About WhatSphere
What is WhatSphere?
There are hundreds of services that will tell you what a show is about. IMDB, Rotten Tomatoes, Metacritic, YouTube reviewers, recommendation algorithms — they all answer the same question: “What is this?”
Nobody answers the question that actually matters: “Who was this made for?”
That's WhatSphere. We are the only platform that tells you who a piece of entertainment was designed to reach — not what genre it is, not what critics think, not what the trailer promises, but who the actual target audience is based on the observable decisions baked into the production itself.
Why Does This Matter?
It is easy to turn a good idea into a bad product, and a bad idea into a good product. Marketing teams are excellent at polishing turds. A trailer can make anything look compelling. Review aggregators tell you what other people thought, not whether the content was made with you in mind.
WhatSphere cuts through the marketing, the trailers, and the manufactured consensus to show you the production decisions that reveal who the content was actually built for.
How Does Scoring Work?
Every title is scored on two primary axes: a Gender Axis (0 = strongly male-targeted, 100 = strongly female-targeted) and an Age Axis (0 = youth-oriented, 100 = mature-audience). These combine into four quadrant scores: Young Men, Young Women, Older Men, and Older Women.
Additional dimensions include racial audience representation (sigmoid deviation from production-origin baselines), LGBTQ+ audience scoring, adaptation faithfulness, and advocacy classification (entertainment, mixed, message-leaning, or advocacy-driven).
Data Sources
We use data from TMDB, IGDB, Jikan/MyAnimeList, AniList, and YouTube. All scoring is original analysis — we do not resell or redistribute source data. Every score is based on observable production decisions: casting, writing, adaptation choices, and creator statements of intent.
Symmetrical Standards
The same rules apply in all directions. A user who thinks Hollywood has lost its mind and a user who thinks representation matters should both be able to trust the data. The scores are the same regardless of your politics. What differs is how you use them: one user filters for entertainment-focused content with high faithfulness; another browses content centering their demographic. Both are valid uses of the same objective data.
Contact
Think a score is wrong? Have data to contribute? Want to report an issue?