LAUSR

Older women have been absent from considerations of YouTube’s algorithmic structure on the visibility of videos made by people from marginalised groups. Older women are keen makers of YouTube videos in many genres but their contributions are less visible than those of younger women due to YouTube’s algorithmic processes. These reflect and reproduce the power relations and inequalities of society (Dan McQuillan 2016) including the social devaluation of older women (Barbara Macdonald and Cynthia Rich 1985). This is in the wider context of online media research in which older women are still assumed to be reluctant content creators and motivated to share videos only with a family and close pre-existing friends (Susan M. Ferreira, Sergio Sayago and Josep Blat 2017). My commentary here draws on my observations of 46 women YouTubers aged 60+, their videos and their interactions. One starting point for considering the influence of YouTube’s algorithmic structures on older women’s production practices and visibility is foregrounding the pervasive power of YouTube’s algorithmically determined search and recommendation functions. It is well understood that Google, YouTube’s owner, uses predictive analytics based on individual and collective characteristics and past watching habits to offer viewers a continual stream of videos they want to see, and at the same time deliver target audiences to advertisers. This is effective: 70% of watching time comes from these automated recommendations (Kevin Roose 2019). This has material consequences for YouTubers and viewers as to visibility and production practices. YouTube appears to favour the most popular and gender normative content (Siqi Wu, Marian Andrei Rizoiu and Lexing Xie 2019; Sophie Bishop 2018). I noted older women’s videos seem to less visible in the recommendations: they are unlikely to be recommended to viewers unless the video currently being watched is by an older person. Moreover, video production practices are shaped by beliefs about what will lead to higher rankings and larger audiences. The exact workings of the ever-evolving algorithm are commercially protected but information has been deduced from Google personnel (for example, Alex Beutel, Paul Covington, Sagar Jain, Can Xu, Jia Li, Vince Gatto, and Ed H. Chi 2018; Kevin Roose 2019) and spread through “algorithmic gossip” (Bishop 2019). These become part of what is considered legitimate or appropriate practices of making YouTube videos, whether or not the YouTuber explicitly intends them to improve their ranking. A different starting point for understanding algorithmic influence is to consider the benefits of being “under the radar.” As Peggy Phelan argued, invisibility can be