Surroundsourced

Surroundsourced is a method used in information science and journalism to establish the provenance and credibility of a claim by analyzing the surrounding contextual material rather than relying solely on an explicit primary source. The approach treats adjacent sentences, metadata, publication history, and related documents as part of the evidentiary ecosystem for a statement.

Etymology: The term combines surround, indicating contextual surroundings, with sourced, indicating attribution. It signals a shift from source-centric to context-inclusive evaluation.

Concept and methods: Surroundsourcing involves constructing a contextual provenance network that links claims to nearby references, notes, authorial drafts, and corroborating passages. In AI and natural language processing, models may use surrounding content to disambiguate claims and assess source trustworthiness.

Applications: Applications include editorial fact-checking workflows, automated citation extraction, digital humanities analyses, and knowledge-graph construction for semantic search.

Advantages and criticisms: Proponents argue surroundsourcing improves traceability, redundancy, and resilience to single-source failures. Critics warn of circular reasoning when the surrounding context itself depends on the claim, the possibility of propagating errors, and privacy concerns related to contextual data and attribution.

See also: provenance, citation, source attribution, contextual inference, knowledge graphs.