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Microblogging for Crisis Communication: Examination
of Twitter Use in Response to a Violent Crisis
Drexel UniversityPhiladelphia, Pennsylvania
Thomas Heverin & Lisl Zach
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Overview
• Purpose
• Description of Event
• Data collection & Methods
• Results
• Conclusion
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Purpose
• What types of information are transmitted over Twitter during violent crises?
• Who is creating and sending the information?
• How can official response agencies use the information transmitted on Twitter during violent crises?
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Sgt. Mark Renninger, Ronald Owens,Tina Griswold, and Greg Richards
November 29, 2009
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“Four police officers shot dead in coffee-shop ambush near Tacoma”
Tacoma News Tribune, McClatchy Newspapers, November 29, 2009
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Data Collection & Methodology
• Observations of Twitter usage
• Collected 6013 tweets (#washooting) Nov 29-Dec 1 via Twitter Search API
• Qualitatively coded data– types of authors– contents of messages– trends of contents of messages
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Results
• Author types and characteristics
• Message types
• Trends of message types
• Observed behaviors
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Author Types
• 1668 unique authors of 6013 tweets
• Citizens: – 91.5% of authors, contributed to 82.3% of the
tweets
• Local/national media:– 4.5% of authors, 8.9% of the tweets
• Local government:– < 1% of authors, < 1% of tweets
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Author Types
• 1668 unique authors
• Citizens: – 91.5% of authors, contributed to 82.3% of the
tweets
• Local/national media:– 4.5% of authors, 8.9% of the tweets
• Local government:– < 1% of authors, < 1% of tweets
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Author Types
• 1668 unique authors
• Citizens: – 91.5% of authors, contributed to 82.3% of the
tweets
• Local/national media:– 4.5% of authors, 8.9% of the tweets
• Local government:– < 1% of authors, < 1% of tweets
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Author Types
• 1668 unique authors
• Citizens: – 91.5% of authors, contributed to 82.3% of the
tweets
• Local/national media:– 4.5% of authors, 8.9% of the tweets
• Local government:– < 1% of authors, < 1% of tweets
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Citizen Geographic Distribution
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Message Types
• Information-related (79.0%)
• Opinion-related (17.8%)
• Technology-related (3.8%)
• Emotion-related (3.7%)
• Action-related (0.9%)
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Information Broadcasting
• Photographs of suspect• Background information about suspect• Previous legal & criminal history of suspect• License plate number of suspect’s get-away car• Twitter & Facebook profiles of suspect• Locations of alleged sightings • Locations of police activity & response
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Message Types
• Information-related (79.0%)
• Opinion-related (17.8%)
• Technology-related (3.8%)
• Emotion-related (3.7%)
• Action-related (0.9%)
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Message Types
• Information-related (79.0%)
• Opinion-related (17.8%)
• Technology-related (3.8%)
• Emotion-related (3.7%)
• Action-related (0.9%)
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Message Types
• Information-related (79.0%)
• Opinion-related (17.8%)
• Technology-related (3.8%)
• Emotion-related (3.7%)
• Action-related (0.9%)
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Message Types
• Information-related (79.0%)
• Opinion-related (17.8%)
• Technology-related (3.8%)
• Emotion-related (3.7%)
• Action-related (0.9%)
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Message Types
• Information-related (79.0%)
• Opinion-related (17.8%)
• Technology-related (3.8%)
• Emotion-related (3.7%)
• Action-related (0.9%)
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Trends of Tweet content per 12 hour time period
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Trends of Tweet content per 12 hour time period
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Trends of Tweet content per 12 hour time period
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Trends of Tweet content per 12 hour time period
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Trends of Tweet content per 12 hour time period
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Trends of Tweet content per 12 hour time period
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Behaviors Observed
• Collaboration• Self-policing information• Citing information sources & creating own• Questioning information sources• Technology instruction
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Conclusion
• In this case….– citizens contributed the most to the stream of
messages – #washooting primarily used for sharing crisis-
related information
• Future work– retweets, information extraction &
visualization, law enforcement views on using & monitoring Twitter