Microparticipation in Transportation Planning
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Microparticipation in Transportation Planning

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Twitter and other social media tools are being used to engage the public. Learn about the innovative use of social media in transportation planning.

Twitter and other social media tools are being used to engage the public. Learn about the innovative use of social media in transportation planning.

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http://www.thinkinnovation.org 92
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http://paper.li 38
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  • Full Name Full Name Comment goes here.
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  • Slide 41 tells a deeper story than is suggested. The paper is well worth reading as it has important lessons for anyone who wants to set up a similar project. I would recommend this paper and this approach because it offers an important tool and insight into an emerging area of engagement. However, one has to be sensitive to and aware of the politics of the organisation as well as the wider political context. The reasons given in the paper, by the City Officials and decisions makers were the basis for rejecting rather than accepting the project. I was disappointed to read that the city officials were pointing to one tweet, out of thousands, that happened to make a mild reference to renewable energy as suggesting the project may have bias.

    It looks like the researchers and the project had a tough hand but played it as well as they could. Looking forward to similar project.
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  • Great presentation! Now *this* is government 2.0 in action!
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  • Really good job explaining what SNAPPatx did and what we learned. Indeed figuring out how to remedy issues on slide 41 is a big deal. Technology will help with some of the issues (timely analysis/reporting and robustness of analysis). We also need to help planners and elected officials innovate uses for social media where it can be particularly powerful.

    The pace of change is becoming astonishingly fast, e.g., a successful revolution in 18 day. We have to gain tools to keep up or forever be relegated to 'day late/dollar short' epitaphs.
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  • Yes, I'll be giving a version of this presentation at the APA National Conference in April.

    Yes, Slide 41 is the most important. My key lessons learned. Public officials need to understand what social media can and cannot deliver before the process starts. Beyond that we need to work on new ways to convey the data collected so that it meets the needs. In this case the City appreciated the sentiment analysis, but they wanted to know the story behind the sentiment analysis. Thoughts on how this might be achieved are welcomed.
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  • Cool presentation! Are you presenting at the National APA Conference in April? I'll be there with some planning students from California.
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  • Assesses emotional, cognitive, and structural components of text using a psychometrically validated internal dictionary. LIWC calculates the degree to which a text sample contains words belonging to empirically defined psychological and structural categories. It calculates the relative frequency with which words related to the psychological dimension occur.
  • Assesses emotional, cognitive, and structural components of text using a psychometrically validated internal dictionary. LIWC calculates the degree to which a text sample contains words belonging to empirically defined psychological and structural categories. It calculates the relative frequency with which words related to the psychological dimension occur.
  • Assesses emotional, cognitive, and structural components of text using a psychometrically validated internal dictionary. LIWC calculates the degree to which a text sample contains words belonging to empirically defined psychological and structural categories. It calculates the relative frequency with which words related to the psychological dimension occur.
  • Assesses emotional, cognitive, and structural components of text using a psychometrically validated internal dictionary. LIWC calculates the degree to which a text sample contains words belonging to empirically defined psychological and structural categories. It calculates the relative frequency with which words related to the psychological dimension occur.
  • Assesses emotional, cognitive, and structural components of text using a psychometrically validated internal dictionary. LIWC calculates the degree to which a text sample contains words belonging to empirically defined psychological and structural categories. It calculates the relative frequency with which words related to the psychological dimension occur.
  • Assesses emotional, cognitive, and structural components of text using a psychometrically validated internal dictionary. LIWC calculates the degree to which a text sample contains words belonging to empirically defined psychological and structural categories. It calculates the relative frequency with which words related to the psychological dimension occur.
  • Assesses emotional, cognitive, and structural components of text using a psychometrically validated internal dictionary. LIWC calculates the degree to which a text sample contains words belonging to empirically defined psychological and structural categories. It calculates the relative frequency with which words related to the psychological dimension occur.
  • Assesses emotional, cognitive, and structural components of text using a psychometrically validated internal dictionary. LIWC calculates the degree to which a text sample contains words belonging to empirically defined psychological and structural categories. It calculates the relative frequency with which words related to the psychological dimension occur.
  • Had 203 Facebook fans (compared to avg of 29 for planning projects Evans-Cowley, 2010) Had 366 Twitter followers (compared to 2% of users with more than 300 followers Cheng et al., 2009) Avg of 45 retweets per week (based on Kwak et al., 2009 typical reach would be 45,000 people per week) 83% of microbloggers contributed 82% of all relevant microblogs (Jansen and Koop, 2005 and Tumasjan et al., 2010 found dominance by heavy users)
  • Assesses emotional, cognitive, and structural components of text using a psychometrically validated internal dictionary. LIWC calculates the degree to which a text sample contains words belonging to empirically defined psychological and structural categories. It calculates the relative frequency with which words related to the psychological dimension occur.
  • Assesses emotional, cognitive, and structural components of text using a psychometrically validated internal dictionary. LIWC calculates the degree to which a text sample contains words belonging to empirically defined psychological and structural categories. It calculates the relative frequency with which words related to the psychological dimension occur.

Microparticipation in Transportation Planning Microparticipation in Transportation Planning Presentation Transcript

  • Source: Ethatgrumguy Micro-participation The role of microblogs in promoting engagement in planning Jennifer Evans-Cowley, PhD, AICP Associate Professor and Head City and Regional Planning The Ohio State University
  • Micro-participation a method to engage “many, unconnected individuals” while minimizing time and opportunity costs to personal involvement Source: jjprojects
  • Micro-participation Austin’s Strategic Mobility Plan Source: Bonita Sarita
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  • Key Social Media Vocabulary URL Presence of a website address Hashtag Represented by #; used to indicate a common grouping of tweets. For example, #APA2009 might represent the American Planning Association Conference in 2009. Mention Represented by @; the number of usernames specified in a tweet Follower A user who is following the tweets of an author Status A representation of a user’s current status, including mood, current news, or other information the user wishes to share Retweet Represented by RT; The sharing of a tweet with other users Push A message being pushed out by a social networking user. This is typically news or an announcement intended to be shared.
  • Source: mathawie
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  • Source: Bonita Sarita How to Analyze Micro-participation?
  • Analyzing Microblogs
    • 49,421 Microblogs Collected
    • 11,500 relevant microblogs: 8,308 from microbloggers; 1,019 from media sources; 2,173 from SNAPPatx (1,007 facilitating conversation and 1,166 pushing information)
    • Coded the 8,308 microblogs from microbloggers byType; Theme; Topic 1, Topic 2; Sentiment;
    • Microparticipation dialogue content analysis
    • Rate of participation
  • Type of Microblog
        • Sharing: “RT @foxaustin: Listen up UT students. City of Austin cracking down on E-bus riders that become unruly on the bus. http://bit.ly/alXE1x ”
        • Engaging : “$7 fa a 24hr bus pass..... how much is it in austin???? exactly....”.
        • Analyzing: “transit: Austin red line vs. Twin Cities Hiawatha line - I like both. Only similarity? single route”.
    Source: Bonita Sarita
  • Theme/Topics of Microblogs
    • Theme: Related to the ASMP (such as eco devo, regional integration)
    • Topic 1, Topic 2: Topics mentioned in microblog
    • “ RT @fitcityleblanc: Where do most of Austin's bicycle-motorist collisions occur? Check this map! http://bit.ly/52X2g ”.
    Source: Paddy Murphy
  • Sentiment Analysis
    • Simple Sentiment Analysis: Review for positive or negative sentiment
    • Detailed Sentiment Analysis: Using Linguistic Inquiry and Word Count (LIWC) text analysis software
    Source: gary j wood
  • Sentiment Analysis Source: Talton Figgins
  • Sentiment Analysis
  • Sentiment Analysis Source: jrmyst
  • Content Analysis
    • Analysis of @, RT, URL usage: 57% @ 60% URL
    • Analysis of Stimulation Attempts: 282 pushes; 54% received a response; Avg 2.3 responses
    • Content Analysis of completed dialogue: 374 attempts; 42% received a response
    Source: ret0dd
    • SNAPP: Okay #Austin, #Nashville has you beat again. An amazing downtown transit station. http://bit.ly/9YDjGU #snappatx
    • Microblogger: @SNAPPatx really impressive station. Like that waiting room. Only downtown transit station I've been to: Eugene, OR. http://flic.kr/p/7gB3cj
    • SNAPP: @btx91 ATX is looking at a combo of BRT and streetcar for the 2012 urban rail project but no mention of a great station like TN. #snappatx
    • Microblogger: @SNAPPatx yeah, Congress Ave. acts as a transit mall of sorts though. Is this BRT the MetroRapid or something diff? #snappatx
    • Microblogger: @SNAPPatx and latest on urban rail? Is the streetcar going to be at-grade, mixed-traffic like Portland, or with some kind of separation?
    • Microblogger: @SNAPPatx Tacoma's LRT is an example of what I mean by slight grade separation #snappatx
    • SNAPP: @btx91 Check out all the deets on the ATX urban rail project here www.austinstrategicmobility.com/resources/urban-rail-project #snappatx
    • SNAPP: @btx91 We admit, it looks and sounds good. Have you been there to experience it in person? If so, how was it? #snappatx
    Content Analysis
  • Content Analysis
    • Microblogger: Seriously the bus system in Austin needs major work.
    • SNAPP: @Katshead42 What about Austin's bus system isn't working for you now? How could it be made better? #snappatx
    • Microblogger: @SNAPPatx my bus was 15 minutes early so i had to wait at the stop for an hour for the next one to come. They drive by stops all the time
    • SNAPP: @Katshead42 It sounds like more frequent buses might help ease the pain if u miss a bus that's running early, yes? Anything else? #snappatx
    • Microblogger: @SNAPPatx that would help. If the buses ran later into the night or early morning that would help too.
    • SNAPP: @Katshead42 Excellent. Voicing your opinion about problems/solutions is the best way to make change happen. We hear you! #snappatx
    Source: Andy Schultz
  • Source: quesoweb @elizmccracken When I was there I saw a guy with a ZZ Top beard pulling a standup bass on a trailer behind his bike. Austin=weird biking. @leahcstewart @elizmccracken Do the weird Austin bikers make you want to ride a bike yourself or are you just happy to observe? #snappatx @SNAPPatx @elizmccracken It depends on whether I have to ride the bike in a g-string toting a standup bass. @leahcstewart @elizmccracken Nope, you can ride the bike in any manner you choose - no g-string or instrument hauling required. #snappatx Content Analysis
  • Equality of Participation Users Messages User Group Total Share % Total Share % One-time (1) 3,690 83.1% 3,690 56.1% Light (2-5) 684 15.4% 1,760 25.9% Medium (6-20) 49 1.1% 465 7.1% Heavy (21-79) 14 0.3% 537 8.2% Very heavy (80+) 2 0.0% 178 2.7% Total 4,439 100% 6,576 100%
  • Source: rockygirl05 Did It Work?
  • Yes
    • Exceeded all past project measures
      • Had 203 Facebook fans
      • Had 366 Twitter followers
      • Avg of 45 retweets per week
      • 83% of microbloggers contributed 82% of all relevant microblogs
    Source: cackhanded
  • No
    • Failure to influence decision-making because of:
      • Time/Time Lag Analytic Tools
      • Bureaucracy Social Networking Applications
      • Trust Anonymity
    Source: SpdRacerRVA
  • Looking Forward
    • Rapidly evolving technology
    • Rise of new analytic tools
    • Widespread adoption of technology
    Source: lucbychet
  • Source: lucbychet Ready for Micro-participation?
  • Source: Slava Baranskyi Twitter –EvansCowley Email – [email_address] LinkedIn – Jennifer Evans-Cowley