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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