Mapping Data Science Influencers on Twitter

Twitter is full of posts tagged #BigData and #DataScience. Which are the ones that people pay attention to most? In a project for Synergic Partners, this team used network science and text-mining techniques to identify Twitter influencers in data science. They built a projected network by combining “retweet” and “mention” layers into a single layer and discovered communities using the K-Clique, Modularity, Random Walk and Mixed Membership Blockmodel community detection algorithms. They identified community influencers using centrality metrics and characterized users and communities using LDA. With a limited dataset of less than 200,000 tweets, they found that the modularity and random walk techniques produced the most coherent communities based on user demographics and influencers. An interactive visualization showed each community’s network and user demographics.

Students: Casey Huang, Claire Liu, Jordan Rosenblum and Steven Royce.

 
The team's use of the random walk and modularity algorithms independently picked out the above community of 5,115 Twitter users, largely concentrated in France. These techniques, along with others the team analyzed, can help identify influencers within communities and those who bridge communities and are thus effective targets for a marketing campaign.
 

 

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