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Twitter data analytical methodology development for prediction of start-up firms’ social media marketing level

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  • Jung, Sang Hoon
  • Jeong, Yong Jin

Abstract

Social media marketing is an essential and important tool for start-up firms, which can help start-up firms remedy the marketing limitations through ease and relatively low costs. Predicting start-up firms’ social media engagement level can allow them to gauge the effectiveness of their social media marketing efforts and can provide numerous benefits related to strategic marketing processes. This study focuses on developing a methodology involving data science processes and machine learning models to account for the ongoing advancement of business intelligence methodologies. This study gathered data of 8,434 start-up firms from Twitter, generated social media-based features, and created machine learning models to predict the social media engagement level of each firm. The results show that deep learning provides the best accuracy in predicting the engagement levels. The results also show that the number of tweets by the firms, the number of retweets received, and the number of likes received have the most significance in determining the effectiveness of social media marketing activities.

Suggested Citation

  • Jung, Sang Hoon & Jeong, Yong Jin, 2020. "Twitter data analytical methodology development for prediction of start-up firms’ social media marketing level," Technology in Society, Elsevier, vol. 63(C).
    • Handle: RePEc:eee:teinso:v:63:y:2020:i:c:s0160791x19306621
    DOI: 10.1016/j.techsoc.2020.101409
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    1. Ranyard, J.C. & Fildes, R. & Hu, Tun-I, 2015. "Reassessing the scope of OR practice: The Influences of Problem Structuring Methods and the Analytics Movement," European Journal of Operational Research, Elsevier, vol. 245(1), pages 1-13.
    2. Rohit Aggarwal & Ram Gopal & Alok Gupta & Harpreet Singh, 2012. "Putting Money Where the Mouths Are: The Relation Between Venture Financing and Electronic Word-of-Mouth," Information Systems Research, INFORMS, vol. 23(3-part-2), pages 976-992, September.
    3. Deborah Agostino & Michela Arnaboldi, 2016. "A Measurement Framework for Assessing the Contribution of Social Media to Public Engagement: An empirical analysis on Facebook," Public Management Review, Taylor & Francis Journals, vol. 18(9), pages 1289-1307, October.
    4. Zoha RAHMAN & SUBERAMANIAN & MOGHAVVEMI, 2017. "The Impact of Social Media Engagement Metrics on Purchase Intention: A Study on Brand Fan Page Followers," Book chapters-LUMEN Proceedings, in: Camelia IGNATESCU & Antonio SANDU & Tomita CIULEI (ed.), Rethinking Social Action. Core Values in Practice, edition 1, volume 1, chapter 61, pages 665-681, Editura Lumen.
    5. Armstrong, J. Scott & Brodie, Roderick J., 1999. "Forecasting for Marketing," MPRA Paper 81690, University Library of Munich, Germany.
    6. Johanna Gummerus & Veronica Liljander & Emil Weman & Minna Pihlström, 2012. "Customer engagement in a Facebook brand community," Management Research Review, Emerald Group Publishing Limited, vol. 35(9), pages 857-877, August.
    7. Dalrymple, Douglas J., 1975. "Sales forecasting methods and accuracy," Business Horizons, Elsevier, vol. 18(6), pages 69-73, December.
    8. Antretter, Torben & Blohm, Ivo & Grichnik, Dietmar & Wincent, Joakim, 2019. "Predicting new venture survival: A Twitter-based machine learning approach to measuring online legitimacy," Journal of Business Venturing Insights, Elsevier, vol. 11(C), pages 1-1.
    9. Tonya Boone & Ram Ganeshan & Robert L. Hicks & Nada R. Sanders, 2018. "Can Google Trends Improve Your Sales Forecast?," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1770-1774, October.
    10. Raymond Yiu Keung Lau & Wenping Zhang & Wei Xu, 2018. "Parallel Aspect‐Oriented Sentiment Analysis for Sales Forecasting with Big Data," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1775-1794, October.
    11. Sandor Czellar & D. Sprott & E. Spangenberg, 2009. "The Importance of a General Measure of Brand Engagement on Market Behavior: Development and Validation of a Scale," Post-Print hal-00458392, HAL.
    12. Mortenson, Michael J. & Doherty, Neil F. & Robinson, Stewart, 2015. "Operational research from Taylorism to Terabytes: A research agenda for the analytics age," European Journal of Operational Research, Elsevier, vol. 241(3), pages 583-595.
    13. Donald S. Tull, 1967. "The Relationship of Actual and Predicted Sales and Profits in New-Product Introductions," The Journal of Business, University of Chicago Press, vol. 40, pages 233-233.
    14. Wm. Gerard Sanders & Steven Boivie, 2004. "Sorting things out: valuation of new firms in uncertain markets," Strategic Management Journal, Wiley Blackwell, vol. 25(2), pages 167-186, February.
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