An important strand of sociological work in the 1990s focused on “invisible work”: the labour that was needed, in the background, to make things run smoothly. Susan Leigh Star (1999) was particularly influential in showing us that everywhere there was an infrastructure apparently standardizing, facilitating and automating, there would be people working to fit the general into the particular, to ease over tensions and to make anomalies work with the standard format. This work of making infrastructures work often simply faded into the background, was done by people considered of lower status, became thought of as mere drudgery, and was relatively invisible. Bringing this invisible work of infrastructuring into the foreground challenges the myth that standardized systems suit everyone, all the time, and that those who don’t fit are somehow at fault. From this perspective infrastructures never universally fit situations, but rather they are actively fitted to situations. Something is always silenced or omitted. Someone’s labour is being taken for granted.
In the mainstream Internet age, the sociological focus on making invisible work visible comes to the fore in a new guise. We have become fascinated by a participatory Internet, as the online infrastructures of social media make us visible to one another in unprecedented fashion. Social science and commercial enterprise alike have enjoyed a previously unimaginable access to the minutiae of everyday lives. “Big data” approaches scrape social media sites, plot networks and track trends, visualize and analyse. The advent of these large scale approaches to social data has made it possible to ask many questions that social scientists could not ask before. Suddenly, instead of interviewing people or carrying out focus groups, we are encouraged to find out about them first-hand by what they do online.
Taking seriously this world of the participatory Internet should, however, not blind us to the invisible work that it takes to make it work. It is important to remember that the versions of our lives that are portrayed online are not lives as we live them: messy, complicated, multi-sited and mobile, lived through many different forms of social connection. Big data approaches to visualizing the online world need to be complemented by small scale approaches which cross between online and offline, finding out who is leaving these online traces, what it means to them to do it and what it costs them to do so. There is a huge amount of invisible work going on – not just the uncompensated labour which produces the participatory Internet, as Christian Fuchs (2014) highlights, but also the work of making sense out of it all. It is important to explore the perspective of the myriad people who read online content but never post any themselves. We still know surprisingly little about how Internet content is consumed, and in what ways it makes a difference.
It is important, then, not to neglect the many forms of invisible work that make the Internet work. The challenge of illuminating this invisible work that surrounds and sustains the Internet is, ironically enough, labour-intensive in a way that big data approaches are not. It requires spending time with people online and offline, working out how the various aspects of their lives fit together and looking at how the Internet makes sense for them in particular, as I explore in a forthcoming book (Hine, 2015). This attitude also inspires a new project focused on the volunteer experience in the digital age that I am working on with colleagues Katrina Pritchard and Gillian Symon. We want to find out about the new opportunities for volunteers that the Internet offers, and to explore how these various forms of social media and online infrastructures are actually experienced, day-by-day, by the volunteers who make use of them.
Fuchs, C.(2014) Social media: a critical introduction. Sage: London.
Hine, C. (2015) Ethnography for the Internet: embedded, embodied and everyday. Bloomsbury Academic: London.
Star, S. L. (1999). The ethnography of infrastructure. American Behavioral Scientist, 43(3), 377-391.
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