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Generate Social Media Synthetic Data in Python

Social media data has a signature structure that random generators get completely wrong: follower counts follow a Pareto distribution (a tiny fraction of accounts captures most of the reach), engagement rates are beta-distributed between 1–5%, and captions include hashtags and contextual text, not lorem ipsum. Misata generates a five-table social platform dataset that looks like it came from a real app: users with realistic follower/following ratios, posts with media types, a follows graph, reactions, and threaded comments.

Python
import misata

tables = misata.generate("A social media app with 10k creators, posts, and viral content", rows=10_000, seed=42)
print(list(tables.keys()))   # ['users', 'posts', 'follows', 'reactions', 'comments']
print(tables["users"][["follower_count", "is_verified"]].describe())

What Misata generates#

Five tables: users → posts → reactions/comments, plus a follows graph linking users. Every post references a real user; every reaction and comment references a real post and user.

Tables and columns#

TableKey columns
usersuser_id, username, display_name, bio, follower_count, following_count, is_verified, joined_at
postspost_id, user_id, caption, media_type, like_count, comment_count, share_count, posted_at
followsfollow_id, follower_id, followee_id, followed_at
reactionsreaction_id, post_id, user_id, type, reacted_at
commentscomment_id, post_id, user_id, text, parent_comment_id, posted_at

Realistic distributions#

  • Follower counts follow a Pareto power-law, most accounts have hundreds of followers, a small elite has millions
  • Engagement rate (~likes/followers) is beta-distributed between 1–5%, matching real influencer benchmarks
  • is_verified is rare (~2% of accounts), consistent with real platform verification sparsity
  • Media types are distributed across image, video, carousel, and text, not uniformly random
  • Comments include parent_comment_id for threaded replies, not all comments are top-level

Quick start#

Python
import misata
import numpy as np

tables = misata.generate("A social media app with 5k users and influencer content", rows=5000, seed=42)

# Pareto distribution check — top 5% of accounts
followers = tables["users"]["follower_count"].sort_values(ascending=False)
top5 = followers.head(len(followers) // 20).sum() / followers.sum()
print(f"Top 5% accounts hold {top5:.0%} of all followers")

# Verified account stats
verified = tables["users"][tables["users"]["is_verified"]]
print(f"Verification rate: {len(verified)/len(tables['users']):.1%}")
print(verified["follower_count"].describe())

Common use cases#

  • Content recommendation model training: generate user-post interaction data (reactions, watch_history) for training collaborative filtering and interest models
  • Social graph analytics: use the follows table to prototype graph algorithms, community detection, and influencer identification
  • Moderation and trust and safety tooling: generate comments and posts at scale to test content classification and flagging pipelines
  • Creator analytics dashboards: build engagement rate, reach, and growth analytics before real creator data is available
  • Feed ranking algorithm testing: validate chronological and ranked feed logic against realistic like/comment/share distributions
  • A/B test framework validation: generate user cohorts with varied follower counts and engagement rates for experiment design testing

Advanced: viral content curves#

Python
tables = misata.generate(
    "Social platform where a viral post caused a follower spike in March, "
    "engagement doubled for 2 weeks then normalized",
    rows=5000,
    seed=42,
)

Advanced: locale-aware generation#

Python
# Indian social platform — Hindi/English usernames, Indian cultural context
tables = misata.generate("Indian social media platform with 5k users", rows=5000)

# Latin American creator economy — Spanish captions, regional trends
tables = misata.generate("Latin American creator platform with 3k influencers", rows=3000)

Advanced: quality-guaranteed generation#

Python
tables = misata.generate(
    "Social platform with 5k users and creator economy",
    min_quality_score=85,
    smart_correlations=True,  # auto-adds follower_count↔like_count correlation
    rows=5000,
    seed=42,
)

Related guides#

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