Networked Masterplots Music, Pro-Russian sentiment and participatory propaganda on TikTok

Open Access
Authors
Publication date 2024
Journal Journal of Digital Social Research
Volume | Issue number 6 | 1
Pages (from-to) 90-103
Number of pages 14
Organisations
  • Faculty of Humanities (FGw) - Amsterdam Institute for Humanities Research (AIHR) - Amsterdam School for Cultural Analysis (ASCA)
Abstract
This article investigates engagement with propagandist videos on TikTok shortly after Russia’s invasion of Ukraine on February 24, 2022, with particular attention to the role of music and comments. By repurposing the infrastructure of TikTok sound-linking, our research upholds sensitivity to how this infrastructure enables affective and participatory workings of propaganda. Exploring how a specific sound, occasionally used in combination with pro-Russian hashtags, prescribes the creation of replicable linkages between different expressive modalities, we conceptualise propagandist templates as networked masterplots. The three analysed masterplots, as we will show, not only intentionally share the use of the same song, but adapt the theatrical effect of situation and suspense on the textual level of “stickers” or messages overlaid on top of videos. A selection of fifteen pro-Russian videos using the stickers—“What if they attack?”, “I am wondering how many will (un)subscribe?”, and “I am (not) ashamed”—in combination with a techno remix of the Soviet folk song Katyusha will be at the centre of our investigation. Arguing that in Katyusha videos, situation and suspense are indivisible, we pay attention to the audiencing practices as they extend into both further memetic spin-offs and comment sections. We conclude by reflecting on how TikTok sharing not only facilitates self-expression and social activism but also enables the weaponization of content within networked memetic environments.
Document type Article
Language English
Published at https://doi.org/10.33621/jdsr.v6i1.201
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Article_6_Networked+masterplots (Final published version)
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