The evolution of television and the ways in which it is consumed has, in the last two decades, gone hand in hand with the evolution of online media sharing platforms. Media sharing platforms have caused a new disruption (Uricchio, 2004) in the experience of broadcast television, entering the scene and providing new alternatives to ordinary programming. The concept of flow, as introduced by Williams in the 1970s, has been continuously updated through the decades to fit the new modes of fruition that accompany content viewing (van Dijck, 2013; Cox, 2018), reaching a point in which the active role of the user has become a key aspect of it. That is the reason why, trying to imagine a phase that could follow the concept of personcasting (Lotz, 2007), we have decided to focus on media sharing platforms that include – in different ways – user interaction. In particular, we have decided to use TikTok as a starting point for our analysis, as we believe it is one of the main platforms that is influencing the way in which people choose to enjoy video content and is participating in the redefinition of televisual content; moreover, it is a platform that allows creators to produce videos of ever growing quality and length that make it a key competitor for users’ attention when it comes to choosing what to watch and where. TikTok’s continuous flow of content selected by an algorithm, which we are calling algorithmic flow, builds on the aforementioned different types of flow that previous media sharing platforms introduced. On the one hand, as on YouTube, it is a staccato flow, since the user has to scroll from one video to the next, on the other, the way it is distributed follows a broadcast-like logic, with the algorithm acting as a broadcaster. Because of the latter point we are hypothesising the concept of datacasting, a phase that that follows narrowcasting (Uricchio 2010) and works alongside personcasting. The selection of content is no longer only in the hands of users, but is done by a recommendation algorithm that suggests content based on our algorithmic identity (Cheney-Lippold, 2011; Gilbert, 2022). In order to fully understand the extent to which users rely on algorithmic suggestions when it comes to enjoying online content we are carrying out 20 in depth interviews with people aged 18-34. These interviews are mostly focused on people’s relationship with TikTok, and try to understand how important the algorithmic flow is to their enjoyment of the platform, building upon the idea that more and more people have started looking for a lean back experience when it comes to enjoying all kinds of media.
Algorithmic Flow and Datacasting. Exploring the Possibility of a New Mode of Fruition for Content Viewing / Firth, Ellenrose. - (2023). (Intervento presentato al convegno ECREA Television Section Conference: Redefining Televisuality: Programmes, Practices, Methods tenutosi a Potsdam, Germany).
Algorithmic Flow and Datacasting. Exploring the Possibility of a New Mode of Fruition for Content Viewing
Ellenrose Firth
Primo
2023
Abstract
The evolution of television and the ways in which it is consumed has, in the last two decades, gone hand in hand with the evolution of online media sharing platforms. Media sharing platforms have caused a new disruption (Uricchio, 2004) in the experience of broadcast television, entering the scene and providing new alternatives to ordinary programming. The concept of flow, as introduced by Williams in the 1970s, has been continuously updated through the decades to fit the new modes of fruition that accompany content viewing (van Dijck, 2013; Cox, 2018), reaching a point in which the active role of the user has become a key aspect of it. That is the reason why, trying to imagine a phase that could follow the concept of personcasting (Lotz, 2007), we have decided to focus on media sharing platforms that include – in different ways – user interaction. In particular, we have decided to use TikTok as a starting point for our analysis, as we believe it is one of the main platforms that is influencing the way in which people choose to enjoy video content and is participating in the redefinition of televisual content; moreover, it is a platform that allows creators to produce videos of ever growing quality and length that make it a key competitor for users’ attention when it comes to choosing what to watch and where. TikTok’s continuous flow of content selected by an algorithm, which we are calling algorithmic flow, builds on the aforementioned different types of flow that previous media sharing platforms introduced. On the one hand, as on YouTube, it is a staccato flow, since the user has to scroll from one video to the next, on the other, the way it is distributed follows a broadcast-like logic, with the algorithm acting as a broadcaster. Because of the latter point we are hypothesising the concept of datacasting, a phase that that follows narrowcasting (Uricchio 2010) and works alongside personcasting. The selection of content is no longer only in the hands of users, but is done by a recommendation algorithm that suggests content based on our algorithmic identity (Cheney-Lippold, 2011; Gilbert, 2022). In order to fully understand the extent to which users rely on algorithmic suggestions when it comes to enjoying online content we are carrying out 20 in depth interviews with people aged 18-34. These interviews are mostly focused on people’s relationship with TikTok, and try to understand how important the algorithmic flow is to their enjoyment of the platform, building upon the idea that more and more people have started looking for a lean back experience when it comes to enjoying all kinds of media.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
