Instrument of Mass Rejection

Abstract

This chapter compares how TikTok’s engagement-driven recommendation system amplifies pro- and anti-LGBTQ+ content in two sharply different socio-legal contexts: Nigeria, where LGBTQ+ advocacy is criminalized under the Same-Sex Marriage (Prohibition) Act, and Canada, where legal protections coexist with ongoing digital hostility. Using a sequential mixed-methods design, the authors build a corpus of 1,500 public videos (2022–2024) and combine computational analysis (classification, diffusion mapping, FYP appearance simulation, and regression modeling of engagement metrics) with interviews and digital ethnography of creators and anti-LGBTQ influencers. Findings show a pronounced algorithmic bifurcation: anti-LGBTQ content in Nigeria circulates faster and reaches substantially higher average views than pro-LGBTQ content, while in Canada pro-LGBTQ content enjoys comparatively greater visibility—yet anti-LGBTQ material still benefits from controversy-driven “share” dynamics. The chapter details how “rage sharing,” watch-time completion, and early amplification windows function as algorithmic currency, and it documents extensive circumvention tactics (dog whistles, “educational” framing, visual–text decoupling, timing hacks, VPNs, backup accounts) that evade moderation.