Embracing Computational Approaches to Social Media Research: Implications for Theory and Praxis in Sport Communication

Abeza G. O’Reilly N. Séguin B. Nzindukiyimana O. (2015). Social media scholarship in sport management research: A critical review. Journal of Sport Management, 29(6), 601–618. https://doi.org/10.1123/jsm.2014-0296

Abeza G. Sanderson J. (2022). Theory and social media in sport studies. International Journal of Sport Communication, 15(4), 284–292. https://doi.org/10.1123/ijsc.2022-0108

Anderson J. (2008, June 23). The end of theory: The data deluge makes the scientific method obsolete. Wired Magazine. Retrieved from https://www.wired.com/2008/06/pb-theory/?redirectURL=https%3A%2F%2Fwww.wired.com%2F2008%2F06%2Fpb-theory%2F

AoIR . (2023). About. Retrieved from https://aoir.org/about/

Balahur A. Jacquet G. (2015). Sentiment analysis meets social media–Challenges and solutions of the field in view of the current information sharing context. Information Processing and Management, 51(4), 428–432. https://doi.org/10.1016/j.ipm.2015.05.005

Batrinca B. Treleaven P. C. (2015). Social media analytics: A survey of techniques, tools and platforms. Ai and Society, 30(1), 89–116. https://doi.org/10.1007/s00146-014-0549-4

Bigsby K. G. Ohlmann J. W. Zhao K. (2017). Online and off the field: Predicting school choice in college football recruiting from social media data. Decision Analysis, 14(4), 261–273. https://doi.org/10.1287/deca.2017.0353

Bigsby K. G. Ohlmann J. W. Zhao K. (2019). Keeping it 100: Social media and self-presentation in college football recruiting. Big Data, 7(1), 3–20. https://doi.org/10.1089/big.2018.0094

Blei D.M. Ng A.Y. Jordan M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(4–5), 993–1022.

Boatwright B. C. Freberg K. (2022). Social media and sport marketing in North America. In Naraine M. L. Hayduk T. Doyle J. P. (Eds), The routledge handbook of digital sport media (pp. 206–216). Routledge.

Boukes M. Van de Velde B. Araujo T. Vliegenthart R. (2020). What’s the tone? Easy doesn’t do it: Analyzing performance and agreement between off-the-shelf sentiment analysis tools. Communication Methods and Measures, 14(2), 83–104.

boyd d. Crawford K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication and society, 15(5), 662–679.

boyd d. Ellison N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer‐mediated Communication, 13(1), 210–230.

Brown K. A. Billings A. C. Murphy B. Puesan L. (2018). Intersections of fandom in the age of interactive media: eSports fandom as a predictor of traditional sport fandom. Communication & Sport, 6(4), 418–435. https://doi.org/10.1177/2167479517727286

Bucur A.-M. Podina I.R. Dinu L.P. (2021). A psychologically informed part-of-speech analysis of depression in social media. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP2021), pages 199–207.

Cai L. Zhu Y. (2015). The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Journal, 14(2), 1–10. https://doi.org/10.5334/dsj-2015-002

Carr C. T. Hayes R. A. (2015). Social media: Defining, developing, and divining. Atlantic Journal of Communication, 23(1), 46–65. https://doi.org/10.1080/15456870.2015.972282

Chowdhury G. G. (2003). Natural language processing. Annual Review of Information Science and Technology, 37(1), 51–89. https://doi.org/10.1002/aris.1440370103

Chung T. L. D. Johnson O. Hall-Phillips A. Kim K. (2021). The effects of offline events on online connective actions: An examination of# BoycottNFL using social network analysis. Computers in Human Behavior, 115, 106623. https://doi.org/10.1016/j.chb.2020.106623

Colombo G. Bounegru L. Gray J. (2023). Visual models for social media image analysis: Groupings, engagement, trends, and rankings. International Journal of Communication, 17, 1956–1983.

Cranmer G. A. Peltz S. Boatwright B. C. Sanderson J. Scheinbaum A. (2023). Athletes’ displaced dissent on social media: Triggering agents, message strategies, and user-generated responses. Communication Quarterly, 71(4), 343–366. https://doi.org/10.1080/01463373.2023.2203828

Falavarjani S.A.M. Zarrinkalam F. Jovanovic J. Bagheri E. Ghorbani A.A. (2019). The reflection of offline activities on users’ online social behavior: An observational study. Information Processing & Management, 56(6), 1–20. https://doi.org/10.1016/j.ipm.2019.102070

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

Frederick E. L. Pegoraro A. Schmidt S. (2022). I’m not going to the f*** ing White House”: Twitter users react to Donald Trump and Megan Rapinoe. Communication & Sport, 10(6), 1210–1228. https://doi.org/10.1177/2167479520950778

Gong X. Wang Y. (2021). Exploring dynamics of sports fan behavior using social media big data-A case study of the 2019 National Basketball Association Finals. Applied Geography, 129, 102438. https://doi.org/10.1016/j.apgeog.2021.102438

Gu C. Kurov A. (2020). Informational role of social media: Evidence from Twitter sentiment. Journal of Banking and Finance, 121, 105969. https://doi.org/10.1016/j.jbankfin.2020.105969

Hambrick M. E. Pegoraro A. (2014). Social Sochi: Using social network analysis to investigate electronic word-of-mouth transmitted through social media communities. International Journal of Sport Management and Marketing, 15(3-4), 120–140. https://doi.org/10.1504/ijsmm.2014.072005

Hambrick M. E. Sanderson J. (2013). Gaining primacy in the digital network: Using social network analysis to examine sports journalists' coverage of the Penn State football scandal via Twitter. Journal of Sports Media, 8(1), 1–18. https://doi.org/10.1353/jsm.2013.0003

Hardin M. (2014). Moving beyond description: Putting Twitter in (theoretical) context. Communication & Sport, 2(2), 113–116. https://doi.org/10.1177/2167479514527425

Hardin M. Billings A. C. (2023). Much ado about twitter, twitch, and more: A maturing research agenda. Communication & Sport, 11(2), 215–218. https://doi.org/10.1177/21674795231152657

Harker J. L. (2021). # JoinTheAlliance: A network exploration into hashtag brand building by an emerging sports league. Journal of Sports Media, 16(1), 71–97.

Hayduk T. Newland B. (2020). Signalling Expertise in Sport Entrepreneurship: A Mixed-Methods Approach Using Topic Modeling and Thematic Analysis. Journal of Applied Sport Management, 12(1), 23–35. https://doi.org/10.7290/jasm120102

Hayes J. L. Britt B. C. Evans W. Rush S. W. Towery N. A. Adamson A. C. (2021). Can social media listening platforms’ artificial intelligence be trusted? Examining the accuracy of crimson hexagon’s (now Brandwatch Consumer research’s) AI-driven analyses. Journal of Advertising, 50(1), 81–91. https://doi.org/10.1080/00913367.2020.1809576

Hidayatullah A. F. Pembrani E. C. Kurniawan W. Akbar G. Pranata R. (2018, April). Twitter topic modeling on football news. In 2018 3rd International Conference on Computer and Communication Systems (ICCCS) (pp. 467-471). IEEE.

Himelboim I. (2017). Social network analysis (Social media). In Matthes J. Davis C. S. Potter R. F. (Eds.), The international encyclopedia of communication research methods (pp. 1–15). John Wiley & Sons. https://doi.org/10.1002/9781118901731.iecrm0236

Himelboim I. Sweetser K. D. Tinkham S. F. Cameron K. Danelo M. West K. (2016). Valence-based homophily on Twitter: Network analysis of emotions and political talk in the 2012 presidential election. New Media and Society, 18(7), 1382–1400. https://doi.org/10.1177/1461444814555096

Hofman J. Watts D. J. Athey S. Garip F. Griffiths T. L. Kleinberg J. Margetts H. Mullainathan S. Salganik M. J. Vazire S. Vespignan A. Yarkoni T. (2021). Integrating explanation and prediction in computational social science. Nature, 595, 181–188. https://doi.org/10.1038/s41586-021-03659-0

Investopedia . (2022, October 23). Web 3.0 Explained, Plus the History of Web 1.0 and 2.0. Retrieved from https://www.investopedia.com/web-20-web-30-5208698

Jünger J. Geise S. Hänelt M. (2022). Unboxing Computational social media research from a datahermeneutical perspective: How do scholars address the tension between automation and interpretation? International Journal of Communication, 16, 1482–1505.

Kalampokis E. Tambouris E. Tarabanis K. (2013). Understanding the predictive power of social media. Internet Research.

Kelling S. Fink D. La Sorte F. A. Johnston A. Bruns N. E. Hochachka W. M. (2015). Taking a ‘Big Data’approach to data quality in a citizen science project. Ambio, 44(4), 601–611. https://doi.org/10.1007/s13280-015-0710-4

Kelling S. Hochachka W. Fink D. Riedewald M. Caruana R. Ballard G. Hooker G. (2009). Data-intensive Science: A New Paradigm for Biodiversity Studies. BioScience, 59(7), 613–620. https://doi.org/10.1525/bio.2009.59.7.12

Kennedy H. Kunkel T. Funk D. C. (2021). Using predictive analytics to measure effectiveness of social media engagement: A digital measurement perspective. Sport Marketing Quarterly, 30(4), 265–277. https://doi.org/10.32731/smq.304.1221.02

Kim M. Oh S. W. Han J. W. (2020). Social big data analysis on demands for the Korean sport industry. International Journal of Applied Sports Sciences, 32(2), 28–39. https://doi.org/10.24985/ijass.2020.32.2.28

Kitchin R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data and Society, 1(1), 2053951714528481. https://doi.org/10.1177/2053951714528481

Kumble S. Diddi P. Bien-Aimé S. (2022). ‘Your Strength Is Inspirational’: How Naomi Osaka’s Twitter Announcement Destigmatizes Mental Health Disclosures. Communication & Sport. https://doi.org/10.1177/21674795221124584

Laney D. (2001, February 6). 3D data management: Controlling data volume, velocity and variety. Meta group. Retrieved from http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf

Lassen N. B. la Cour L. Vatrapu R. (2017). Predictive analytics with social media data (pp. 328–340). The SAGE handbook of social media research methods.

Lazer D. Pentland A. Adamic L. Aral S. Barabási A. L. Brewer D. Van Alstyne M. (2009). Computational social science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742

Lazer D. M. Pentland A. Watts D. J. Aral S. Athey S. Contractor N. Wagner C. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060–1062. https://doi.org/10.1126/science.aaz8170

Li C. Kent M. L. (2021). Explorations on mediated communication and beyond: Toward a theory of social media. Public Relations Review, 47(5), 102112. https://doi.org/10.1016/j.pubrev.2021.102112

Liu P. Koivisto S. Hiippala T. van der Lijn C. Vaisanen T. Nurmi M. Muukkonen P. (2022). Extracting locations from sport and exercise-related social media messages using a neural network-based bilingual toponym recognition model. Journal of Spatial Information Science, 24, 31–61. https://doi.org/10.5311/josis.2022.24.167

Lucas G. M. Gratch J. Malandrakis N. Szablowski E. Fessler E. Nichols J. (2017). GOAALLL!: Using sentiment in the world cup to explore theories of emotion. Image and Vision Computing, 65, 58–65. https://doi.org/10.1016/j.imavis.2017.01.006

Margolin D. B. (2019). Computational contributions: A symbiotic approach to integrating big, observational data studies into the communication field. Communication Methods and Measures, 13(4), 229–247. https://doi.org/10.1080/19312458.2019.1639144

Mehta P. Pandya S. Kotecha K. (2021). Harvesting social media sentiment analysis to enhance stock market prediction using deep learning. PeerJ Computer Science, 7, Article e476. https://doi.org/10.7717/peerj-cs.476

Mneimneh Z. Pasek J. Singh L. Best R. Bode L. Bruch E. Budak C. Davis-Kean P. Donato K. Ellison N. Gelman A. Groshen E. Hemphill L. Hobbs W. Jensen J. B. Karypis G. Ladd J. M. O’Hara A. Raghunathan T. … Wojcik S. (2021, March 16). Data Acquisition, sampling, and data preparation considerations for quantitative social science research using social media data. https://doi.org/10.31234/osf.io/k6vyj

Morgan A. Wilk V. (2022). Social media users’ crisis response: A lexical exploration of social media content in an international sport crisis. Public Relations Review, 47(4), 102057. https://doi.org/10.1016/j.pubrev.2021.102057

Murray A. Kim D. Combs J. (2022). The promise of a decentralized Internet: What is web 3.0 and HOW can firms prepare? Business Horizons, 66(2), 191–202. https://doi.org/10.1016/j.bushor.2022.06.002

Naraine M. L. Wanless L. (2020). Going all in on AI: Examining the value proposition of and integration challenges with one branch of artificial intelligence in sport management. Sports Innovation Journal, 1, 49–61. https://doi.org/10.18060/23898

Pedersen P. M. (2014). A commentary on social media research from the perspective of a sport communication journal. Communication & Sport, 2(2), 138–142. https://doi.org/10.1177/2167479514527428

Pegoraro A. (2013). Sport fandom in the digital world. In Routledge handbook of sport communication (pp. 262–272). Routledge.

Popp N Du J. Shapiro S.L. Simmons J.M. (2023). Using Artificial Intelligence to Detect the Relationship Between Social Media Sentiment and Season Ticket Purchases. International Journal of Sport Communication, 1–15. https://doi.org/10.1123/ijsc.2023-0155

Radosavljevic V. Grbovic M. Djuric N. Bhamidipati N. (2014). Large-scale World Cup 2014 outcome prediction based on Tumblr posts. In KDD workshop on large-scale sports analytics.

Rains S. A. (2020). Big data, computational social science, and health communication: A review and agenda for advancing theory. Health Communication, 35(1), 26–34. https://doi.org/10.1080/10410236.2018.1536955

Reifurth K. Bernthal M. J. Ballouli K. Collins D. (2019). Nonlocal fandom: Effects of geographic distance, geographic identity, and local Competition on team identification. Sport Marketing Quarterly, 28(4), 195–208. https://doi.org/10.32731/smq.284.122019.02

Roder M. Both A. Hinneburg A. (2015). Exploring the space of topic coherence measures. In Proceedings of the Eighth ACM International Conference on Websearch and Datamining, pp. 399–408. https://doi.org/10.1145/2684822.2685324

Rowe D. (2014). Following the followers: Sport researchers’ labour lost in the twittersphere? Communication & Sport, 2(2), 117–121. https://doi.org/10.1177/2167479514527431

Segev E. (Ed), (2021). Semantic network analysis in social sciences. Routledge.

Shah D. V. Cappella J. N. Neuman W. R. (2015). Big data, digital media, and computational social science: Possibilities and perils. The ANNALS of the American Academy of Political and Social Science, 659(1), 6–13. https://doi.org/10.1177/0002716215572084

Sul H. K. Dennis A. R. Yuan L. (2017). Trading on twitter: Using social media sentiment to predict stock returns. Decision Sciences, 48(3), 454–488. https://doi.org/10.1111/deci.12229

Thelwall M. (2017). The Heart and Soul of the Web? Sentiment Strength Detection in the Social Web with SentiStrength. In Cyberemotions (pp. 119–134). Springer. https://doi.org/10.1007/978-3-319-43639-5_7

Theocharis Y. Jungherr A. (2021). Computational social science and the study of political communication. Political Communication, 38(1-2), 1–22. https://doi.org/10.1080/10584609.2020.1833121

Townsend L. Wallace C. (2017). The ethics of using social media data in research: A new framework. In The ethics of online research. Emerald Publishing Limited.

van Atteveldt W. Peng T.Q. (2018). When Communication Meets Computation: Opportunities, Challenges, and Pitfalls in Computational Communication Science. Communication Methods and Measures, 12(2–3), 81–92. https://doi.org/10.1080/19312458.2018.1458084

van Atteveldt W. Van der Velden Boukes M. (2021). The validity of sentiment analysis: Comparing manual annotation, crowd-coding, dictionary approaches, and machine learning algorithms. Communication Methods and Measures, 15(2), 121–140.

Wang G. Zhang Z. Sun J. Yang S. Larson C. A. (2015). POS-RS: A random subspace method for sentiment classification based on part-of-speech analysis. Information Processing and Management, 51(4), 458–479. https://doi.org/10.1016/j.gene.2014.10.058

Wanless L. Naraine M. L. (2023). Analogous forecasting for predicting sport innovation diffusion: From business analytics to natural language processing. Journal of Sport Management, 37(3), 191–202. https://doi.org/10.1123/jsm.2022-0026

Wäsche H. Dickson G. Woll A. Brandes U. (2017). Social network analysis in sport research: An emerging paradigm. European Journal for Sport and Society, 14(2), 138–165. https://doi.org/10.1080/16138171.2017.1318198

Xiong Y. Cho M. Boatwright B. (2019). Hashtag activism and message frames among social movement organizations: Semantic network analysis and thematic analysis of Twitter during the# MeToo movement. Public Relations Review, 45(1), 10–23. https://doi.org/10.1186/s13000-019-0786-4

Yang E. C. L. Hayes M. Chen J. Riot C. Khoo-Lattimore C. (2020). A social media analysis of the gendered representations of female and male athletes during the 2018 commonwealth games. International Journal of Sport Communication, 13(4), 670–695. https://doi.org/10.1123/ijsc.2020-0045

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. https://doi.org/10.1177/21674795221090420