The Essential Context: Theorizing the Coming Out Narrative as a Set of (Big) Data

Considering the impact of “big data,” particularly predictive or anticipatory technologies, in terms of their ability to organize assumptions about gender and sexual identities reveals the primacy of (a) coming out narrative as (an) a priori, regardless of the perspective taken. Moreover, these technologies presume a gender normative or heterosexual subject because the accumulation and construction of the necessary algorithm(s) and data rely on the binary structure of conformity or deviation from a norm. An example: Google Scholar lists JFK as my co-author for a paper for which I used a quotation as an epigraph (see Figure 1). Their crawler reads it that way. When contacted, their response was to make the journal change its citation method to conform to their preferred format. Thing is, the journal already does, but the Google algorithm only recognizes things that agree with its normative values: those that don’t must change.

Figure 1. Google Scholar screen capture. November 14, 2015

Indeed, tracing the roots of the unstated construction and/or assumption of the coming out narrative also reveals a tendency in the scholarship to assume that this cultural construct applies primarily to GLBTQ youth (Hammack & Cohler, 2011; Waitt & Gorman-Murray, 2011). Similarly, the scholarship also presumes that GLBTQ youth will be the ones most likely to be seeking out digital technologies (Cover & Prosser, 2013). In this way, the coming out narrative becomes both rationale and outcome whether one considers the mass customization potential of big data as democratizing and liberating, scrutinizing and stereotyping, or necessarily intrusive but manipulable. The principal driving force remains the ability to identify and to predict consumers (consider, for example, the sale of predictive data for rape victims). Not only does this place GLBTQ users into the position of consumers instead of citizens, it produces rather than identifies (a) “queer” data set(s), whose derivation depends on deviations from the (heteronorm), against which all others will be assessed. This data set essentially comprises the (new) coming out, rendering even the most “strategic outness” nearly impossible to manage once digital technologies come into play (Orne, 2011).

Therefore, whether one adheres to or deviates from the data set, these become the defining criteria — not according to GLBTQ communities, members, individuals, participants, allies, etc., but according to the collection of algorithms of so-called “contextual” or “associative” search engines. These often take the form of meta-search engines which glean data from portals, clicks-through, and the more familiar web search engines such as Google and Yahoo. As Search Engine Watch puts it, the ultimate goal for these search engines is “precise results and advertisements” particularly for mobile users (J. Slegg, 19 Feb. 2014). As well, these users are presumed to be heavily skewed towards a youthful demographic both in terms of spending and in mobile usage.

In light of these algorithmic identifications, I offer this question to explore the role algorithms have in coming out narratives: What are the multiple and simultaneous modes of understanding the alternatively liberatory and constraining potentialities for the big data provided by contextual or associative search engines? I’m especially interested in their ability to determine and to instantiate what can only be described as “control” data, in all senses of that term.

As we address this question, I am reminded of Alex Doty’s axiom that any text can be a site for a queer reading in order to locate such an understanding of these data accumulation technologies. Our investigation affords an opportunity to evaluate and elucidate the critical (and commercial) bias towards a youthful audience, especially in terms of constructions and conceptions of GLBTQ identities.

References and Additional Resources

Anderson, Eric. “Updating the Outcome Gay Athletes, Straight Teams, and Coming Out in Educationally Based Sport Teams.” Gender & Society 25 (2011): 250-68.

Bracke, Sarah. “From ‘Saving Women’ to ‘Saving Gays’: Rescue Narratives and Their Dis/continuities.” European Journal of Women’s Studies 19.2 (2012): 237-52.

Cover, Rob and Rosslyn Prosser. “Memorial Accounts Queer Young Men, Identity and Contemporary Coming Out Narratives Online.” Australian Feminist Studies 28 (2013): 81-94.

Craig, Shelley L. and Lauren McInroy. “You Can Form a Part of Yourself Online: The Influence of New Media on Identity Development and Coming Out for LGBTQ Youth.” Journal of Gay & Lesbian Mental Health 18 (2014) :95–109.

Hammack, Phillip L. and Bertram J. Cohler. “Narrative, Identity, and the Politics of Exclusion: Social Change and the Gay and Lesbian Life Course.” Sexual Research and Social Policy 8.3 (2011): 162-82.

Kahne, Joseph, Ellen Middaugh, and Danielle Allen. “Youth, New Media, and the Rise of Participatory Politics.” Youth, New Media and Citizenship (2014).

Orne, Jason. “‘You Will Always Have to ‘‘Out’’ Yourself’: Reconsidering Coming Out through Strategic Outness.” Sexualities 14.6 (2011): 681–703.

Taylor, Yvette, Emily Falconer, and Ria Snowdon. “Queer Youth, Facebook and Faith: Facebook Methodologies and Online Identities.” New Media & Society (2014): 1-16.

Vivienne, Sonja and Jean Burgess. “The Digital Storyteller’s Stage: Queer Everyday Activists Negotiating Privacy and Publicness.” Journal of Broadcasting & Electronic Media 56.3 (2012): 362-377.

Waitt, Gordon and Andrew Gorman-Murray. “‘It’s About Time You Came Out’: Sexualities, Mobility and Home.” Antipode 43.4 (2011): 1380–1403.

Zuckerman, Ethan. “New Media, New Civics?Policy & Internet 6.2 (2014): 151–168.


I appreciate your survey response: it raises a number of interesting and significant questions, and it makes me realize that I haven't given enough thought to algorithms.

This point really stood out to me: "whether one adheres to or deviates from the data set, these become the defining criteria — not according to GLBTQ communities, members, individuals, participants, allies, etc., but according to the collection of algorithms of so-called 'contextual' or 'associative' search engines."

It has me thinking broadly about the way people are shaped by even the most mundane interactions – with people, with environments, with tools. While these interactions may go unnoticed (algorithms seem so invisible, just working in the background of our online activities), these interactions can shape perceptions of one another, of the self. If these systems operate according to an established binary “of conformity or deviation from a norm,” then any interaction with this system shapes the user’s perception of normativity. That’s significant!

So we are influenced by the algorithm (we’re shaped by our interaction with it), but who influences the algorithm?

As demonstrated by your example, these algorithms are limited: they operate according to a specific model, and affected parties have little (to no) room to influence it. Even though your journal followed the “correct” citation method, even though you contacted them to correct the error, JFK remains your co-author. "It produces rather than identifies” normative values, potentially taking a level of control away from communities who would otherwise do the work of defining and identifying themselves.

As a result, algorithms don’t appear to be very “democratizing and liberating.” Instead, they seem to constrain online behaviors and quietly influence our sense of self. It makes me uncomfortable. I wonder what JFK would say. 

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