Algorithms, as constitutive elements of online platforms, are increasingly shaping everyday sociability. Developing suitable empirical approaches to render them accountable and to study their social power has become a prominent scholarly concern. This article proposes an approach to examine what an algorithm does, not only to move closer to understanding how it works, but also to investigate broader forms of agency involved. To do this, we examine YouTube’s search results ranking over time in the context of seven sociocultural issues. Through a combination of rank visualizations, computational change metrics and qualitative analysis, we study search ranking as the distributed accomplishment of ‘ranking cultures’. First, we identify three fo...
When users search online for content, they are constantly exposed to rankings. For example, web sear...
We propose a simple model of an idealized online cultural market in which N items, endowed with a hi...
This article presents an exploratory study of the network of associations among 22,141 YouTube music...
Algorithms, as constitutive elements of online platforms, are increasingly shaping everyday sociabil...
Building on Rieder’s (2015) approach to describe the structure and dynamics of outputs of the YouTub...
YouTube’s “up next” feature algorithmically selects, suggests, and displays videos to watch after th...
YouTube's "up next" feature algorithmically selects, suggests, and displays videos to watch after th...
Over the past 15 years, YouTube has emerged as a large and dominant social media service, giving ris...
Drawing on the idea of platform observability, this paper combines computational and qualitative met...
This article is concerned with how different agencies play out in shaping public debate online and, ...
Ranking algorithms are the information gatekeepers of the Internet era. We develop a stylized framew...
Ranking algorithms are the information gatekeepers of the Internet era. We develop a stylized framew...
YouTube’s ‘up next’ feature algorithmically suggests videos to watch after a video that is c...
Ranking algorithms are the information gatekeepers of the Internet era. We develop a stylizedframewo...
International audienceThis article presents an exploratory study of the network of associations amon...
When users search online for content, they are constantly exposed to rankings. For example, web sear...
We propose a simple model of an idealized online cultural market in which N items, endowed with a hi...
This article presents an exploratory study of the network of associations among 22,141 YouTube music...
Algorithms, as constitutive elements of online platforms, are increasingly shaping everyday sociabil...
Building on Rieder’s (2015) approach to describe the structure and dynamics of outputs of the YouTub...
YouTube’s “up next” feature algorithmically selects, suggests, and displays videos to watch after th...
YouTube's "up next" feature algorithmically selects, suggests, and displays videos to watch after th...
Over the past 15 years, YouTube has emerged as a large and dominant social media service, giving ris...
Drawing on the idea of platform observability, this paper combines computational and qualitative met...
This article is concerned with how different agencies play out in shaping public debate online and, ...
Ranking algorithms are the information gatekeepers of the Internet era. We develop a stylized framew...
Ranking algorithms are the information gatekeepers of the Internet era. We develop a stylized framew...
YouTube’s ‘up next’ feature algorithmically suggests videos to watch after a video that is c...
Ranking algorithms are the information gatekeepers of the Internet era. We develop a stylizedframewo...
International audienceThis article presents an exploratory study of the network of associations amon...
When users search online for content, they are constantly exposed to rankings. For example, web sear...
We propose a simple model of an idealized online cultural market in which N items, endowed with a hi...
This article presents an exploratory study of the network of associations among 22,141 YouTube music...