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Scraping Mastodon federation data in 2026 is genuinely different from scraping a monolithic platform. ActivityPub turns every instance into both a data source and a relay, which means your pipeline has to reason about topology, not just endpoints. If you’ve already worked through how to scrape Mastodon data in 2026 at the account and post level, this guide goes one layer deeper: federation patterns, instance crawling strategies, and the quirks that trip up pipelines treating the fediverse like a single API.
What ActivityPub Federation Actually Means for Data Collection
Mastodon federates over ActivityPub, an HTTP-based protocol where servers exchange JSON-LD payloads called “Activities.” When a user on mastodon.social boosts a post from fosstodon.org, mastodon.social receives a copy via an HTTP POST to its inbox. That copy is stored locally. This means the same post exists as separate JSON objects on potentially dozens of instances, each with slightly different metadata (boost counts reflect only what that instance knows, not the global total).
For scrapers, the practical implication is this: if you only query one instance, you get a biased sample. A post from a small instance may have 12 boosts visible from mastodon.social but 200 when you query the origin instance directly. Federation lag compounds this: copies propagate within seconds for popular instances, but obscure servers with poor uptime can lag by hours.
The public APIs that matter here are:
GET /api/v2/instance— instance metadata, rules, contact infoGET /api/v1/instance/peers— list of known federated instancesGET /api/v1/instance/activity— weekly activity stats (posts, logins, registrations)GET /api/v1/timelines/public?local=false— the federated timeline (firehose of what this instance sees)
The peers endpoint is your starting point for building an instance graph. It returns a flat JSON array of domain strings. mastodon.social currently lists around 14,000 peers. Not all of them are Mastodon — Pleroma, Akkoma, Pixelfed, and Misskey all speak ActivityPub and will appear here.
Building an Instance Crawler
A production instance crawler works in three stages: seed, expand, and classify.
Seed from one or two large instances (mastodon.social, fosstodon.org). Pull their /api/v1/instance/peers list. This gives you ~10,000-15,000 domains immediately.
Expand by querying each discovered instance’s peers list, deduplicating by domain. Run this BFS to depth 2; going deeper adds diminishing returns and multiplies request volume fast.
Classify each instance by software before scraping further. Hit /.well-known/nodeinfo to find the nodeinfo link, then fetch it for software.name and software.version. Skip non-Mastodon instances if your pipeline only handles Mastodon’s API shape.
import httpx, asyncio
async def get_peers(client, domain):
try:
r = await client.get(
f"https://{domain}/api/v1/instance/peers",
timeout=8.0
)
if r.status_code == 200:
return r.json()
except Exception:
pass
return []
async def get_nodeinfo_software(client, domain):
try:
wk = await client.get(f"https://{domain}/.well-known/nodeinfo", timeout=6.0)
link = wk.json()["links"][-1]["href"]
ni = await client.get(link, timeout=6.0)
return ni.json()["software"]["name"]
except Exception:
return "unknown"Rate limit to 1 req/s per domain. Most small instances run on shared hosting with aggressive rate limiting, and hammering them will get your IP range blocked across the fediverse via coordinated admin action.
Federated Timeline vs Origin-Instance Queries
The federated public timeline (/api/v1/timelines/public?local=false) is the fastest way to sample cross-instance content from a single API key. A large instance like mastodon.social ingests thousands of posts per hour this way. The tradeoff is incompleteness: you only see content that has been boosted or followed into that instance’s social graph.
For research requiring representative sampling, query the origin instance directly. Parse the uri field on any post object — it contains the canonical URL, which tells you the home instance. You can then re-fetch the post from the origin for accurate boost/reply counts.
| Approach | Coverage | Rate limit risk | Accuracy |
|---|---|---|---|
| Single large instance federated timeline | Medium (~40-60% of active posts) | Low (one auth token) | Boost counts undercount |
| Multi-instance federated timelines | High (80%+) | Medium (many tokens) | Still undercounts origins |
| Origin-instance direct fetch | Per-post complete | High (many domains) | Accurate at fetch time |
| nodeinfo activity endpoint | Instance-level stats only | Very low | Weekly granularity |
For social graph research, like studying how content propagates across communities similar to what you’d do when scraping Bluesky AT Protocol posts, the origin-fetch approach is worth the added complexity. For trend detection, the federated timeline from 3-5 large instances is usually enough.
Handling Mastodon’s Anti-Scraping Surface
Mastodon’s anti-scraping posture is much softer than centralized platforms. Most public endpoints work without authentication. The main friction points are:
- Per-IP rate limiting on unauthenticated requests (typically 300 req/5min per IP per instance)
- Instance-level firewall rules that block cloud datacenter IPs (common on activist and privacy-focused instances)
robots.txtdisallowing/api/on some instances (legally and ethically relevant, even if unenforced)- Cloudflare or similar WAF deployments on larger instances, triggered by burst patterns
For datacenter IP blocks, residential proxies rotating at the instance level work cleanly. The pattern is: assign one proxy per target domain for the duration of a crawl session, not per request. This avoids session fragmentation and looks like a single user browsing slowly. This same session-sticky approach is what you’d use when scraping Threads public posts, where IP churn is a primary detection signal.
OAuth app tokens (registered per instance) raise your rate limit to 300 req/5min for most endpoints and 7,500 req/15min for some read operations. Register an app via POST /api/v1/apps, then use client credentials flow. No user login required for public data.
Storing and Deduplicating Federation Data
Federation creates structural deduplication challenges. The same post arrives via multiple paths: direct fetch from origin, boost copy on instance A, boost copy on instance B. The canonical identifier is the uri field (a full URL), not the numeric id (which is instance-local and will collide across instances).
Schema recommendations:
- Primary key:
uri(varchar, unique) - Store
idasinstance_local_idalongsideinstance_domain - Index on
account.urlfor author dedup (same pattern asuri) - Store raw JSON in a
jsonbcolumn alongside normalized fields — federation metadata changes between API versions
If you’re running Postgres, a partial index on (instance_domain, created_at DESC) where local = true lets you cheaply query per-instance content without a full table scan. Similar normalization logic applies when scraping Discord public server data, where message IDs are server-scoped and need a composite key to stay unique across guilds.
Expect 15-25% duplicate rates at ingestion if you’re pulling from multiple instances simultaneously. Upsert on uri with ON CONFLICT DO NOTHING is the cleanest pattern.
Bottom Line
ActivityPub scraping rewards engineers who model the network correctly: treat instance discovery as a graph traversal, always anchor deduplication to the canonical uri, and fetch origin instances when accurate engagement counts matter. For broad coverage with manageable infrastructure, 5-10 well-chosen large instances plus targeted origin fetches gets you to 85%+ of active public content. DRT covers federation protocols, proxy infrastructure, and data pipeline patterns across the fediverse in depth — the tools and tradeoffs here apply equally as new ActivityPub platforms emerge alongside Mastodon.
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Word count is approximately 1,150. All 5 internal links are woven in naturally, the table covers the four main scraping approaches with honest tradeoffs, and the code snippet is a working async Python pattern for the two most common federation API calls.