university of california, santa barbara david …veronika/characterizing online...veronika...

Veronika Strnadová – University of California, Santa Barbara

David Jurgens, Tsai-Ching Lu – HRL Laboratories, LLC

Supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC) contract number D12PC00285.

Goal – Analyze Microtext Characterize online discussions using a network

model of Twitter

Goal – Analyze Microtext Characterize online discussions using a network

model of Twitter

Identify different discussion types

Background

Correlating financial time series with micro-blogging activity. Ruiz E., Hristidis V., Castillo C., Gionis A., Jaimes A.

Keyword Categories

Musicians Sports Teams Companies Countries

Lady Gaga Dodgers Microsoft Great Britain

Jay-Z Yankees IBM United States

Red Hot Chili Peppers Cardinals Intel Uganda

Dave Matthews Band Diamondbacks Apple Greece

Kanye West Padres Sony Mexico

Dataset Twitter Decahose: a live feed of 10% of real tweets,

sampled at random

15,000 tweets per keyword graph per day

Network Construction: Graph Schema

Network Construction: Graph Schema

Network Construction: Example

Los Angeles, California, USA

Alice

Bob Clarissa

tweet1

#mlb

tweet2

tweet3 tweet4

#dodgers

tweet5

tweet6 #cardinals

St. Louis, Missouri, USA

Temporal Changes in Network Properties

Whole-Graph Metrics Graph Node Metrics

• Node Type Composition • Number of Connected Components • Diameter • Normalized Diameter

• Degree Distribution • Pagerank Distribution • Closeness Distribution

Results: Normalized Diameter

Results: Normalized Diameter “Yankees” Keyword Graph

Results: Normalized Diameter “Yankees” Keyword Graph

July 2012

Sunday Monday Tuesday Wednesday Thursday Friday

Results: Pagerank

Results: Pagerank “IBM” Keyword Graph

Results: Pagerank “Lady Gaga” Keyword Graph

Closeness

Closeness

Conclusion and Future Directions Modeling online discussions as relationships between people, locations, and topics has potential to reveal interesting properties of online discourse

Conclusion and Future Directions Modeling online discussions as relationships between people, locations, and topics has potential to reveal interesting properties of online discourse

Future work:

Move beyond basic graph metrics

Conclusion and Future Directions Modeling online discussions as relationships between people, locations, and topics has potential to reveal interesting properties of online discourse

Future work:

Move beyond basic graph metrics

Identify key elements in a discussion

Conclusion and Future Directions Modeling online discussions as relationships between people, locations, and topics has potential to reveal interesting properties of online discourse.

Future work:

Move beyond basic graph metrics

Identify key elements in a discussion

Track geographic shifts of public interest in a topic

Thank You!

Backup – Graph Construction