pith:2U6U4M6P
Spectral Analysis of Fake News Propagation
Spectral bounds on graph propagation patterns distinguish fake news cascades from real ones and enable classification plus structural interpretation.
arxiv:2605.13861 v1 · 2026-04-18 · cs.SI · cs.AI
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Record completeness
Claims
we introduce several new bounds and integrate them with existing ones into a unified spectral representation of information propagation. We then use these spectral bounds for downstream classification and design a discrete structural optimization framework to interpret learned propagation patterns.
That the derived spectral bounds and first-order perturbation approximation faithfully capture the distinguishing structural properties of real versus fake news cascades without substantial information loss or approximation error that would invalidate downstream classification and interpretation.
New spectral bounds create a unified representation of news cascade structures that supports competitive fake news detection and interpretable optimization of propagation patterns.
References
Receipt and verification
| First computed | 2026-05-17T23:39:19.484723Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/2U6U4M6PGQ2IIPUBCHQ7C434PF \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: d53d4e33cf3434843e8111e1f1737c794e319ec90cfb18dbe7b8217a8a4acb58
Canonical record JSON
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