The interaction deficit

For over a decade now social media have been turning our interactions into data. Once relations, dialogue and connections are abstracted into data, they are opened up to analysis, comparison and engineering. Occasionally we get little glimpses of how these data fold-back into the way people connect and relate. Instagram’s announcement of their new feature for tracking user interaction is one of those moments.

Presented as a kind of social media training tool, it is suggested that this new feature allows the user to better manage their social media relationships and to better curate their feed. What it points toward, I'd suggest, is a move toward using data to identify a kind of interaction deficit. This can be understood as an attempt to use data to gauge a shortfall in expected communication levels. Where an interaction deficit exists in Instagram this new feature allows it to be identified so that the user can then choose whether to end the connection and sever links.

Turning interactions into data inevitably changes the way that they are understood. In this case, social interaction is seen as kind of capital that can accumulate and be measured - enabling individuals to be assessed for their contribution to the network. Here it is being suggested that we should use the data and rankings to train ourselves in our social media use and to manage our networks. This is to use a lack of accumulated capital to judge the network value or use value of those we connect with. A friendship or interaction economy is formed in which we are judged and ranked in a marketplace of engagement based on our volume and frequency of connection. The logic of this new feature is that underperforming members should be identified and cut from the network.

In stark terms, and in keeping with the expanding scope of data analytics, once interactions are turned into data they can be analysed and measured. Which also means the data then fold-back into future interactions and connections. With this move, past interactions fold into future connections, dialogue and network-making. And so, through these feedback loops, the the data take on an active or performative role int he shaping of networks and social relations. Social media often seek to use data to train social media users - giving us our social media performance back to us in basic numbers and in more advanced analytics.

Turning data into interactions allows apps like these to identify and report interaction deficits (or, potentially, surpluses) that can be judged against newly and self-reinforcing standards or norms. In a ‘world of indicators’, as it has been described, we even end up with benchmarks against which to judge interactions, connections and relations.

One way to see this is as the emergence of a kind of negative friendship. This is a form of friendship based upon the absence of interaction. These can be understood as social relations based upon what the data suggests is missing. Wendy Espeland has argued that metrics often have the effect of ‘stripping away of narrative’. Once we abstract the world into numbers and data, we remove existing narratives and new stories are then told with the data. In the case of this Instagram feature and other social media data, the narratives are stripped away from those interactions and relations, leaving a space in which new narratives can be developed. In this case, they are reduced to a league table of ‘accounts’. These social connections are seen through the logic of social media and the drive for ever greater volumes of engagement. And so they are then narrated on those terms.

Inevitably all of this suits the social media platforms. This approach to analysing our own data and creating rankings of worth promotes the use of the platform. It does more than this though, it has an impact beyond the users of such features. Once people sense that they might be judged for the frequency and volume of their interactions, they are likely to start interacting to match the expected performance levels. The visibility of interactions through data will inevitably fuel the interactions themselves. The fear driving this is again defined by the logic of social media: no one wants to lose followers.

As networks expand the longer people use social media and the more people joint them, we should expect more tools that are aimed at analysing and trimming them back. There will be many future initiatives aimed at making networks and feeds manageable. These solutions are likely to be based upon creating a sense of competition through ranking. And so social media users will have in mind the need to interact often and widely so as to maintain their social media presence and their place in the network. They will know that they will be judged for their value to the network based upon their data.

This, it could be argued, is not an isolated instance. Social media run on interaction deficits. This Instagram feature is part of that. It is important not to overlook the way that an interaction deficit might also shape visibility in the algorithmic prioritisation of content.

The Instagram case is one that calls upon the user to analyse the rankings of interlocutors, but interaction deficits are also part of how the algorithms are deciding what content to prioritise in news feeds. Perhaps, in this sense, a lack of algorithmic visibility will exacerbate an existing interaction deficit - deproritising content from those we interact with less and therefore making future interactions less likely to occur. If we don't see content in the first place we are unable to respond or enter into dialogue.

Turning interactions into data means that they become part of the politics of circulation within social media. They also become subject to analysis, which in turn will implicate the relations and networks that they measure. The example of Instagram's new feature is one instance in which we are presented with this interaction deficit as a self-training tool, this is important but its reach goes further.


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