How major platforms uniquely evaluate content
Instagram, Spotify, TikTok, and Spotify evaluate content differently.
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How major platforms uniquely evaluate content
Platform-specific algorithm nuances artists should know.
Why it matters
While all entertainment platforms operate on the fundamental repetitive test principle, each has unique algorithmic nuances that significantly affect content distribution.
Grasping these differences enables artists to create tailored strategies for each platform instead of a one-size-fits-all approach.
Platform-specific variations reflect each company's business models, behavioural patterns, and competitive positioning. Recognising these distinctions helps artists allocate their limited resources more effectively across platforms.
How it works
It is important to note that while the platforms confirm some algorithmic details through transparency reports and help centres, others reflect industry consensus based on researcher observations and creator experiences, or just simple educated guesswork. Platforms regularly update their systems, often without fully disclosing the changes.
Instagram
Evaluates average watch time, likes per reach, and sends per reach.
Rewards smaller creators with highly engaging, original content.
Significantly reduces visibility of content with non-native watermarks.
Places heavy emphasis on the history of interaction between creators and viewers.
Distributes content through three systems: Following, Explore, and Feed.
Values quality of engagement over quantity of followers.
Spotify
Weighs skips, saves, playlist adds, and complete listens over partial streams.
Uses contextual data (friends, similar users, time of day, device, listening history) for recommendations.
Values consistent release schedules and listener retention.
Employs large language models (LLMs) to create personalised "narratives" connecting recommended tracks
Implements "Text2Tracks" technology, enabling more nuanced natural language search for music discovery.
Focuses on listener intent and mood rather than just genre-based classification.
Considers consumption patterns and explicit feedback (likes, skips) for algorithmic placement.
TikTok
Uses wider initial audience testing than other platforms.
Prioritises completion rate and watch-time over traditional engagement metrics.
Relies less on follower count and more on content quality for distribution.
Excels at matching content with previously unknown but interested audiences.
Rewards use of platform-native creative tools and trending sounds.
YouTube
Emphasises watch time and session duration as primary metrics.
Uses sophisticated topic clustering to understand content relationships.
Treats Shorts differently from long-form content in algorithmic distribution.
Values consistent, regular posting within content niches.
Yes, but..
Each platform’s unique content evaluations illustrate their competitive positioning. Instagram's emphasis on smaller creators responds to TikTok's success in creator discovery. Meanwhile, YouTube refines Shorts to recapture attention, while Spotify balances major label priorities with independent artist discovery and shifts towards more personalised, context-aware music discovery.
Artists with limited resources face difficult choices about which platforms to prioritise. It might be best to focus on one or two platforms only.
Take action now
Artists could experiment with a cross-platform content strategy by identifying which two platforms best align with their audience and content style and creating platform-specific versions instead of directly reposting their content.
Your thoughts
Further reading
How algorithms work in 2025 (The Fanbase Builder)
Instagram Ranking Explained (Instagram)
How Instagram's algorithm ranks content (Future Social)
Instagram is updating its algorithm to surface more content from smaller, original creators (TechCrunch)
Can you beat the Spotify algorithm? (Harpers Bazaar)
Understanding recommendations on Spotify (Spotify Safety and Privacy Centre)
Contextualized Recommendations Through Personalized Narratives using LLMs (Spotify R&D)
Text2Tracks: Improving Prompt-based Music Recommendations with Generative Retrieval (Spotify R&D)
Socially-Motivated Music Recommendation (Spotify R&D)
Bridging Search and Recommendation with Generative Retrieval (Spotify R&D)
How Content Recommendation Works on TikTok (TikTok Newsroom)
YouTube's Recommendation System (YouTube)
Why video shares are crucial for creators on TikTok and Instagram (The Fanbase Builder)
Why artists should select their platforms with care (The Fanbase Builder)
Social media is a thing of the past (The Fanbase Builder)
Retrieving catalogue data using the Spotify Web API (The Fanbase Builder)