Big Data and Innovative Research Methods

in International Journal of Sport Communication

Click name to view affiliation

Yoseph Z. Mamo Sport Management, Department of Human Movement Sciences, Old Dominion University, Norfolk, VA, USA

Search for other papers by Yoseph Z. Mamo in
Current site
Google Scholar
PubMed Close
https://orcid.org/0000-0001-5171-5186 *
DOI:
https://doi.org/10.1123/ijsc.2023-0109
Keywords:
sentiment analysis; machine learning; natural language processing; sport management; social media
First Published Online:
19 May 2023
In Print:
Volume 16: Issue 3
Page Range:
352–360
Restricted access

Big data and innovative research methods are two rapidly evolving trends that are transforming how we conduct research in sport management. Considering the natural relationship between social media, which is widely recognized as a major big-data source, and sport, this commentary centers on contemporary research method applied to social media data. In doing so, it discusses contemporary innovative techniques for social media data, focusing on exploring ways to access social media data, the natural language-processing techniques used, the challenges they address, the strengths and limitations of different techniques, and the ethical and privacy considerations associated with their use. Furthermore, the commentary demonstrates that using sentiment-analysis tools (e.g., Syuzhet, Bing, and AFFIN) is appropriate and efficient in analyzing sport’s social media data. Thus, a rigorous application of contemporary innovative techniques can significantly shape the future of sport management research. However, researchers must exercise caution when considering the source and preprocessing of the data prior to applying advanced analytical techniques.

Address author correspondence to ymamo@odu.edu, https://orcid.org/0000-0001-5171-5186

  • Collapse
  • Expand

International Journal of Sport Communication

Cover International Journal of Sport Communication

  • Abeza, G. (2020). Big data in sport industry: Interview with Michal Lorenc, head of industry—Ticketing and live events at Google. International Journal of Sport Communication, 13(4), 663–669. https://doi.org/10.1123/ijsc.2020-0272

    • Search Google Scholar
    • Export Citation

  • Chang, Y. (2019). Spectators’ emotional responses in tweets during the Super Bowl 50 game. Sport Management Review, 22(3), 348–362. https://doi.org/10.1016/j.smr.2018.04.008

    • Search Google Scholar
    • Export Citation

  • Davidson, N.P., Du, J., & Giardina, M.D. (2020). Through the perilous fight: A case analysis of professional wrestling during the COVID-19 pandemic. International Journal of Sport Communication, 13(3), 465–473. https://doi.org/10.1123/ijsc.2020-0224

    • Search Google Scholar
    • Export Citation

  • Du, J., Floyd, C., Kim, A.C., Baker, B.J., Sato, M., James, J.D., & Funk, D.C. (2021). To be or not to be: Negotiating leisure constraints with technology and data analytics amid the COVID-19 pandemic. Leisure Studies, 40(4), 561–574. https://doi.org/10.1080/02614367.2020.1862284

    • Search Google Scholar
    • Export Citation

  • Fan, M., Billings, A., Zhu, X., & Yu, P. (2020). Twitter-based BIRGing: Big data analysis of English national team fans during the 2018 FIFA World Cup. Communication & Sport, 8(3), 317–345. https://doi.org/10.1177/2167479519834348

    • Search Google Scholar
    • Export Citation

  • Filo, K., Lock, D., & Karg, A. (2015). Sport and social media research: A review. Sport Management Review, 18(2), 166–181. https://doi.org/10.1016/j.smr.2014.11.001

    • Search Google Scholar
    • Export Citation

  • Floyd, C., Gulavani, S.S., Du, J., Kim, A.C., & Pappas, J. (2021). A tale of two cities: COVID-19 and the emotional well-being of student-athletes using natural language processing. Frontiers in Sports and Active Living, 3, Article 710289. https://doi.org/10.3389/fspor.2021.710289

    • Search Google Scholar
    • Export Citation

  • George, G., Haas, M.R., & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57(2), 321–326. https://doi.org/10.5465/amj.2014.4002

    • Search Google Scholar
    • Export Citation

  • Gong, H., Watanabe, N., Soebbing, B., Brown, M., & Nagel, M. (2021). Do consumer perceptions of tanking impact attendance at National Basketball Association games? A sentiment analysis approach. Journal of Sport Management, 35(3), 254–265. https://doi.org/10.1123/jsm.2020-0274

    • Search Google Scholar
    • Export Citation

  • Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the 2004 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining  (pp. 168–177). Association for Computing Machinery. https://doi.org/10.1145/1014052.1014073

    • Search Google Scholar
    • Export Citation

  • Liu, B. (2012). Sentiment analysis and opinion mining. In D. Roth (Ed.), Synthesis lectures on human language technologies (Vol. 5, pp. 1–167). Morgan & Claypool Publishers.

    • Search Google Scholar
    • Export Citation

  • Lopez, M.J. (2020). Bigger data, better questions, and a return to fourth down behavior: An introduction to a special issue on tracking data in the National football League. Journal of Quantitative Analysis in Sport, 16(2), 73–79. https://doi.org/10.1515/jqas-2020-0057

    • Search Google Scholar
    • Export Citation

  • MacInnes, P. (2022, November 19). Fact check: 11 eye-catching lines from Gianni Infantino’s speech in Qatar. The Guardian.

  • Mamo, Y, Su, Y., & Abeza, G. (2021). Social media and data management in sport. In G. Abeza, N. O’Reilly, J. Sanderson, & E. Fredrick (Eds.), Social media in sport: Theory and practice. World Scientific.

    • Search Google Scholar
    • Export Citation

  • Mamo, Y., Su, Y., & Andrew, D.P. (2022). The transformative impact of big data applications in sport marketing: Current and future directions. International Journal of Sport Marketing and Sponsorship, 23(3), 594–611. https://doi.org/10.1108/IJSMS-03-2021-0073

    • Search Google Scholar
    • Export Citation

  • Matti, J. (2021). Frustrated customers: The effect of unexpected emotional cues on Yelp reviews. Journal of Sport Management, 35(3), 203–215. https://doi.org/10.1123/jsm.2020-0147

    • Search Google Scholar
    • Export Citation

  • Piña-García, C.A., Gershenson, C., & Siqueiros-García, J.M. (2016). Towards a standard sampling methodology on online social networks: Collecting global trends on Twitter. Applied Network Science, 1(1), Article 3. https://doi.org/10.1007/s41109-016-0004-1

    • Search Google Scholar
    • Export Citation

  • Simsek, Z., Vaara, E., Paruchuri, S., Nadkarni, S., & Shaw, J.D. (2019). New ways of seeing big data. Academy of Management Journal, 62(4), 971–978. https://doi.org/10.5465/amj.2019.4004

    • Search Google Scholar
    • Export Citation

  • Wanless, L. (2022). Progressive analytics diffusions: rewiring our software. Journal of Applied Sport Management, 14(4), 8.

  • Watanabe, N., & Rewliak, J. (2022). Where analytics gets it wrong. Journal of Applied Sport Management, 14(4), Article 2. https://doi.org/10.7290/jasm14hbcr

    • Search Google Scholar
    • Export Citation

  • Watanabe, N.M., Shapiro, S., & Drayer, J. (2021). Big data and analytics in sport management. Journal of Sport Management, 35(3), 197–202. https://doi.org/10.1123/jsm.2021-0067

    • Search Google Scholar
    • Export Citation

  • Yan, G., Watanabe, N.M., Shapiro, S.L., Naraine, M.L., & Hull, K. (2019). Unfolding the Twitter scene of the 2017 UEFA Champions League Final: Social media networks and power dynamics. European Sport Management Quarterly, 19(4), 419–436. https://doi.org/10.1080/16184742.2018.1517272

    • Search Google Scholar
    • Export Citation

  • Yoo, J.J., Min, B., & Koh, Y. (2022). Cross-national news narratives of the Paralympic Games: Computational text analysis of the media coverage in the United States and South Korea. Communication & Sport. Advance online publication. https://doi.org/10.1177/21674795221090420

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 382 382 74
Full Text Views 20 20 3
PDF Downloads 32 32 3