TOP 5 TINDER HACKS! BLACKBOXING ALGORITHMS IN THE DATING APP INDUSTRY

Authors

  • David Myles McGill University, Canada
  • Martin Blais Université du Québec à Montréal

DOI:

https://doi.org/10.5210/spir.v2021i0.12216

Abstract

Tinder’s swipe feature operates algorithms that have influenced a new generation of dating apps. In this paper, we argue that the mystique surrounding Tinder’s algorithms is as productive for the dating app industry as the actual technical operations they perform. We seek to understand how actors in the dating industry construct matchmaking algorithms as strategic unknowns that can be harnessed to reach commercial objectives. To do so, we mobilize the notion of ‘algorithmic blackboxing’ – how actors strategically construct algorithms as black boxes to reach certain goals – to analyze a corpus of 48 online dating guides that offer ‘best advice’ to exploit Tinder’s matchmaking algorithms. Our analysis shows that dating guides overwhelmingly construct Tinder’s algorithms as black boxes whose secrets must be unlocked for users to generate matches and, therefore, find love. The alleged unintelligibility and opacity of Tinder’s algorithms allow self-proclaimed ‘dating experts’ to sell their advice or services in the context of a speculative dating economy. To obtain more matches, dating guides promote a common injunction: to hack Tinder. They invite users to modulate their behaviors and practices to become more algorithmically recognizable. Dating guides also readily invoke rhetorical arguments that draw on statistical data produced by Tinder, which highlights the emergence of new ‘regimes of truth’ within the matchmaking industry that enact a dataist ideology. We conclude by advocating for the importance of critically examining the increasing algorithmic mediation of dating cultures at the intersection of Internet, gender, and sexuality studies.

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Published

2021-09-15

How to Cite

Myles, D., & Blais, M. (2021). TOP 5 TINDER HACKS! BLACKBOXING ALGORITHMS IN THE DATING APP INDUSTRY. AoIR Selected Papers of Internet Research, 2021. https://doi.org/10.5210/spir.v2021i0.12216

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Section

Papers M