EventHive Through the Lens of Actor-Network Theory

In the EventHive world, events are viewed as hives of activity. These hives are made up of people, media content, organisers, mobile devices, computers, places, and the list goes on. In short, the hive is a network made up of all of the human and non human actors that make any given event, an event. EventHive’s role is to facilitate interaction between all the nodes in the network, enhancing the live experience for its users.

One basis for this way of thinking about live events resides within academia and a methodology known as Actor Network Theory (ANT). ANT was first developed by Bruno Latour, John Law, and Michel Callon, but has since taken on a life of its own. In this blog post, Jack would like to introduce Actor Network Theory and consider how it might be useful for thinking about events and the EventHive platform.

Introduction to Actor Network Theory

At its heart, Actor Network Theory is a sociology of associations and it is a methodology that allows us to look at the entwinement of society and technology, rather than considering these things to be distinct[1]. It argues that technology and society are the effects of patterned networks of human and non human actors, often described as heterogenous networks[2]. When these actors reach an agreement and begin to pull in the same direction, technologies and societies function.

A live event, such as a football match, is such when all of the human and non human actors that constitute that event (e.g. crowd, footballers, referee, stadium, the institution of football, the sponsors, the grass etc.) work in agreement. The process of reaching an agreement and common agenda is known as translation. When the human and non human actors in the network have reached a state of agreement this is known as stabilisation[3].

The advantages of the Actor Network Theory method is that it opens up one’s analytical frame to non-human actors and takes any agency that they might have seriously. This is called super-symmetry, where no a priori assumption that one actor has greater agency over another is made[4].

However, some academics argue that ANT is narrow in outlook; everything in the world has to be thought of as super-symmetric actor-networks where no other properties and culture can exist[5]. In addition, the ascription of agency to non-human actors is considered to be problematic. Some argue that only humans (with ‘consciousness’) can have agency[6]. And the fact that a lot of ANT research focuses upon human actors is cited as evidence of this[7].

Heterogeneity

As I mentioned already, Actor Network Theory’s acceptance of heterogeneity (human and non human actors) is one of its defining features. For EventHive, this way of thinking encourages us to take into consideration other non-human actors that might co-construct a live event. One could argue that the Web and Web-enabled mobile devices are increasingly important actors in live experiences such as music festivals. These technologies allow audiences to capture and communicate with other people the experience they are having in real time.

Consequently in our service, user-generated media associated with events (photos/videos) are assigned equal importance alongside people at events. Our event feed pushes notifications about your friends and the amount of people who are at events, media that has been shared about these events, as well information about organisers and ticket purchasing. The event page allows the user to see different human and non actors relevant to that event, such as the the people attending, the organiser, photos, videos, Tweets, ticket vendors, venue information etc. In effect, each individual event hive (event page) is a snapshot of the actor-network that constitutes that event.

Translation and Stabilisation

When thinking about EventHive’s strategy in Actor-Network Theory terms, we need to think about how our application, or we as a node (a node which actually encompasses a separate EventHive human/non-human actor-network) can become part of a live event’s actor network. Ultimately, EventHive should shape the process of translation and become part of the event’s stablised actor-network, becoming commonplace and ubiqutous at live events. To address this, we need to ask how we can add value to events so event organisers and audiences will want to use our application at, and for, a live event.

Summary

Jack’s blog post has attempted to illustrate how a well known methodology within academia – Actor-Network Theory – can be applied to thinking about event-based social networks and the design of event-based social networking services. Actor Network Theory reminds us that what makes an event, an event, includes more than just human actors. In additon, we are encouraged to think of events as processes, or networks of activity, that go through stages of translation and stablisation. For EventHive, it is down to us to work out how we can shape this process of translation and become part of a stablised event actor-network.

References

[1] Bruno Latour, “Introduction: How to Resume the Task of Tracing Associations,” in Reassembling the Social: An Introduction to Actor-Network Theory (Oxford, UK: Oxford University Press, 2005), 9.

[2] John Law, “Notes on the Theory of the Actor Network: Ordering, Strategy and Heterogeneity,” Systems Practice 5, no. 4 (1992): 340.

[3] Sergio Sismondo, “Actor-Network Theory,” in An Introduction to Science and Technology Studies (Chicester, UK: Blackwell Publishing, 2009), 87.

[4] Sismondo, “Actor-Network Theory,” 87.

[5] Nick Lee and Steve Brown, “Otherness and the Actor Network: The Undiscovered Continent,” American Behaviour Scientist 37, no. 6 (1994): 774.

[6] H. M. Collins and S. Yearley, “Epistemological Chicken,” in Science as Practice and Culture, ed. A. Pickering (Chicago: University of Chicago Press, 1992), 318.

[7] Sismondo, “Actor-Network Theory,” 90.