Google's (now public) PageRank algorithm isn't that complicated, and it brought an astounding improvement in our ability to find what we wanted. There might in

Google's (now public) PageRank algorithm isn't that complicated, and it brought an astounding improvement in our ability to find what we wanted. There might in fact be a simple algorithm for determining who to trust -- but a closed-source algorithm seems like it cannot by definition go very far to improve media transparency. If we had more kinds of metadata publicly available -- "[person] trusts [person]", "[person] found [article] helpful for understanding [topic]", "[person] testifies that [person] wrote a good PhD dissertation on [topic]", etc. -- we could publicly experiment with algorithms like, "restrict results to people whose writings have been contradicted fewer times than they have been endorsed", or "to people trusted by people I trust," or "to persons X for whom at least 50 other persons Y testify that X helped them understand [topic]," or whatever. Hypergraph databases (e.g. Grakn, HypergraphDb) and logic programming languages (e.g. Datalog) already let us express arbitrary metadata -- anything expressible, as far as I am aware. Getting ordinary users to state their thoughts (and their search queries over those thoughts) in an easily machine-readable manner might be tough. For that purpose I wrote a language[1,2], extremely close to natural language, that allows such machine-readable expression of higher-order relationship information. [1] Why use Hash: https://github.com/JeffreyBenjaminBrown/digraphs-with-text/blob/master/Hash/why-use-hash.md https://github.com/JeffreyBenjaminBrown/digraphs-with-text/b... [2] How to use Hash: https://github.com/JeffreyBenjaminBrown/digraphs-with-text/blob/master/Hash/the-hash-language.md https://github.com/JeffreyBenjaminBrown/digraphs-with-text/b...