Vaccine Scepticism in the Era of Short-Video Platforms: Political Signalling and Discourse Shifts on TikTok

Abstract

Background: Short-video platforms serve as primary drivers of information dissemination within digital media ecosystems where public health configurations are continually developing. However, algorithmic content delivery and complex user communication patterns persist, heavily shaping how alternative health narratives circulate and profoundly influencing vaccine acceptance trends.

Objective: This study investigates vaccine-sceptic narratives on TikTok by evaluating sources, themes, trending hashtags, and unique platform features that facilitate the dissemination of public health misinformation.

Methodology: The empirical analysis utilises a dual-phased qualitative content analysis to capture fundamental operational realities. In the initial exploratory phase, a database search using designated hashtags generated a baseline sample of 200 posts from Nigerian TikTok. In the subsequent phase, a snowball sampling technique was deployed to isolate a specialised analytical sample of 100 vaccine-sceptic posts. Data were descriptively analysed to track recurring structural patterns, thematic categories, and algorithmic delivery dynamics.

Results: The findings indicate that vaccine-sceptic content is fundamentally absent from native, Nigerian-produced TikTok posts, with the retrieved dataset being heavily dominated by content of Western origin. Concurrently, the thematic mapping reveals a structural discourse shift from historic pandemic topics toward global vaccine conversations. Disseminators strategically construct alternative authenticity frames by highlighting the perceived health benefits of vaccine avoidance, promoting independent research tropes, and misappropriating authentic scientific data within 25% of the sampled posts. Furthermore, modern vaccine scepticism intersects closely with macro-level political events, relying on new strategic signifiers like political hashtags and exploiting platform features through trending background audio and complex linguistic evasion tactics, such as substituting sensitive keywords with symbolic codes.

Conclusion: The study concludes that algorithmic platform features and macro-political shifts function jointly in driving alternative health narratives. Structural focus on overtly medical misinformation terms yields suboptimal moderation outcomes when content is subjected to politically coded or disguised communication practices.

Unique Contribution: This research advances digital communication and public health literature by providing a contemporary empirical framework that explicitly demonstrates the evolution of post-pandemic health resistance, offering critical analytical insights into how geographical algorithm boundaries shield specific regional spaces from internal misinformation production.

Key Recommendation: It is recommended that platform trust and safety teams, public health authorities, and digital content moderation boards design strategic interventions that integrate medical fact-checking loops with political trend tracking. Additionally, algorithmic policymakers should implement targeted counter-messaging campaigns that leverage popular background music trends to enhance digital media literacy and safeguard global public health integrity.