1. 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
2. Micro-participation a method to engage “many, unconnected individuals” while minimizing time and opportunity costs to personal involvement Source: jjprojects
17. 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.
37. 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
38. 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%
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.