Social network analysis with graph databases - Neo4j
Graph databases are optimized for working with complex and connected data. Social media data is a great example of a complex dataset where the connections in the data are often as important as the discrete data points, making it a great use case for a graph database.
In this hands-on workshop we will cover how to model, import and query Twitter data using the Neo4j graph database. We will focus on learning the property graph data model and how to use Cypher, the query language for graphs, to write queries that can help find stories in the data. We'll use a dataset of tweets from Twitter accounts tied to Russia that were released as part of the House investigation into Russia's interference in the 2016 US election.
This session is good for: Those with some basic familiarity with databases and data analysis. We will start at an introductory level for those new to graph databases.
Meredith Broussard is an assistant professor at the Arthur L. Carter Journalism Institute of New York University and the author of Artificial Unintelligence: How Computers Misunderstand the World. Her research focuses on artificial intelligence in investigative reporting, with a particular interest in using data analysis for social good. Follow her on Twitter @merbroussard or contact her via meredithbroussard.com.
William Lyon is a software developer at Neo4j, the open source graph database. He works on building integrations for Neo4j with other technologies, helping users build graph applications, and also leads the Neo4j Data Journalism Accelerator Program. He holds a masters degree in Computer Science from the University of Montana. You can find him online at lyonwj.com or @lyonwj
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