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Data Science of Messy Metrics

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Gilad Lotan
Gilad Lotan

My #TEDxPoynter talk slides

TechnologyBusiness
1 of 35
Hello Gilad Lotan | @gilgul | gilad@betaworks.com
still sexy?
Illustration: Matt Taylor
status affordances
let’s look at some data
Establishing the Norm
Data Reflects Us
Social Movement TV Show Awards Ceremony
Newtown Shooting News Breaks Obama visits
Volatility of Trends over Time
network analysis
User A User B Graph Representation of Social Data User A is connected to B User A User B User A follows User B Word A Word B Word A appeared with Word B User A User B User A retweeted User B connection follows appears with retweeted
Graph Measures • Centrality – Betweenness – Closeness – Eigenvector – Degree • Clustering Coefficient (clique) • Modularity outdegree=2 indegree=3 degree=5
Twitter Users with “python” in their Bios • 2 days of Twitter data • 4246 users • 62k tweets • Colors represent modularity class
Pythonistas on Twitter English / European Japanese Python (the snake) Chinese Spanish Speakers Musicians, Artists
#Debates / Ohio
#Debates / Ohio Politicos OSU Students Ohio based Media
VP Debate
supportaccusations
Fragmented audiences + multiple platforms = very messy data
metrics in context
graph-based analytics
Gilad Lotan | @gilgul gilad@betaworks.com

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