A citation analysis platform that employs machine learning to classify how academic papers cite one another — as supporting, contrasting, or mentioning — enabling researchers to assess the empirical reliability of sources beyond raw citation counts.
Scite's core innovation — classifying citation sentiment (supporting, contrasting, mentioning) — provides a methodologically significant layer that raw citation indices (Web of Science, Scopus) do not offer. The Reference Check tool can validate an entire manuscript's bibliography against retraction databases and citation context. Smart Citations are pre-indexed from millions of full-text articles via publisher agreements (Wiley, Cambridge University Press, Karger, Wolters Kluwer, Thieme) and preprint servers (arXiv, bioRxiv, medRxiv).
Assessing the empirical standing of key references during literature review — specifically determining whether a foundational paper has been predominantly supported or contested by subsequent scholarship.
Citation classification accuracy depends on the NLP model's ability to parse domain-specific language; nuanced or ambiguous citations may be misclassified. Coverage is strongest in STEM and biomedical fields; humanities coverage is thinner. The AI Assistant component carries standard generative AI caveats.
Scite.ai processes user data under GDPR-aligned frameworks for EU/UK residents, including cross-border transfer protections via EU standard contractual clauses. Account-related data is retained for the life of the account plus 10 years. The privacy policy does not explicitly state whether uploaded PDFs are used for model training. Opt-out mechanisms for marketing are available.
Scite's Smart Citations are extracted directly from published academic texts via NLP, not generatively produced. Citation classifications (supporting, contrasting, mentioning) are derived from the actual citing sentences in source documents. The Reference Check feature flags retracted or problematic references. The AI Assistant component, however, uses generative models and should be treated with standard LLM verification protocols.
Free tier with limited access. Individual plan starts at a Paid tier level. Organization and institutional plans are custom-priced with SSO, REST API access, and dedicated support.
✓ Institutional licensing available
Editorial independence: ScriptorLabs has no commercial relationship with Scite.ai. This review is independent and based solely on our academic evaluation framework.