 2022. "Listening to the buzz: Exploring the link between firm creation and regional innovative atmosphere as reflected by social media," Environment and Planning A, , vol. 54(2), pages 347-369, March.
    2. Jens Prüfer & Patricia Prüfer, 2020. "Data science for entrepreneurship research: studying demand dynamics for entrepreneurial skills in the Netherlands," Small Business Economics, Springer, vol. 55(3), pages 651-672, October.
    3. Werner Liebregts & Pourya Darnihamedani & Eric Postma & Martin Atzmueller, 2020. "The promise of social signal processing for research on decision-making in entrepreneurial contexts," Small Business Economics, Springer, vol. 55(3), pages 589-605, October.
    4. Johannes Bloh & Tom Broekel & Burcu Özgun & Rolf Sternberg, 2020. "New(s) data for entrepreneurship research? An innovative approach to use Big Data on media coverage," Small Business Economics, Springer, vol. 55(3), pages 673-694, October.
    5. Martin Obschonka & David B. Audretsch, 2020. "Artificial intelligence and big data in entrepreneurship: a new era has begun," Small Business Economics, Springer, vol. 55(3), pages 529-539, October.

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