Algorithms may now be our most important knowledge technologies, “the scientific instruments of a society at large,” and they are increasingly vital to how we organize human social interaction, produce authoritative knowledge, and choreograph our participation in public life. Search engines, recommendation systems, and edge algorithms on social networking sites: these not only help us find information, they provide a means to know what there is to know and to participate in social and political discourse.
If not as pervasive and structurally central as search and recommendation, trending has emerged as an increasingly common feature of such interfaces and seems to be growing in cultural importance. It represents a fundamentally different logic for how to algorithmically navigate social media: besides identifying and highlighting what might be relevant to “you” specifically, trending algorithms identify what is popular with “us” more broadly.
But while the techniques may be new, the instinct is not: what today might be identified as “trending” is the latest instantiation of the instinct to map public attention and interest, be it surveys and polling, audience metrics, market research, forecasting, and trendspotting. Understanding the calculations and motivations behind the production of these “calculated publics,” in this historical context, helps highlight how these algorithms are relevant to our collective efforts to know and be known.
Rather than discuss the effect of trending algorithms, I want to ask what it means that they have become a meaningful element of public culture. Algorithms, particularly those involved in the movement of culture, are both mechanisms of distribution and valuation, part of the process by which knowledge institutions circulate and evaluate information, the process by which new media industries provide and sort culture. This essay examines the way these algorithmic techniques themselves become cultural objects, get taken up in our thinking about culture and the public to which it is addressed, and get contested both for what they do and what they reveal. We should ask not just how algorithms shape culture, but how they become culture.
For a deeper dive into this topic, read Tarleton’s #trendingistrending: when algorithms become culture.