When I was selected to be the editor for the Review of Communication, the first themed issue I had in mind was one that addressed big data in communication. I had three main reasons for focusing on big data in communication. First, over the past 20 years, there have been numerous high-quality big data studies in communication. Many of these studies have predominantly fallen within the realms of mass communication, political communication, information and communication, and digital communication. However, increasingly, studies have started to employ big data within health communication, interpersonal communication, and other “communication” disciplines. Second, my interest in this topic stemmed from the same interest shared by ParksFootnote1 (2014), who in his special issue on big data in communication wanted to provide a benchmark for research innovation. In his special issue afterword, Parks discussed how much of the work being done on big data at the time might not stand the test of time but would guide future work. Thus, I wanted to provide an outlet for researchers to methodologically progress the field of big data in communication. The third reason was more personal in nature. I have been working closely with big data for the past six years. My university has a Master’s of Analytics program, and in my role as Head of School, I have worked closely with academics from the College of Science, and College of Business to mentor master’s students using big data to explore multiple interdisciplinary questions. I have been fortunate enough to even advise a few students on their master’s theses as they published their works.Footnote2 I had become a part of the Big Data Movement from a bureaucratic point of view and then a research point of view.
In this themed issue, I wanted to see how communication scholars were employing big data. It is my hope that the articles in this themed issue represent the diversity of communication and big data, progress the field of big data in communication, and pique your own interest in this growing field of inquiry.
So, what is big data? Big data refers to large, dynamic, and distinct volumes of data created by people, tools, and machines. In each of the articles within this themed issue, “big data” is conceptualized and operationalized differently. However, what each article has in common is that each article shows the diversity of how the communication discipline understands big data and how the discipline latched onto big data as an important research tool/technique/philosophy.
In Adamczyk’s piece, “Communicating Dataism,”Footnote3 the author draws on the rhetoric of social intervention modelFootnote4 to critique the ideology of dataism. This model interprets dataism to include communication discourses comprising attention, need, and power that work in conjunction to maintain the public’s faith and trust in big data. The piece explores how attention, power, and need for big data influence how we interpret big data. Recognizing there have been many violations of public trust using big data, Adamczyk offers scholars ways to rhetorically understand how big data technologies have acquired cultural and political power. Adamczyk’s recommendations on how to alter the dataism ideology and interrogate technoliberalism offer insightful avenues for further research into big data and communication.
Hartelius’ piece, “The Great Chain of Being Sure about Things: Blockchain, Truth, and a Trustless Network,”Footnote5 explores texts that make up blockchains in political imagination. A blockchain is essentially a distributed ledger or record of digitally networked transactions. Growing public discourse about blockchains shows a public need to access truth and stamp out corruption within this system and these transactions. Using Phelan’sFootnote6 study of neoliberal politics and global media, Hartelius critiques the discursive function of blockchains as “governance policies,” and how such balance access to truth and trustlessness. A key point made in the piece, which resonated across all the articles in this issue, is that the data technology makes the truth it contains come true, as the data and its technology are undercut by an absence of prospective.
Lukito and Pruden’s “Critical Computation: Mixed-Methods Approaches to Big Language Data Analysis”Footnote7 examines the limitations of using only computational methods/techniques to study big language data. While methods for collecting data about language have increased, such methods employing computers (computational) do not process meanings. Thus, from a critical perspective, the authors propose that computational approaches to big language data analysis must be conducted using mixed methods approaches: quantitative, qualitative, and computational. In their article, the authors propose a step-by-step mixed methods approach and offer future considerations for researchers.
In Ryan, Hong, and Rashid’s “From Corpus Creation to Formative Discovery: The Power of Big Data-Rhetoric Teams and Methods,”Footnote8 the authors outline an approach to using big data for corpus building in rhetoric. Specifically, the authors conduct an ideographic analysis of state veteran laws linked with gun permits covering more than 7,000 files. The article presents preliminary findings. However, the key takeaways for this article are how it builds our understanding of corpus building in big data research. The article provides a clear discussion of corpus building, data cleaning, and analysis, and clearly demonstrates big data and rhetorical conceptualization. The article provides a guide for future work into big data-rhetoric.
Scott, Casmir, and Smith’s “Communication Studies Research and Big Data: Always Already Queer”Footnote9 uses artificial intelligence (AI) to explore public engagement with campus monuments and memorials at a Southeastern university in the United States. Examining issues of public memory, memorialization, and social justice, the researchers working within a larger research project, the team sought to create a nonintrusive computational method to detect engagement with memorials on campus. They then linked aspects of this project and method with queer methods, such as exploring crip time, or the disruption of heteronormative time.Footnote10 Based on their analysis, the authors acknowledge that research can be messy, with which most of us could agree. However, embracing the various discourses of big data, communication, the digital humanities, and queer methods can produce fruitful research.
Collectively, the articles within this special issue show the diverse and growing field of big data and communication. Articles in this issue come from queer methods, rhetoric, language, communication, and political communication, and draw from areas such as the humanities, digital communication, mass media, etc. In addition, these pieces each provide benchmarks for future works. Whether exploring big data-rhetoric, mixed methods approached to language, queer methods, AI, or the exploration of block chains, I encourage you to not only read these pieces but also reach out to the authors for any questions you might have.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 Malcolm Parks, “Big Data in Communication Research: Its Contents and Discontents,” Journal of Communication 64, no. 2 (2014): 355–60. https://doi.org/10.1111/jcom.12090.
2 Yuming Wang, Stephen M Croucher, and Erika Pearson, “National Leaders’ Usage of Twitter in Response to COVID-19: A Sentiment Analysis,” Frontiers in Political Communication 6 (2021): https://doi.org/10.3389/fcomm.2021.732399.
3 Christopher Adamczyk, “Communicating Dataism,” The Review of Communication 23, no. 1 (2023): 4–20.
4 William R. Brown, “Ideology as Communication Process,” Quarterly Journal of Speech” 64, no. 2 (1978): 123–40.
5 Johanna E Hartelius, “The Great Chain of Being Sure about Things: Blockchain, Truth, and a Truthless Network,” The Review of Communication 23, no. 1 (2023): 21–37.
6 Sean Phelan, Neoliberalism, Media and the Political (New York: Palgrave Macmillan, 2014).
7 Josephine Lukito and Meredith Pruden, “Critical Computation: Mixed-Methods Approaches to Big Language Data Analysis,” The Review of Communication 23, no. 1 (2023): 62–78.
8 Sarah Ryan, Lingzi Hong, and Mohotarema Rashid, “From Corpus Creation to Formative Discovery: The Power of Big Data-Rhetoric Teams and Methods,” The Review of Communication 23, no. 1 (2023): 38–61.
9 David T. Scott, Joshua Catalano Casmir, and Christa Smith, “Communication Studies Research and Big Data: Always Already Queer,” The Review of Communication 23, no. 1 (2023): 79–94.
10 Alison Kafer, Feminist Queer, Crop; Lee Edelman, No Future (Durham, NC: Duke University Press, 2004).