Digital Demography: Reflections on a tabular data method(ology)

Hello! Last time round, I talked about my Sherman Centre research project into the annual Saveur food blog awards, where I track the demographic data of recognized food bloggers to determine if/how the Awards promote the digital food work of underrepresented groups. While that earlier blog post focused on my first forays into the ‘technical stuff’ of data collection, today I’d like to work through some features of what I call my tabular data method(ology)—the conceptualization of which, throughout my residency, has oftentimes loomed larger than the data it helps me to collect. More specifically, this method(ology) refers to an ongoing process in which I repeatedly question, critique, and reconsider the traditional demographic categories that organize my tabular data—including blogger gender, sexuality, and race/ethnicity—while also working to responsibly and transparently document my working definitions, coding strategies, and theoretical assumptions. 

The thematic sections below organize some of this thinking, supported by my reading into feminist-demography studies (to help me think through my project’s connections to census enumeration and academic surveillance) and critical data and media studies (to problematize connotations of digital data neutrality and availability).

Why method(ology)?

I use the phrase ‘tabular data method(ology)’ to remind myself that the decisions I make about how to do this work bring with them a number of theoretical, epistemological, and disciplinary assumptions. My project aims to produce a statistical summary of the diversity of the Saveur archive; this quantitative method, with its connotations of objectivity, at first felt very distant from my training as a literary and cultural studies scholar. Upon looking further into demographic theory, however, I discovered the ongoing work of feminist demographers who called for the merits of a mixed-methods approach, which can help to ensure that quantitative measurements are thoroughly contextualized and that demographers attend to their own positionality and guiding assumptions when collecting and analyzing population data (Riley; Williams). For my own part, the manual collection of demographic data from narrative blog posts meant that I was already deploying a mixed-methods approach, in which I read many (many!) blog posts for expressions of identity. I aimed to extend this mixed approach into a methodology by annotating my tabular data with contextual details from the blogs in question—e.g. in what way did a blogger disclose their age, race, or sexuality?—as well as with reminders of the problematic histories of the demographic categories I consider.

Enumeration, Classification and Surveillance

In a study of racial and ethnic census categories and their impact on everyday experiences of identity in the contemporary United States, Candice J. LeFlore-Muñoz comments that such categories reflect “the construction of race by those who [have] the power to define it” (2). Through door-to-door interviews and the processing of self-administered surveys, enumerators have historically been entrusted with state power to re-code individuals to suit a dominant narrative—for example by leaving a blank space to indicate that a person was white, in the 1850 and 1860 US censuses (LeFlore-Muñoz 36), or by changing the gender of an “unmarried partner” to create a different-sex partnership, up until the 2000 US census (Velte 84)^[1]—and to produce a manageable data set. 

My work involves recording blogger demographics, and I needed my data set to be workably consistent, but the power of the enumerator sat—and sits—uneasily with me. While I do categorize bloggers according to ‘traditional’ demographic categories, I also keep a record of bloggers’ own references to (for example) their race, ethnicity, or heritage, so I can use these (anonymized) statements to problematize any racial ‘category’ as universally understood or experienced. 

Furthermore, my method(ology) also aims to problematize the “big data divide”—the gap between those who collect, harvest, and analyze data and those who are targeted by those practices (Andrejevic). Put differently, the enumerator of digital demographic data frequently enacts a panoptic gaze, which assumes that “the watchers do not touch the watched and vice versa” (Kim). Throughout my residency, I have been prompted by these commentaries to insert my own research practices directly into the data set, specifically by including the coding strategy used to classify a blogger within my tabular data. Is a blogger’s sexual orientation, for example, recorded according to a direct statement of identity or according to implied evidence, such as pronoun use and passing references to partners? Though this strategy certainly doesn’t mark the end of my ethical reflection and engagement, it does keep blogger voices central to the research process, and also helps to hold me accountable for the classificatory decisions I make.  

The Value of “Unknown”

My efforts to continuously reflect on the decision-making processes supporting my research have also led me to place great value in the incompleteness of this work. Not every blogger fills their “About Me” page with details of their lives. For example, one blogger tells followers that she won’t reveal her age or birthday, so don’t bother asking; another operates her blog under the username from her favourite video game. Part of what my broader research tries to understand is how the decision to disclose various aspects of one’s identity, or not, is shaped by the platform affordances and conventions of the blog itself (Morrison). The blog is a confessional genre which trades in self-disclosure (when was the last time you visited a food blog without an “About Me” page?), but at the same time, gestures of inconsistency and contradiction can also be used to convey an impression of authenticity (think of a long-standing blog that remarks self-deprecatingly on the quality of its earliest posts). The example of the WordPress Veggie theme—which includes an “About” tab, a blogger profile, and a sample text reference to “one of my husband’s favourite recipes” (all for the price of $80 CAD)—reminds us that this genre, and its potential costs and profits, expects to do business with a particular clientele.

In light of this, my method(ology) works to avoid equating visibility with politics (Morrow et al.). On the one hand, “it is important to ask which lives, and which choices, even have the opportunity to become public and/or politicized on the Internet” (529), and in this particular online world of the food blog. At the same time, bloggers have their own reasons and motivations for de/emphasizing various aspects of their identities and biographies, and I don’t wish to hold them accountable for any representational shortcomings that should more appropriately be directed to the adjudicating body of Saveur. In light of these considerations, coding a demographic value as “unknown,” where applicable, is just fine with me.

Going Forward

So: where to next? How does this tabular data method(ology) help me move forward with this research project, and my goal of assessing the diversity of the food blogosphere according to Saveur? I still have a lot of questions, but I’m glad to be able to share them on a blog and to open myself up to feedback, insights, and suggestions from others. As feminist geographers Oona Morrow, Roberta Hawkins, and Leslie Kern ask while reflecting on their own social media research projects:

But where do we go from here? […] We would encourage online researchers to be more intentional about positionality by opening themselves and their research processes up to the same conditions of transparency, vulnerability, and scrutiny as their online research participants […] A good place to start being more transparent would be updating your web profile to read ‘researcher’ or blogging your field notes.

(Morrow et al., 538)

Here’s to blogging field notes!

Emily

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Footnotes

^[1] These examples are from an American context, but my reading has touched on census and population survey practices around the world in order to reflect the widespread geography of bloggers in the Saveur archive.

Works Cited

Andrejevic, Mark. “The Big Data Divide.” International Journal of Communication, vol. 8, 2014,  pp. 1673-1689.
Kim, Dorothy. “Social Media and Academic Surveillance: The Ethics of Digital Bodies.” Model
View Culture: Technology, Culture, and Diversity. 7 October 2014.
LeFlore-Muñoz, Candice J. I’ve Got a Story to Tell: Critical Race Theory, Whiteness, and
Narrative Constructions of Racial and Ethnic Census Categories.2010. Bowling Green
State University. PhD dissertation.
Morrison, Aimée. “Facebook and Coaxed Affordances.” Identity Technologies: Constructing
the Self Online, edited by Anna Poletti and Julie Ran, University of Wisconsin Press, 2014.
Morrow, Oona, Roberta Hawkins, and Leslie Kern. “Feminist Research in Online Spaces.” 
Gender, Place and Culture, vol. 22, no. 4, 2015, pp. 526-543.
Riley, Nancy E. “Challenging Demography: Contributions from Feminist Theory.” Sociological
Forum, vol. 14, no. 3, 1999, pp. 369-397.
Velte, Kyle C. “Straightwashing the Census.” Boston College Law Review, vol. 61, no. 1,
2020, pp. 69-127.
Williams, Jill R. “Doing Feminist-Demography.” International Journal of Social Research
Methodology, vol. 13, no. 1, 2010, pp. 197-210.