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Geocoding Paradise Papers Addresses In Neo4j To Build Interactive Geographical Data Visualizations

This post explores how to build spatial data visualizations using address data from the Paradise Papers leak of offshore corporations and the people connected to them. First, we geocode all addresses in the leaked data, then build a heatmap and interactive map for exploring the data of offshore legal entities.

Applying NLP and Entity Extraction To The Russian Twitter Troll Tweets In Neo4j (and more Python!)

Natural language processing (NLP) techniques like entity extraction can be used to help make sense of a large text corpus. In this post we apply named entity resolution to the scraped Russian Twitter Troll tweets to try to get a better understanding of how these trolls were spreading fake news.

Scraping Russian Twitter Trolls With Python, Neo4j, and GraphQL

In this post we explore how to scrape tweets from Internet Archive for Russian Twitter Troll accounts, import into Neo4j for analysis, and how to build a simple GraphQL API exposing the data through GraphQL.

Analyzing A Local Startup Ecosystem With Mattermark, GraphQL, Apollo Client, and Neo4j

Many web services are converting their publicly facing APIs from REST to GraphQL. Companies like GitHub and Shopify have been leading this transition to GraphQL. In this post we take a look at how we can query the new Mattermark GraphQL API using Apollo Client, storing the results in Neo4j to then see what we can learn about a local startup ecosystem.

Combining The BuzzFeed Trumpworld Graph with Government Contracting Data in Neo4j

One of the powers of working with graph databases is the ability to combine disparate datasets and query across them. Today we'll look at how we can combine the BuzzFeed Trumpworld graph with data about federal government contracts from USASpending.gov, allowing us to examine any government contracts that were awarded to organizations that appear in Trumpworld.