Machine Learning, Graphs and the Fake News Epidemic (Part 2)

This post discusses how to leverage graph technology for a solution that aims to gauge the overall controversiality of an article by analyzing its connections with other articles. The proposed framework uses Neo4j's native graph database architecture, which excels at leveraging connected data through index-free adjacency, pattern-matching searches, and graph traversals. By storing entities and topics mentioned by articles as nodes in the graph, users can intuitively search for similar articles using Cypher, a graph query language. The post also outlines the construction of a news graph, which focuses on important relationships between authors, sources, and articles to measure controversiality and identify communities that consistently agree with one another.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.