Meta Social Connectedness Index
Meta Social Connectedness Index
Meta's Social Connectedness Index measures the relative probability of Facebook friendship between pairs of countries or subnational areas from an anonymized platform snapshot. Keyless CSV and ZIP resources contain origin and friend geography codes plus scaled_sci values at country, GADM, geoBoundaries, NUTS, U.S. county and ZCTA levels.
Coverage
The checked release covers 178 countries and represents a snapshot from 2025-12-26 through 2026-01-25; the country file has 31,684 pairs. Subnational files range from about 14 MB to 465 MB, with larger level-2 data split into shards. Meta does not provide older versions and says the index should not be interpreted as a time series.
Verdict
A relationship-intensity signal for modeling cross-border affinity, regional spillovers, migration links or exposure propagation. scaled_sci is a relative friendship measure, not a count of users, trips, messages or transactions. Coverage follows Facebook activity and the available geographic crosswalks, so it can differ from population-representative networks. Method changes are not backfilled, and the provider explicitly says not to interpret releases as a time series.
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Use in your agent
Load Meta country connectedness into pandas and Parquet. Checked against the provider's documentation on Nov 1, 2026.
Access
- No account or key for the checked country CSV download.
- The HDX metadata API lists ten resources and their current download URLs.
Request
curl -O 'https://data.humdata.org/dataset/e9988552-74e4-4ff4-943f-c782ac8bca87/resource/652cf9c9-541f-47de-8d53-ff818062bd0c/download/country.csv'Format
CSV country pairs with user_country, friend_country, user_region, friend_region and scaled_sci.
Load into pandas and Parquet
Needs Python 3 with requests, pandas and pyarrow.
"""Meta Social Connectedness Index country pairs -> pandas -> Parquet. No key."""
import io
import requests
import pandas as pd
URL = "https://data.humdata.org/dataset/e9988552-74e4-4ff4-943f-c782ac8bca87/resource/652cf9c9-541f-47de-8d53-ff818062bd0c/download/country.csv"
r = requests.get(URL, timeout=60)
r.raise_for_status()
df = pd.read_csv(io.BytesIO(r.content))
df.to_parquet("meta_sci_country.parquet", index=False)
codes = set(df["user_country"]) | set(df["friend_country"])
print(len(df), "country pairs", len(codes), "country codes", "columns", ", ".join(df.columns))Run by us on 2026-11-01 (python 3.13.5 | pandas 3.1.0 | pyarrow 26.0.0 | requests 2.34.2). Result: 31684 country pairs 178 country codes columns user_country, friend_country, user_region, friend_region, scaled_sci
Limits
- Rate limit: Not stated.
- The country file is about 0.56 MB; some subnational files are hundreds of megabytes.
- Older versions are not provided.
Quirks
- scaled_sci is a relative index, not a raw friendship or user count.
- Do not combine releases as a time series because construction methods can change without historical backfills.
- Country and region pairs are directional columns even when an analysis later treats the relationship as symmetric.
Terms and attribution
- The checked HDX metadata marks the data CC0 with no restrictions.
- The dataset page supplies academic citations for the index methodology.
Sources
- https://data.humdata.org/api/action/package_show?id=social-connectedness-index: “The Social Connectedness Index (SCI) quantifies the relative probability that two individuals from different geographies are Facebook friends”
- https://data.humdata.org/api/action/package_show?id=social-connectedness-index: “including new subnational units across 178 countries”
- https://data.humdata.org/api/action/package_show?id=social-connectedness-index: “Public Domain / No restrictions (CC0)”
- https://data.humdata.org/api/action/package_show?id=social-connectedness-index: “We do not provide older versions of the Social Connectedness Index”
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