Authors

Discussion

This research aims to explore how user-driven media platforms, understood as sociotechnical alliances of human (developers, editors and moderators, users) and non-human actors (algorithms, norms and policies), form and develop different (critical) algorithmic publics in unfree media system. 

The empirical materials include the cases of two Russia’s user-driven media platforms: Pikabu and TJournal. Both sites are notable for their high level of automation, including elaborated algorithms of ranking, recommendation, and sorting. Both sites are famous for fostering a free atmosphere, which enables users to publish and discuss social and political issues alongside entertaining content. The research investigates how similar procedural formulas of algorithms get different social meanings within different cultural environments of two media platforms.

The research employs triangulation approach and use three sources of data: interviews with founders and workers of two media platforms (N≤7), users discussions regarding algorithmic curation and prioritisation on the platforms, platforms' official documents about rules and policies.