Introduction 

I write this article to you on one screen, and you, reader, view it on another, a transmission I invoke here to highlight an embodied digitality that suffuses my points to come. The impress of my keyboard keys indexes movement across networks—digital and otherwise—that both captures my bodily movement and sequences it across our infrastructural milieu. The springs of my keys as my fingers travel across them ferry meaning to you across space and time, and there you are—somewhere on the other side of light-based fiber optics, data servers cohering our networked lives, a router powered at planetary expense, the person who plugged in the router in the first place, the radiant technology of Wi-Fi seemingly inhering our connectivity. This small collection you make of me here (and I am collecting you, too) that is, this arrangement of bodies and technologies—deceptively simple—belies the theoretically dense conceit that at the core of our interaction are bodies that have become embodied: upcycled, translated, and communicated in some socio-corporeal manner (Bratta and Sundvall; Bates et al.; Johnson et al.). 

Using this storied invocation of my body (and, really, yours too), I demarcate a conceptual aperture and advance two heuristic axioms that underpin this conversation piece. First, I highlight the material conditions of our meeting here amid this cluster in Peitho to foreground a methodological stance toward digitally mediated settings, accounting for complex human identities, technologies, and practices, as well as their commensurate effects on our work as internet researchers—in essence, the ways we collect each other through storied interactions in online settings. Second, I foreground the idea that identity and technology are co-imbricated amid the respective imperial and anticolonial projects of humanness (Brown)^[1]. With this techno-identive interplay, I argue for refreshed research practices that account for “digital bodies, [that] either virtually produced or augmented, complicate traditional perspectives of embodiment” (Bates et al.).  

To account for embodiment in research methods, I offer a methodological approach to doing digital cultural rhetorics research called slow coding, a qualitative research practice of better attuning ourselves to the intersectional internet, a term used by Brendesha M. Tynes and Safiya Umoja Noble to indicate “an epistemological approach to researching gendered and racialized identities in digital and information studies. It offers a lens, based on the past articulations of intersectional theory, for exploring power in digital technologies and the global Internet(s)” (“Introduction” 3). In so doing, I contend that we square our analytical potency as internet researchers driven by feminist ethics against white supremacist configurations of research as a practice and the humans we research as a colonially marked, epistemological category. Such a move resonates with Jennifer Sano-Franchini’s call for more research on online spaces that focuses on the everyday rhetorical-relational work of foregrounding community in relation to marginalizing forces that accounts for and disrupts such forces. Disruption, then, serves as the modus operandi for slow coding across the full breadth of this article. 

 I therefore advance slow coding as a research practice grounded in the intersectional internet, affording researchers an approach to working ethically in the ebbs and flows of oppression while allowing for meaningful engagement with the effects of colonization on precarious groups of people. Slow coding as a qualitative research practice adheres a slow, deliberate intentionality at the pre-coding and coding stages of a research project (Saldaña), actively centering the oppressive context that led to the data itself (in my case, tweets) and configuring analysis to disrupt the identified oppression. Given that it comprises the pre-coding and coding stages, slow coding consolidates these stages and facilitates the researcher reviewing their data while they collect it and shortly thereafter, creating analytic memos that respond to the colonial context of the data, reviewing any accompanying meta-data to understand the geographic history at the fore of oppression, learning the identive particularities of the people who comprise the data as a departure from a typified research subject wherein anonymity is whiteness. Perhaps most importantly, slow coding requires particular research questions that are attuned to the settler colonial machinations of how oppressions are wrought, particular stances grounded in anticolonialism, and the time needed to do meaningful work beyond the publishing timeline that entraps many of us.  

In what follows, I outline how research as practice has been wrought from colonial enterprise (Absolon; Tuhiwai Smith), with commensurate epistemological implications in the ways we research people using technology (Benjamin). These humanistic configurations in turn inflect a typical internet-based research project via our methodologies, including what the site can be, who the participants are, and what the data comprise (Gallagher). To fully articulate slow coding, a research practice that works in relation and opposition to these colonial conditions, I share my own research experiences illustrating the deep care required of working with marginalized communities, starting with respectful observation, moving toward ethical engagement and gathering, and then culminating in antiracist analytic strategies that allow the data to story itself and tying online life to the offline oppressions. In this way, I offer suggestions for each step of the multilayer process that stacks into a research project: who the participants are, where the research site is, what the rhetorical-relational data comprise, and the other ingredient strands that mesh into such a project.  

Researching on the Internet: Colonial Contexts and the Need for Anticolonial Options 

Colonial conditions set the stage for both our meeting on your screen and the array of practices that led to this moment. Research, despite our best intentions, comprises the colonial conditions by which research as a practice emerged, perpetuates, and now functions (Absolon; Tuhiwai Smith). Indeed, research hinges on “maintaining the status quo and supporting the evolution of societies that reward some people and inhibit others. Research can be used to suppress ideas, people, and social justice just as easily … than it can be used to respect, empower, and liberate. Good intentions are never enough to produce anti-oppressive processes or outcomes” (Potts and Brown 260). That said, I follow the lead of cultural rhetoricians whose purview constellates across the colonial tensions within digital studies (Edwards; Haas) and embodiment (Johnson et al.). Slow coding thus proceeds from the simple conceit that research is a sticky consolidation of inquisitive acts derived from the history and now nefarious machinations of settler colonialism as it shapes both research and the internet, combined in the form of internet-based research projects (Powell).  

Homing in on digital technologies, the internet itself is a colonial project (Amrute; Simmons). For all the good it can and does foster, the internet today comprises a corporatized, platformed architecture that actively suppresses marginalized groups of people: “everything from representation to hardware, software, computer code, and infrastructures might be implicated in global economic, political, and social systems of control” (Tynes and Noble 6). Notably, Nicole Marie Brown highlights the algorithmic nature of the assembling, so-called objective computational forces that “expose how power in decisioning is being organized within the social world” (56). In this way, the very algorithms that organize the data researchers collect—especially white researchers—perpetuate whiteness. Further, beyond the function of the internet, digital infrastructure itself serves mainly as settler colonial expansion for colonial metropoles, with communicative thresholds expanding across the world and worsening climatological conditions (Edwards; Haas).  

However, I do not want to wallow in the saturnine conditions of research and the internet in this piece, as doing so performs a disservice to the kinds of questions we might ask within our purview as internet researchers. Moreover, as mentioned above, to perform slow coding is to ask preemptively the kinds of research questions grounded in anticolonial intent that work in contradistinction to colonial purpose. Amid the above considerations that underpin slow coding, an attunement to happiness, joy, well-being—community—serve as a critical departure from colonial research practices; in other words, rather than generally asking, “How is harm being perpetuated to this marginalized community?” we might ask, “How is this community keeping itself safe in the face of harm out in the world—and what can I do to foster better care?” In pivoting to this question, the slow coder must attune to communities that bring the fullness of their lives—the struggles and triumphs—to digital spaces in a manner that resonates with the offline oppressions that weave together a daily milieu; in other words, we must configure our projects to operate on the intersectional internet.  

Asking Anticolonial Research Questions: Researching on the Intersectional Internet 

Considering the flattening effect of conducting research on the internet (that is, the identive baseline that casts a datapoint as a mere utterance with little lived context), the use of social media as a force for good reveals a schema for revising the internet as an intersectional network through which the on-the-grounds work of identity politics might be enacted (construed from the lineage of Black feminist thought; Collins; Tynes, Schuschke, and Noble). For Tynes and Noble, digital intersectionality is a concept at the juncture of potential and control “in the form of both analytic strategy and critical praxis, as a resource grounded in the offline and online subjectivities of participants” (26). The intersectional internet instills an attunement to Black life on the internet via Black feminist thought (Collins) and Black feminist technology studies (Noble, “Future”), revealing cracks in the hostile, algorithmic terrain of the internet wherein marginalized users upcycle the tools at hand to meet and to counter both their oppressors and oppressions. As an analytic strategy, digital intersectionality foregrounds identity and all of its import, especially for Black users of the internet; as critical praxis, it requires attuning research projects to the concept of the intersectional internet.  

 The intersectional internet serves as a mutinous framework, revealing how Black and other people of color live, play, and organize online around and against the offline violence they face and the online violence that are the algorithmic forces that center whiteness. It also serves as an antenarrative of the internet, which becomes a tool for empowerment despite colonial histories. Indeed, “from its earliest articulations, intersectionality has not only been used in scholarly work and teaching but has also been used as analytic strategy and critical praxis directed at social and political intervention” (Tynes, Schuschke, and Noble 35). In this way, slow coding as an approach to asking anticolonial research questions departs from the colonial research configuration and attends to “individuals’ intersectional vantage points on topics allow for a fluid exchange of ideas and beliefs” (Tynes, Schuschke, and Noble 36). For me, slow coding was an emergent practice I developed via perspectives in Indigenous methodologies (Gaudry; Tuck and Yang), my own intention on centering the needs of my community as a queer Chicano, and time afforded to me at the height of the COVID-19 pandemic. Thus, I created my dissertation project—an interrogation of the social media practices of queer and trans BIPOC on Twitter—by first asking research questions steered in part by the considerations I outlined above. In other words, I posed research questions that could be used to craft a project contingent on social justice that squared the focus of the project against the oppression itself, while also attending to the commensurate work via my disciplinary training and my intent on taking my time (a luxury, to be sure, but one I was afforded because of the COVID-19 pandemic). 

My research questions were: “What are the rhetorical practices of queer and trans Black, Indigenous, and People of Color who tweet about their sexual health practices online? How might these practices be ethically integrated into public health outreach?” I spotlight identity and community enrichment with these questions, each serving as a framework for building the actual research project itself. In creating a research project, the slow coder must ask an anticolonial research question that highlights the context of the digital spaces in which research is conducted. To that end, I highlight the anticolonial utility found within the concept of the intersectional internet. Given that the intersectional internet as concept upcycles a cadre of critical perspectives on digital technologies, sociotechnical processes, digital-material labor conditions, and the identive capacity of social media platforms, research questions that allow for slow coding must function in contradistinction to colonial configurations of internet-based research. In this way, slow coding becomes a solution—and I stress the indefinite article here—to conducting research on a data set that comprises groups of people using the internet in a manner consistent with anti-oppressive research (Potts and Brown). 

Collecting, Pre-Coding, Coding: Slow Actions and Deliberate Capture 

 After creating research questions that facilitate slow coding, we can proceed to the construction of the project, the ethical considerations, the data collection, and then the interpretive framework (with the latter two components comprising slow coding as a practice itself). In essence, slow coding represents an attention to the fact that digital expressions of life are not merely communicative instances, but rather extensions of life online. Thus, we can use methods of capture and interpretive frameworks to understand the stories the data are saying; that is, we can investigate how the range of human experience translates to takeaways that matter to the questions we pose in the first place when conducting research. To that end, I offer an example of building a substantive research project that was my dissertation, offering salient examples and considerations that springboard from slow coding as a methodological approach. I detail the actual methods of enacting the project to the act of parsing the data and then coding it.  

In working on my dissertation, I followed Heidi McKee and James Porter in The Ethics of Internet Research: A Rhetorical, Case-Based Process and “Digital Media Ethics and Rhetoric,” taking a multi-stage approach to research: 1) data collection, 2) pre-coding, which involved slowly reading through the tweets in an extant archive (gathered using methods below), excluding those that were retweets and from organizations, clinicians, providers, or other public health officials (i.e., applying exclusion criteria), and pre-coding those relevant to the research project to derive thematics; and 3) coding them to establish three case studies based on these themes that reveal how users showcased their own sexual health literacy in relation to the topics at hand. 

Collection 

 Using an insurgent appropriation born from Indigenous methodologies (Gaudry; Tuck and Yang), I adapted internet- and social media-based methods for gathering and analyzing the data. Thus, tweets were gathered as data using an automated, self-populating Twitter Archiving Google Sheet (TAGS), a system developed by Martin Hawksey that uses Google Sheets’ functionality and Twitter’s then-open API to conduct a keyword search across public Twitter users. This search began fall 2018 and continues, refreshing every hour until I am locked out. The keywords used were the hashtags #PrEP and #Truvada, and these were used to attune the data collection to users talking about their sexual health in relation to ongoing changes surrounding medication, culture, and health. These keywords were also selected because they have been prominent in the cultural milieu of queer and trans people of color since the advent of new HIV-prevention medication. Tweets collected through the TAGS system were aggregated in a Google Sheets document, along with usernames, user-made bios, timestamps, avatars, and locations (when available). For the hopeful slow coder, proceeding from data collection continues to attend to a research project’s dimensionality, adhering epistemic parameters to standard protocol in the follow ways: understanding that research is a practice mired in colonial processes (discussed above), responding to how anonymized data defaults to whiteness because of the manner by which a human user of the internet is construed, and attending to the organizing algorithms of the internet (which privilege white sensibilities).   

Pre-coding 

My collection methods captured much meta-data for the datapoints gathered—perhaps too much, which initiated my slow approach and led me to cohere this process as slow coding. I was therefore able to use the meta-data to cross-check that the cultural content that users generated and frequented in their discrete Twitter feeds related to the topic at hand and their identity (i.e., checking to see who the user is and what they talk about online—learning who they are and what their life is about; though, of course, information associated with Twitter accounts is not always accurate). In creating slow coding as a digital cultural rhetorics methodological practice, I made the important but complex decision to not anonymize the data collected; identity is integral to internet and technology use, as I touch on above, and anonymizing the data would lead to poor conclusions regarding my research questions because cultural and racial identity is vital to answering the research questions in the first place. 

That said, I presented the data in the dissertation—and subsequent publications—in a manner that only recounts identive aspects of users as derived from contextual elements, including general locations (e.g., Atlanta featured heavily in my data as it is often called the Black queer capital of the world), other tweets, biographic information, and photos that were not of the user but posted (typically memes). I did not nor will I ever use Twitter usernames, show avatars, or use any other identifying information in my writing, stewarding users’ data by using password-protected hard drives to store data gathered. The stewardship I enacted requires, again, the creation of a project that cannot function without care and deliberation in mind. In this way, I was able to approach the necessary messiness of approaching consent when working with semi-public data, users who did not respond to direct messages, and the general unwieldiness that accompanies social media platforms as research sites. Of course, no approach will ever be perfect, especially regardless of IRB approval (as in my case, wherein my project was deemed exempt)—but again, care and deliberation and substantive protection protocols must be derived.  

Because my data collection was self-populated as users’ generated content, I created a copy of the overall archive and effectively ended data collection for the dissertation in June 2020. From this document, I began pre-coding by following my inclusion/exclusion criteria, focusing solely on non-specialist posts in the data collection (i.e., posts from non-medical experts). To conduct pre-coding, with a collection of about 300 individual tweets and relevant posts and media after culling, I carefully read through each, highlighting ones that sparked an interest and were seemingly related to the research questions. During this stage, I also expanded on some tweets, delving into the conversational context in some cases and storing these tweets for further investigation. I also included analytic memos left in the form of comments on specific cells containing interesting tweets, and they were later factored into analysis. When this stage was completed, included tweets and their accompanying meta-data were compiled in a Microsoft Excel spreadsheet and manually entered into Dedoose, a qualitative coding software.  

Coding 

Slow coding entails a close attention to intersectional theory as it pertains to online life, and in pushing against the textual notion of anonymized data—which voids those meaning-rich cultural expressions of daily life online—I used pre-coding to lead into more comprehensive coding in line with constructing grounded theory. Following a two-cycle approach (Saldaña), I derived three overarching themes across the data that showcased three contextual factors that garnered the most attention on Twitter. Following Johnny Saldaña, and as part of the first cycle of constructing a grounded theory, holistic coding was used as it “is applicable when the researcher already has a general idea of what to investigate in the data . . . [which can be] preparatory groundwork for more detailed coding of the data” (119). In this round of coding, then, I analyzed the selected tweets and accompanying meta-data, which I construed as experiential data that fleshed out the tweet given that they formed contextual vignettes for conveying information. As such, in this initial coding stage, I derived initial codes such as HUMOR, EDUCATION, and CRITICISM, among others, based on an assumed purpose of the tweet in relation to the colonial conditions writ large. With these initial codes, I then moved to the second round of coding. 

With axial coding as the second cycle, I prioritized “properties (i.e., characteristics or attributes) and dimensions (the location of a property along a continuum or range) of a category” (Saldaña 159). As the follow-up to the first cycle of coding, axial coding allowed me to dwell in those “components [of] the conditions, causes, and consequences of a process—actions that let [me] know ‘if, when, how, and why’ something happen[ed]” (Saldaña 159). In other words, axial coding affords an interconnected approach to data, including parsing through tweets related to the specific utterances gathered in the finalized data set and then constellating them amongst each other and the broader forces at play that led to the specific instance of the tweets. Thus, through this round of coding—which I spent months doing to fully flesh out the case studies I eventually derived—I was able to derive codes based on the contemporaneous events, cultural complexities, and oppressive forces tied to them that led to the tweets themselves. With the coding and memos, I derived three thematics (i.e., community health practices wrought during the HIV/AIDS crisis of the 1980s and 90s, ad hoc networks for sharing information on insurance and healthcare providers, and countering capitalist biomedical systems), which were then used to construct the case studies comprising the project. I was only able to create these case studies by taking my time, and how I went about collecting the data and coding them might take more time than it would otherwise, but this deliberate slow research was necessary to facilitate my commitment to anticolonial research within a digital environment.  

Conclusion: The Ethics of Slowly Learning an Internet Life 

 The researcher and the research subject, site, and project intermingle histories of colonial violence that demand more, slow, thoughtful attention from us. As such, slow coding entails a deliberate approach to building the project itself, including the ethics underpinning the work itself. In my case, I grounded my work in a relational ethics tied to a theoretical framework grounded in Indigenous concepts of relationality, which steered my analysis of tweets amongst broader forces of oppression, directly shaping the remainder of my methodological considerations (Arola; Riley-Mukavetz; Wilson). I also followed the Association of Internet Researchers’ ethics of internet research (franzke et al.), asking myself how data would be traceable and if it could be potentially harmful to the Twitter users when published and whether identifying information was required. Thus, relational ethics set the parameters by which I stayed with the data, simmering in the complex lives of people taking to social media to talk about a critical facet of their lives. Then, via slow coding and the layers of considerations above stacked up on one another, I set out to learn about online lives and let them story my dissertation project. 

 Here, at the end of this piece, I foreground this centrality as a deep, epistemological requirement of slow coding as a practice. If you cannot build a project made for slow coding, then build a different project. I will say, though, that much of what I have found in the data via this process is joy—the bliss of queer and trans people of color being in community despite everything in the world, including the technologies that bring them together, tearing them down. This joy is precious and requires much of us as researchers. I hope that what slow coding offers is a glimpse into working in the ebbs and flows of the liminal spaces that lets community be what it is—joyful work that rescinds the wickedness too often central to how the world works. 

Works Cited 

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 End Note

^[1]Here, I mean to render the human as conceptual parameters by which we cohere history, culture, whiteness, colonialism, and cisheteropatriarchy to what we think of as the prototypical human user of the internet (Brock). Indeed, André Brock highlights how the internet functions as “a social structure [that] represents and maintains white, masculine, bourgeois, heterosexual and Christian culture through its content” (1088). The internet, then, amid the varying tales that comprise its cultural import, is a mirror to the project of humanness—a conceit I intermingle within the critical vantage of my thinking in this piece.