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Civic CrowdAnalytics:Making sense of crowdsourced civic input
with big data tools !!
Tanja Aitamurto Kaiping Chen Ahmed Cherif
Jorge Saldivar Galli Luis Santana
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Vote
Crowdsourcing ideas and solutions
for policy
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Crowdsourced urban planning strategy
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Hillary Gitelman, Urban planner, City of Palo Alto 6!
Piles of unstructured data
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Civic data overload
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Bottleneck in participatory channel
Citizens’ input
Policy
Lack of data analytics tools 9!
x!What if our votes were not counted?
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Civic CrowdAnalytics Web application for analyzing civic data with Natural Language Processing and machine
learning
Sentiment analysis; Find related concepts
Categorization
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Dashboard
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Categorization
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Dig deeper
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Sentiment analysis
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Crowd’s impact on policy
Key findings
• The more (categorization) and the stronger (sentiment analysis) the crowd’s demands are, the more likely to make it to the policy
• CAC’ agenda reflects less the crowd’s suggestions than the policy
• High frequency terms (concept occurrences) reflect the level of expertise in policymaking
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Top key terms and occurrences(Subcategory: Big picture infrastructure)
Crowd’s input Cars (19), driving (16), road (12)
CAC input Development (16), traffic congestion(15), traffic safety (10)
Policy!
Traffic (22), improvement (14), safety (12)
From piles of unstructured data to structured results
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NLP for civic use !• 80% accuracy rate at best
• Disproportionally laborious training for small datasets
• Larger datasets for improved accuracy
• Using the best of NLP for civic purposes
• Training algorithms across cases à “Cross-training” crowdsourced policymaking efforts in several countries
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Open Government Partnership countries
Commitment for civic engagement and transparency
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Thank you! Questions, comments?
Dr. Tanja Aitamurto
tanjaa@stanford.edu
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Crowdsourced constitution in Chile
Over 30,000 submissions
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