Graph analysis of 200,000 tweets from Russian Twitter trolls

Description

As part of the US House Intelligence Committee investigation into how Russia may have influenced the 2016 US election, Twitter released the screen names of nearly 3,000 Twitter accounts tied to Russia’s Internet Research Agency. These accounts were immediately suspended, removing the data from Twitter.com and Twitter’s developer API. Ryan Boyd explains how he and his team reconstructed a subset of the Twitter network of Russian troll accounts and applied graph analytics to the data using the Neo4j graph database to uncover how these accounts were spreading fake news.

Ryan covers how they collected and munged the data distributed by NBC, taking advantage of the flexibility of the property graph and demonstrates how NLP and graph algorithms like PageRank and community detection can be applied in the context of social media to make sense of the data. Ryan shows how Cypher, the query language for graphs, is used to work with graph data and how visualization is used in combination with these algorithms to interpret results of the analysis and to help share the story of the data.