Synthetic Data for Social Media
Social graphs are power-law: a few accounts have millions of followers, most have few. Misata generates that distribution directly, writes captions and comments from a seeded grammar instead of lorem ipsum, and keeps posts and engagement tied to real users.
The tables Misata generates
usersPower-law follower counts, bios, post-count roll-upspostsCaptions from a seeded grammar, timestamps, engagementcommentsSentiment-consistent text referencing real postsfollowsFollower graph referencing real usersWhat holds true, every time
- Follower counts follow a power law, not a flat distribution
- Captions and comments read like text, never lorem ipsum
- Every post and comment references a real user
- Same seed reproduces the identical network
Frequently asked
Do I need real social media data to generate this?
No. Misata builds the dataset from a specification, not a sample. There is no real social media data to source, anonymize, or leak. You describe the tables you need and the engine constructs them with referential integrity and realistic distributions.
Is the generated social media data privacy safe?
Yes, by construction. Nothing is learned from real records, so there is no membership to infer and nothing to leak. It runs entirely on your machine with no API key for the core engine.
Can I control the outcomes, like rates and totals?
Yes. Declare a target such as a monthly volume curve or an event rate and Misata produces rows that hit it exactly, while foreign keys stay intact and roll-up columns reconcile after a JOIN.
Choosing a tool? How Misata compares