Microparticipation with Social Media for Community Engagement in ...

Microparticipation with Social Media for Community Engagement in Transportation Planning Jennifer S. Evans-Cowley and Greg Griffin microparticipation, while minimizing the time and opportunity costs in personal involvement. A grant from the Federal Transit Adminis tration allowed Austin, Capital Metro, and the Capital Area Metro politan Planning Organization to partner with the Texas Citizens Fund to create the Social Networking and Planning Project (SNAPP). SNAPP was charged with piloting, tracking, and evaluating the use of an integrated array of tools to build relationships through online social networking to increase the quantity and quality of participation as part of the ASMP process between April and October of 2010. There were public meetings and other forms of public engagement but these were part of their standard engagement process. This paper focuses on SNAPP’s online engagement. This study asks whether the results of microparticipation could be measured and analyzed to create understanding of the public’s view on transportation topics. Was microparticipation equitable? Did the results of the microparticipation effort result in meaningful engagement used to support decision making among Austin’s policy makers? This paper begins with a discussion of microparticipation, followed by an explanation of SNAPP, presentation of methods, sharing of results, and a discussion of the implications that this case study has on the future of microparticipation.

Transportation planning processes may be enhanced and plans improved by engaging the community through social media technologies. “Micro participation” means the engagement of the public with social media methods for the purpose of maximizing the information going into a planning process while minimizing the plan’s development time and the cost to the public. Twitter, Facebook, and other microparticipation media have been used for planning but have not been extensively evaluated for that purpose. This study examined more than 49,000 posts on Twitter and other social networking sites tracked by the Social Networking and Planning Project to determine public engagement in the Austin Stra tegic Mobility Plan. With the use of a mixed methods approach, relevant posts were examined to determine sentiment, extent of engagement, and impact on the decision-making process. The study found methods that could be used to analyze microparticipation. The report concluded that microparticipation could be effective in generating participation but faced substantial technical, analytical, and communication barriers to influencing decision making.

In its recent report A Planet of Civic Laboratories: The Future of Cities, Information, and Inclusion, the Institute for the Future asks a critical question: “How can social networks and social media be leveraged to engage broader participation in building smart infra structures that meet the needs of diverse users?” (1). While the question was raised in the context of information technology infra structure, the question could pertain to other types of infrastructure. The City of Austin, Texas, asked this question as it undertook its Austin Strategic Mobility Plan (ASMP) process in advance of the city’s November 2010 transportation bond referendum. As do most cities, Austin faced challenges in engaging with the public to achieve those three purposes, including getting the rep resentative public to attend public meetings, getting the word out about ongoing planning projects, and encouraging meaningful par ticipation. Over time, Austin tried a wide variety of engagement techniques, including online social networking (2–5), eventually taking a more in-depth look at social networking as part of its larger engagement strategy. The city decided to experiment with online

Literature Effective public participation in planning projects shares the following three common purposes (6): 1. Involving citizens in planning and design decision-making processes, 2. Providing citizens a voice in planning for improvement of plans and decision making, and 3. Promoting a sense of community by bringing people who share common goals together. Transportation is one area where the struggle for public participation still exists (7, 8). Among other goals, participation in the transportation planning process should “provide opportunities for early and continu ing public involvement,” and “provide mechanisms to solicit public comments and ideas” (9). One of the key dimensions that planners must consider when look ing at participation is the required investment of time and personal research that a citizen must invest for a given participation method (10, 11). For example, a public meeting may require an hour of time plus travel and opportunity costs. On the other extreme, a short Internet post with social media tools such as Twitter or Facebook requires only a few seconds. In reality, planners should consider

J. S. Evans-Cowley, College of Engineering, Ohio State University, 275 West Woodruff Avenue, Columbus, OH 43210-1138. G. Griffin, Texas State University–San Marcos; Austin Office, Texas A&M Transportation Institute, 1106 Clayton Lane, Suite 300E, Austin, TX 78723. Corresponding author: G. Griffin, gg34@txstate.edu. Transportation Research Record: Journal of the Transportation Research Board, No. 2307, Transportation Research Board of the National Academies, Washington, D.C., 2012, pp. 90–98. DOI: 10.3141/2307-10 90

Evans-Cowley and Griffin

91

TABLE 1 Time Investment and Effectiveness, by Technique Participation Technique

Public Meeting

Survey Administered Online or on Paper

Facebook Post

Twitter Post

Citizen investment

High timea— opportunity cost

Medium timeb— opportunity cost

Low timec— opportunity cost

Lowest timed— opportunity cost

Low High Low-medium

Medium Medium Low

Low Low Medium

Medium Low Low

Effectiveness of participation purposes Involving citizens Voice in planning Sense of community a

Direct access to decision makers; likely includes multiple agenda items; board chair may reorder items. Demographic questions allow respondent stratification. Difficult to collect comments. d Nonlinear responses can make agency action seem opaque. b c

several participation methods that offer citizens a choice of their time investment, as illustrated in Table 1. Different involvement techniques have a variety of levels of citizen investment and concom itant effectiveness for the three previously identified key purposes of participation (6). Online participation methods are most often rec ommended as part of a broader involvement strategy, supplementing rather than supplanting conventional techniques. Grossardt and others (12) propose that one way to create a stronger base of information to support participation can be achieved through online tools. Online social networks represent one type of tool that can be used to support interaction between groups of people who share a common interest; for example, this project’s use of online social networks to support transportation planning. One of the features of many social networking tools is that they allow participants to microblog, which is the posting of short content such as phrases, quick comments, images, or links to Internet resources that may include photos, audio, or video. For example, in both Facebook and Twitter, a user can post a microblog such as “I’m stuck in traffic on 315,” which can stand by itself or be tied to additional information, such as a picture or video of the traffic jam or a link to a news story about traffic problems. This practice allows the user to share information with those in their social network—called “friends” on Facebook and “followers” on Twitter. Microblogging has tremendous popularity, with Facebook claim ing more than 750 million users (13) and Twitter claiming around 200 million (14) because of their ease of use and ability to share information in real time, either on a computer or mobile device (15–20). For example, 40% of Facebook users and 76% of Twitter users post from their mobile devices (21). Because of widespread use of social media, the demographics are becoming more represen tative of the population, a key issue for involvement (22). A 2008 study of Twitter found a median user age of 26, older than the users of Facebook and MySpace; Twitter users were seen as more diverse than the population as a whole (21). One challenge for the planner is how to build an online relationship with a segment of hundreds of millions of users that would then lead to microparticipation, the capture of microblogs, and then reaching out to the microblogger to engage in dialogue. Facebook is an exam ple of a closed social network that relies on users to make individual determinations of whether they wish to participate. For example, SNAPP has a Facebook page called “snappATX.” An individual in Austin can learn about SNAPP, decide to go to the snappATX Facebook page, and then assume the designation of “fan.” At that point, the fan will see SNAPP’s microblogs. The individual may then take the initiative to comment on a particular microblog post.

For microbloggers to engage, they must have strong community attachments—from either a highly local community of contributors or a larger community of interest around a topic. In Facebook, a planner cannot see the posts of a fan unless the planner is part of the fan’s network of friends—a term that implies mutual acceptance of communication. Previous research found significant challenges for planners attempting to use Facebook because of its closed social network and the fact that planners have not yet been effective in creating connections with the public (23, 24). In a study of microblogs, researchers found that connections that under lie the declared set of friends and followers are a key driver of their engagement choices (25). People also participate in online micro blogs because they enjoy it (26–29). The challenge is for planners to find ways to connect with the public and provide opportunities that are enjoyable as they build trust with the public (30). There is growing recognition that Twitter can be a powerful tool to engage the public (31–34). Twitter is an open social network that allows users to send messages known as “tweets” from a computer or mobile device (35). Twitter users can post microblogs of no more than 140 characters, which are shared with others who have signed up to receive them (21). In addition, all tweets can be searched unless a user puts privacy settings in place. The ability of plan ners to tap into the extensive world of microblogging offers a rich opportunity to reach an audience that may not otherwise participate in planning for the future of their community. Planners want to be able to engage microbloggers in dialogue and to share information. “Retweeting” is the forwarding or diffusion of information to new audiences (15). Retweeting is often associ ated with information sharing with a specific audience, commenting on someone’s tweet, agreeing with someone, or saving tweets for future access. Tweets with URLs (uniform resource locator code used to reference Internet resources) and hashtags (# symbol, used to mark key words or topics) are the most likely to be retweeted, and headline news is the most popular topic (36, 37). Diffusion is sig nificant and almost instantaneous, with retweeted microblogs reaching an average of 1,000 users (17). If planners are trying to share impor tant information, their ability to have followers retweet their micro blogs to other networks of followers has significant value. However, it should be noted that retweeting is difficult to measure directly because although “RT” is a standard notation for retweeting, not all retweeters use it. In addition, it can be difficult to find all the subsequent retweets of an original tweet (36). The communication style that a planner would use in micro participation may differ from approaches used in other forms of

92

participation. The best Twitter users are able to communicate with extreme economy and precision, creating significant meaning in the minimum number of characters (32). Using “sentiment analysis” on more than 150,000 postings, researchers were able to determine sentiment about brands (38). But are microblogs in aggregate rep resentative of the sentiments of the public as a whole? One study found that the sentiments of tweets on politicians or political parties mirror the measured off-line political sentiments (39). Although online discussion exhibits some deliberative characteristics, it is often inequitable, focusing instead on nonsubstantive issues with unconstructive engagement between bloggers (38). How do planners effectively engage the citizenry in an equitable and constructive dialogue? Noveck (40) argues that to be effective in online engagement, four factors must be met: the right questions must be asked, the right people must be asked, the process must be designed for the desired end, and the process must be designed for groups and focus on policy making rather than websites or web tools themselves. Design matters in that the project design must be created in a way that fosters a collaboration system that brings together the stakeholders and the policy makers. The researchers’ expectation of SNAPP was that they would be successful in generating significant public engagement through microparticipation.

Methodology To support microparticipation, SNAPP created a website that served as a centralized platform for the social networking tools. SNAPP used the http://snappatx.org website as a launching point, with links to its Twitter network, its Facebook page, and a blog that integrated an auto matic feed from partners’ blogs, as well as e-mail (conversation@ snappatx.org), surveys, and external resources. SNAPP captured all comments from SNAPP’s blog, any comments on selected local trans portation blogs that were tagged with “#snappatx” or “@snappatx,” and automatic (Atom and RSS format) feeds to news blogs that primarily deal with transportation. The SNAPP website includes an application that processes incom ing requests for recent microblogs. The main conversation page of the website lists the latest “snapps”—the microblogs that include the keyword “snappatx.” A pull-down menu allows further searches of microblogs by the transportation mode topics buses and rail, congestion and cars, and walking and bicycling. Both users and administrators are given a real-time, relevant Internet search of transportation issues particular to the Austin region in an easily understood format. SNAPPatx used standard conversational protocol in Twitter to engage in microparticipation. Upon reading a relevant microblog, a SNAPP facilitator would respond as in the following example: Microblogger: “I miss the GW parkway bike path in DC. Austin’s great, but nothing like an 18 mile paved, straight-ish, scenic, safe (no cars) bike path.” SNAPP facilitator@ajepst: “What’s the best surface to ride on? Does paving make a big difference to you in bike trail design?” #snappatx. The SNAPP facilitators add the hashtag “#snappatx” to every microblog that they post to enable searching for relevant conversa tions. SNAPP established partnerships with some of these third-party sources, who agreed to tag the posts that they would like SNAPP to capture in its feed with the keyword “snappatx.” SNAPP developed a capture tool that takes the microblogs (based on keywords) and stores them in a database for analysis. The SNAPP administrator can

Transportation Research Record 2307

then export the microblogs meeting certain criteria into a Microsoft Excel spreadsheet for conversion and analysis. SNAPP’s communication team focused on keeping ASMP in discussions through incrementally educating its component parts and pushing out information relevant to transportation-related topics; however, there are a limited number of instances in which the ASMP was directly referenced. The city of Austin was responsible for brand ing the ASMP, so there was a desire to separate its efforts from SNAPP. Two microsurveys were completed to focus on questions that the city staff wanted answered in regard to the ASMP and to the proposed package of projects that could be included in the bond package. SNAPP provided the researchers with 49,421 raw microblogs harvested from SNAPPatx’s Facebook, Blogger pages, and Twitter searches, as well as the processed microblogs, results of surveys, and any other data requested. Posts were generally not tagged with geo graphic locations and could not be evaluated according to the location of a post, although methods to perform this kind of analysis are increas ing (41). In addition, extensive correspondence between the principal researcher and the SNAPP administrator occurred between December 2010 and January 2011 to clarify data collection procedures, commu nication protocols, and communication between SNAPP and the city of Austin. The researchers then conducted further analysis. The purpose of this study was to answer the question of whether microparticipation can be analyzed to help understand the public’s views on transportation issues. To answer that question, the study used a multipart mixed-methods analysis of the microblogs. Was microparticipation equitable with a minimal number of heavy users? Did the results of the Austin microparticipation effort result in mean ingful information that was used to support decision making among policy makers in Austin? The methods are described further below.

Coding Protocol SNAPP systematically collected 49,421 microblogs that contained both “Austin” (“atx” or other abbreviation) and transportation-associated terms. SNAPP reviewed each of the microblogs for relevancy. The principal researcher of this article examined and commented on the protocol described below in April 2010, when the first month of microblogs were collected to ensure that the coding protocol was appropriate. SNAPP determined that microblogs were irrelevant if they were not related to both Austin and a relationship to the plan ning objectives of the ASMP. For example, “Austin and I are taking the train to Boston” is irrelevant because the reference is to a person named Austin rather than the city. The microblogs were reduced to 11,500 relevant microblogs, including 8,308 from general micro bloggers, 2,173 from SNAPPatx (1,007 facilitating conversation, and 1,166 sharing information), and 1,019 from media sources. The 8,308 microblogs from general microbloggers were coded on five variables: 1. Type (sharing, engaging, or analyzing), 2. Theme (general subject matter), 3. Topic 1, 4. Topic 2, and 5. Positive or negative sentiment (analysis of evaluative text and tracking of predictive judgments). Type refers to the type of comment (sharing, engaging, or analyz ing). Sharing microblogs are those sharing information (example: “RT @foxaustin: Listen up UT students. city of Austin cracking


93

down on E-bus riders that become unruly on the bus. http://bit.ly/ alXE1x”). Engaging microblogs are those in which a response is invited (example: “$7 fa a 24hr bus pass. . . . . how much is it in austin???? exactly. . . .”). Analyzing microblogs are those offering a comparison or analysis of transportation (example: “transit: Austin red line vs. Twin Cities Hiawatha line - I like both. Only similarity? single route”). Theme relates to the general subject matter of the messages. The themes included such ASMP indicators as economic development, regional integration, land use, and transit system. There are two topic classifications for each microblog because, in many cases, each includes multiple topics. For example, “RT @fitcityleblanc: Where do most of Austin’s bicycle-motorist collisions occur? Check this map! http://bit.ly/52X2g.” In this example, the theme is safety while topic one is bicycle and topic two is car. The selection of topics was based on the common topics discussed by microbloggers. Finally, SNAPP assessed each microblog for either positive or negative sentiment. Sentiment analysis is used in reference to the analysis of evaluative text and tracking of predictive judgments (42, 43). SNAPP’s analysis exclusively included examination for positive or negative polarity. In some cases, interpretation was nec essary where sarcasm is detected. For example, in “stuck again, gotta love this Austin traffic” the microblogger used love sarcastically to convey a negative sentiment. Caution was taken where ambiguity existed, and these posts were coded as neither positive nor negative. Approximately 45%, or 4,203 of the general microblogs, expressed sentiment.

they used the #snappatx tag to allow for easy tracking of conversa tions. As conversations extend, it becomes more difficult to fit all of the #, @, and “RT” tags into the ongoing conversation, so micro bloggers will chop off all or a portion of these tags. For analysis purposes, this practice makes it very difficult to track conversations, and there is a known undercounting of facilitated conversations in this study. This analysis uses the 1,007 microblogs that contain the SNAPP side of facilitation messages and then searches for the miss ing pieces of the conversation. For example, one of the SNAPP microblog messages contained “@calebsimpson So . . . how did the first-time bike commute go this morning? #snappatx” From that point, the larger microblog database was searched to identify the microblogger and the relevant date to determine whether there were other relevant microblogs that could be matched to the conversation. All conversations occurring within one week are counted as com pleted dialogues. The researchers identified 157 completed dialogues and examined each for its content and the way that SNAPP engaged the microblogger and guided the conversation. SNAPP attempted to engage with specific microblogs, but there were instances in which there were no responses (or none that could be found because of possible stripping of tags). For example, respond ing to the microblog “Austin traffic. never fails,” SNAPPatx posted, “@_bnicole08 ATX traffic never fails to what? Disappoint? Occur? Frustrate? Give you quality alone time? Where’d you get stuck? #snappatx.” However, the microblogger did not provide a further response. SNAPP had a total of 217 attempted but not completed engagements.

Sentiment Analysis

Equity of Microblogger Participation

For a more detailed sentiment analysis, the researchers objectively and systematically analyzed the general microblogs to assess emo tional, cognitive, and structural components of the text. An automated sentiment analysis was undertaken to evaluate the general microblogs. A text analysis software, Linguistic Inquiry and Word Count (LIWC) (44), assessed emotional, cognitive, and structural components of the tweets using a psychometrically validated internal dictionary. For example, the software looks for words such as “annoyed” or “hate,” along with 182 other words that characterize anger. LIWC has been used in a variety of fields, including political science, psychology, and linguistics (43). LIWC analyses have been used to examine short texts, such as instant messages and tweets (39, 42). Using Yu et al.’s (43) methodology, all of the microblogs were aggregated into a single text sample for evaluation by LIWC. The aggregation occurred in two topics: microblogs related to mode of travel and microblogs related to the bond election and the ASMP.

In the off-line world of participation, there are the planning regulars, citizens who regularly attend public meetings and participate. Within meetings, there are those who sit quietly and others who dominate the discussion. Is the same thing happening online, where only a small group of people tweet about transportation in Austin? To determine equality of participation, an examination of the individual micro bloggers and the number of relevant microblogs contributed was undertaken. The microbloggers were categorized as one-time con tributors, light contributors (2–9 microblogs), medium contributors (10–19 microblogs), heavy contributors (20–49 microblogs), or very heavy contributors (more than 50 microblogs), both on a monthly and an entire-period basis. This allowed the researchers to determine whether particular microbloggers were dominating discussions in either a given month or over the entire 7-month period. Following SNAPP’s principal microparticipation process, SNAPP invited every user who had participated or whose microblog was captured to participate in a survey about their experience. This survey, which attempted to capture some demographic data about a sample of the microparticipation, asked for the age and gender of participants.

Analysis of Microparticipation Dialogues The researchers also examined the microparticipation dialogues. A microblog can be limited to a simple statement that shares information. Honeycutt and Herring (34) found that the vast majority of @ symbols were used to direct a tweet to a specific addressee. A review of the 8,308 relevant tweets was undertaken to determine the portion that included the symbol @ and “RT” (for retweet), and contained a URL. The markers help to understand the intent of the microblogger who is engaging in microparticipation. Measuring microparticipation between two individuals is particu larly difficult. When SNAPP facilitators engaged in a conversation,

Influence on Decision Making In order to understand the degree to which microparticipation influ enced decision making, SNAPP contracted with a consultant to under take independent pre-project and post-project interviews with key stakeholders. Interviews were conducted with nine people, including the city’s communications director, assistant director of transporta tion, mayor pro tem, two council members, a transportation planner

94


The results are explained below, starting with an analysis of micro participation, followed by a discussion of equality in participation and then the influence on decision making.

blogs represented only 0.2%. This result can be attributed to the method by which microblogs were obtained. Because of privacy settings, only microblogs posted on SNAPPatx’s Facebook page could be collected. As described in the methodology section above, each microblog was examined to determine its content. Despite their brevity, the microblogs expressed substantive issues that were relevant to the ASMP. The public discussed 173 different topics, from accidents to Willie Nelson Boulevard. Figure 1 includes the 10 most discussed topics. These topics indicate that the public was routinely discussing topics that are directly relevant to the ASMP; however, discussion was heav ily dominated by traffic. Planning issues were nearer the bottom of the list of broad categories. This finding underscores two important notions: (a) planners engaging the public should not assume a public curious of the power of planning to improve a city, and (b). analysis of social media streams can be a valuable tool to engage people not typically involved in planning efforts. When microbloggers discuss transportation topics, in what context are they raised? Microbloggers discussed a variety of transportation topics, and the sentiment varied from topic to topic. To explore the sentiment within topics, the authors aggregated tweets about each topic area and then mapped the degree to which sentiments varied as a whole by topic. Figure 2 illustrates the frequency of positive or negative sentiment by topic. This project was ultimately about the mobility plan and the bond election. There was minimal emotion associated with the bond elec tion and some level of positive motion. When people tweeted about the mobility plan, they used words that described the plan as an achievement.

Analysis of Microparticipation

Microparticipation Dialogues

How can microparticipation be analyzed to create an understanding of the public’s views on transportation topics? To start, it is impor tant to understand where the microblogs come from. Of the 8,308 relevant microblogs, the vast majority (99.7%) came from tweets. Facebook represented 0.1% of the microblogs, and comments on

What about the content of the microblogs? Approximately 33% of them shared information, 38% engaged, and 20% analyzed. Micro participation is achieved through dialogue, which can be achieved in part through the use of the @ symbol, “RT,” and the URL. These can represent conversations between a microblogger and others in his

with the Metropolitan Planning Organization, a leading blogger in Austin, and two consultants for the ASMP. The pre-project inter views took place in March of 2010. The participants responded to a series of questions about their experience levels with social media, how effective social media is for pushing information out, how effective social media is for engaging people, what optimal par ticipation would be, and how participation in social media should be measured. The post-project interviews were undertaken between December of 2010 and January of 2011. The interviewees were asked five questions: How well did SNAPP help in pushing infor mation out to the public? How well do you think SNAPP engaged the public in decision making? How did SNAPP perform versus your [previously stated] ideal of public engagement? How do you think SNAPP helped with decision making? What do you think was frustrating about making decisions with information from SNAPP? Beyond the pre- and post-project interviews, the researchers interviewed the SNAPP administrator and a staff member in the City’s Transportation Department. This allowed the researchers to better understand the decision-making process beyond the questions asked in the pre- and post-project interviews. The central question from the researchers: why wasn’t SNAPP more influential in the decision-making process?

Results

Motorcycle/Scooter

Categories of Comment

Planning Other Walking Passenger Rail Cars Bus Bicycling Traffic 0%

5%

10%

15% 20% 25% 30% Percent of Total (n=8,308)

FIGURE 1 Ten most discussed microblog topics.

35%

40%

45%


95

FIGURE 2 Frequency of positive or negative sentiment, by topic. (Topics with five or more microblogs.)

or her social network or with particular microbloggers in the broader community. Approximately 57% of microblogs in this study contain an @ symbol. This indicates that people are not just using micro blogs to share their opinions but are also engaging in interactive discussions. This study found a high degree of retweeting—24% among the general microblogs, demonstrating that microbloggers were retweeting information about transportation in Austin. In part, SNAPP’s role was to push information out that could be shared with others. The information provided on social networks by SNAPP was retweeted 79 times. A full 60% of messages contained a link to a website. These results indicate that people were finding transportation information that they then shared with their network of followers. Although 57% of the microblogs in this study use @ to indicate an effort to engage in dialogue, it is difficult to document conversations. Of the 374 documentable SNAPP attempts to engage in dialogue, 42% received a response from the microblogger. This is a high rate of participation when one considers that SNAPP was attempting to drop in on conversations that did not include SNAPP. The 157 com pleted conversations between SNAPP and microbloggers that could be documented for analysis were primarily brief but specific. Each conversation was unique and highly contextual, limiting the ability to generalize. If a microblogger complained about traffic, the tweets quickly moved to the precise location where that person often experiences traffic difficulty. In the following dialogue, SNAPP was able to educate and receive information on potential solutions. The microblogger starts by telling a fellow microblogger his or her thoughts about Austin, noting that it is car-dependent and lacks light-rail as a mode of transportation. SNAPP then provides information that urban rail is part of the bond election and asks how the microbloggers would improve mobility. While there is a sense of humor about getting rid of the Interstate highway, it is also clear that land use is a critical issue. The following

dialogue is instructive about a resident’s commuting patterns and his or her ideas for the future of transportation: @gary_hustwit Austin. Good: nice public outdoor spaces. Bad: Very car dependent, no urban light rail. #Urbanized. @compactrobot Urban rail is an item on the 2012 transport bond so keep an eye out. How else would you improve Austin mobility? #snappatx. @SNAPPatx reduce the need for mobility to begin with. More VMU. Lessen the grip of NAs. @SNAPPatx oh yeah, also nuke I-35 from space. @compactrobot Well, that might create a different sort of traffic jam . . . Where are your worst I-35 trouble spots? #snappatx. @SNAPPatx I avoid it, frankly. I just don’t like the way it’s sliced downtown in half and isolated the east side from the city. @SNAPPatx it’s great for trucking companies and horrible for Austin residents. and it’s a giant eyesore. @compactrobot All fair points. Do you successfully take local routes to avoid I-35? Do you feel similar ire toward Mopac too? #snappatx. @SNAPPatx I only take 35 if I’m eating on the east side, & only after rush hour. otherwise I’ll use airport, Lamar, or Guadalupe & cut over. @SNAPPatx Mopac’s not as bad. but then I don’t have to use it to daily to go to/from work.

Equality of Microblogger Participation The total number of microblogs per microblogger was measured to determine the equality of participation. Table 2 illustrates the rate of participation. These results indicate that there were a wide variety of

96


TABLE 2 Microblogger Rate of Participation Users

Messages

User Group

Total

Share (%)

Total

Share (%)

One time (1) Light (2–5) Medium (6–20) Heavy (21–79) Very heavy (≥80) Total

3,690 684 49 14 2 4,439

83.1 15.4 1.1 0.3 0.0 100

3,690 1,760 465 537 178 6,576

56.1 25.9 7.1 8.2 2.7 100

Note: Data include only general microblogs.

participants. In part, this may be because SNAPP was an observer of and engager in conversations already happening. When examining the rate of messaging, it is apparent that heavy users account for a very small portion of the messaging. The heaviest user is Austin_Crime, a microblogger who reports on crime issues. SNAPP picked up tweets related to transportation-related accidents or other transportation-related content. Most came from one-time users (57%). One-time users and light users accounted for more than 75% of the messages. The post-participation survey of participants indicated that 58% were male and that the largest segment of respondents (57%) was people between the ages of 23 and 44. The participants are daily users of social networking and primarily use these tools for social activities such as networking, keeping in touch, and socializing. Two-thirds of the respondents indicated that they use social net working when communicating with friends, and 9% indicated that they use it when communicating with colleagues. The demograph ics of microbloggers may not be representative of either the Austin citizenry or electorate. While statistics on users in Austin are not available, one can reasonably assume that Twitter and other microblog users are not the majority of the population.

Influence on Decision Making SNAPP’s end goal was to use the meaningful information obtained through microparticipation to influence the decision-making project. Despite the pervasive discussions introduced by SNAPP to the Austin Strategic Mobility Plan public involvement process, city staff from the Austin Transportation Department report that this information was not formally presented to the Austin city council as the trans portation bond language and project scopes were being developed (K. Villalon, personal communication, January 5, 2011). However, the project’s results were communicated individually to some city council members by the SNAPP administrator. Villalon explained that the successful language for provoking online discussions was not always compatible with the city’s effort to remain neutral, which created a challenge in working with SNAPP. As one city official put it “one of the overarching issues SNAPP faced was the temptation to lead into advocacy, which we cannot do with taxpayer money” (K. Villalon, personal communication, February 1, 2011). For exam ple, an October 1, 2010, tweet by SNAPP read, “possible solution to air pollution: Renewable energy. So why hasn’t it passed through Congress? http://ow.ly/2N7HH #snappatx”. This fact may have been effective in inciting comments, but it may not have been perceived

as completely neutral. The city staff pushed back because they said the surveys SNAPP developed were leading or too short to provide an accurate picture or choice. One city staff member described the results as “anonymous” and “little more than a finger in the wind” (K. Villalon, personal communication, February 1, 2011). From the pre- and post-project interviews, there was a high expectation of the ability of social networking to inform the public. However, there was significant skepticism about SNAPP’s ability to engage the public in decision making. The interviewees were impressed with how much SNAPP helped to engage the public. The respondents also appreciated the sentiment graphs. While impressed, decision makers pointed to the need for more metrics that would help them validate the public information and understand the stories behind the data. Respondents indicated that they were frustrated that the provided reports lacked specific policy direction and did not address authen ticity. They wanted to have direction on specific policy decisions but also recognized that it would be unrealistic to garner detailed commentary on policy options. The issue of who participates and who they represent was central to some respondents. Respondents indicated that greater visibility of participant information might alleviate these concerns in the future.

Discussion of Project Results Ultimately, the voters of Austin approved the transportation bond, with 55% of voters supporting it. SNAPP used microparticipation to educate and engage the public around the ASMP that led to the bond election. The highly experimental nature of the project meant that there was a learning curve for both SNAPP and the City of Austin in learning how to use the results of public engagement effectively. The results indicate that information on microblogs can be aggregated in a meaningful way, that SNAPP was able to achieve more equi table participation, and that microparticipation was not effective in supporting decision making in this case. The sentiment analysis demonstrated that it is possible to aggregate microblogs to create meaning. City officials reported that having sentiment analysis was particularly helpful in understanding the perspectives of the public. Furthermore, previous research suggests that the observed tweet sentiments are representative of the broader public (39, 45, 46). Our research is based on the LIWC text analysis software, which is not specifically tailored to classify short tweets, common acronyms, and emoticons used. This means that the analysis may not have fully represented the true sentiment expressed in the microblogs. Future research could develop an analysis tool specifically designed for microblog analysis. SNAPP was effective in generating engagement. SNAPP was able to generate 203 fans, seven times the average number found in previous research of Facebook in planning projects (24). This was, in part, achieved through a strategy of “friending” organiza tions and then encouraging them to invite their members to join SNAPP. However, finding and engaging each individual through Facebook’s closed network required substantial effort. SNAPP gen erated 366 followers on Twitter and 217 fans on Facebook. SNAPP exceeded the number of Twitter followers of 98% of Twitter users by having more than 300 followers (33). This success probably resulted from SNAPP’s ability to directly engage with Twitter users. Retweeting is most effective with concerted efforts to limit tweets to a single topic (45), the strategy used by SNAPP. SNAPP’s efforts resulted in an average of 45 microblogs retweeted per week. Based on


research that showed that a retweet reaches an average of 1,000 users, SNAPP was potentially reaching 45,000 people per week (17). Some fans and followers were media outlets such as the Austin Chronicle newspaper, Fox7 television, and the University of Texas public radio. These connections allowed SNAPP to reach traditional news media, providing the potential to extend the reach of the communi cation without requiring press releases and by potentially attracting fans or followers. While the diffusion of microblogs is important, what is the informational value of what is being shared? SNAPP found that only 23% of the microblogs were relevant, similar to another study on the value of tweets (47, 48). The city was particu larly interested in microblogs that were analyzing or engaging, which represented 57.5% of the relevant microblogs. The City of Austin was concerned that some people would not comment and others would dominate the online discussion. The authors found that when it comes to equality, SNAPP achieved better results than found in other studies, with most contributors contribut ing only one time (39, 49). Further study should examine the rep resentativeness of participation and the degree to which the online views echo those of the broader population. One of the challenges for elected officials and transportation staff members was how to use the analysis provided by SNAPP to sup port decision making. Data coding and analysis is labor intensive, resulting in a typical lag of 15 to 30 days to provide monthly analysis. City officials reported that sentiment analysis was a very helpful way to understand what the public was saying about a topic. In the post-project survey, these officials reported that it was difficult to understand the relationships among the data (50). The promise of microparticipation is that it provides an opportunity to get nearly real-time tracking of public opinion. Yet, further development of analysis techniques are needed to enable timely reporting that will be most useful to public officials. The city had unrealistic expectations of what the use of microblogs could deliver, in part because the microparticipation was not inte grated into a comprehensive engagement plan. The city is accurate in that the microblog user’s identity is not fully known and that what SNAPP captured is “a finger in the wind.” However, the city was provided with much more information about what people are saying about transportation in Austin than what they were receiving through more traditional forums. The city simply did not understand how to effectively use this information, and the SNAPP team was unable to deliver the information in a format that the city found highly usable. As one city official stated, “This was an experiment for the city, and I think we learned that the time and attention needed to sup port Twitter dialogue, beyond informational tweets, is not currently feasible, even with automated analytics” (K. Villalon, personal communication, Feb. 1, 2011). In this case, decision makers asked for help in understanding the story behind the data. One of the key weaknesses of this project was the inability to translate the results of the engagement into stories that could resonate with decision makers. Because this project began after initial engagement of the pub lic officials in the planning process, they did not have an opportunity to direct how the project could coordinate best with other efforts.

Conclusions Other cities developing strategic approaches to public involvement with social media should engage public officials and other key stake holders early in the public involvement planning process. Many of them have strong social media campaigns of their own, and could be

97

ideal drivers of public engagement in partnership with transportation planners. This research has experimented with different methods for analyzing the data that could, with further refinement, lead to the ability to influence decision making in future microparticipation efforts. Further research should empirically investigate the way in which information can be weighted from microblogging sites. Planning is not leading the call for new technologies in social media, but it is riding a wave of rapid change in global and local communication networks. The growth in the use of location-aware social media, such as geo-tagged microblogging, has the potential to extend planning participation to citizens, who could digitally tag such planning issues as, in this case, the location of traffic congestions, areas where bike paths are needed, or other transportation-related issues. Smartphone technologies have the potential to further democ ratize planning by allowing participants to join the planning conversa tion from their regular locations and on their own terms. The authors believe that microparticipation provides new and valuable opportuni ties for public participation that should be integrated into a broad-scale participation process. Future research is needed to develop a model for using microparticipation for effective planning engagement.

Acknowledgments This research was supported by a Federal Transit Administration Public Transportation Participation Pilot Program research grant. Glenn Gadbois managed local implementation of the project, and Brittany Kubinski assisted with data compilation. Finally, the authors appreciate the comments of three anonymous reviewers from the TRB Public Involvement in Transportation Committee. References 1. Townsend, A., R. Maguire, M. Liebhold, and M. Crawford. A Planet of Civic Laboratories: The Future of Cities, Information, and Inclusion. Technical report. Institute for the Future, Palo Alto, Calif., 2010. 2. Capital Area Metropolitan Planning Organization, Group Solutions RJW, and Estilo Communications. People, Planning, and Preparing for the Future: Round 2 Public Involvement. Technical report. Capital Area Metropolitan Planning Organization, Austin, Tex., 2009. 3. City of Austin. Making Austin: Public Participation in a New Comprehensive Plan. Technical report. City of Austin, Tex., 2009. 4. Evans-Cowley, J. S. Planning in the Age of Facebook: The Use of Social Networking in Planning Processes. Geojournal, Vol. 75, No. 3, 2010, pp. 407–420. 5. Goodspeed, R. The Dilemma of Online Participation: Comprehensive Planning in Austin, Texas. http://web.mit.edu/rgoodspe/www/papers/ RGoodspeed-Austin_Online_Participation_9-19-10.pdf, September 19, 2010. Accessed Feb. 10, 2011. 6. American Planning Association. Planning and Urban Design Standards. Wiley Graphic Standards. John Wiley & Sons, Hoboken, N.J. 2006. 7. Bullard, R. D., G. S. Johnson, and A. O. Torres. Highway Robbery: Transportation Racism and New Routes to Equity. South End Press, Cambridge, Mass., 2004. 8. Chaves, E., E. Garcia, and S. Gilmore. Consensus Building in Trans portation Planning: Practice Case Evaluation of Process and Outcomes. In Transportation Research Record: Journal of the Transportation Research Board, No. 1981, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 76–83. 9. Ahern, J., L. Jennings, B. Fenstermacher, P. Warren, N. Charney, S. D. Jackson, J. R. Mullin, Z. Kotval, S. F. Brena, S. A. Civjan, and E. Carr. Issues and Methods for Transdisciplinary Planning of Com bined Wildlife and Pedestrian Highway Crossings. In Transportation Research Record: Journal of the Transportation Research Board, No. 2123, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 129–136.

98

10. Comeau, P. S., and D. A. Rodriguez. Picking Publics Properly: An Artful Science. In Transportation Research Record: Journal of the Transportation Research Board, No. 1706, TRB, National Research Council, Washington, D.C., 2000, pp. 92–99. 11. Lowry, M. B., T. L. Nyerges, and G. S. Rutherford. Internet Portal for Participation of Large Groups in Transportation Programming Deci sions. In Transportation Research Record: Journal of the Transportation Research Board, No. 2077, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 156–165. 12. Grossardt, T., K. Bailey, and J. Brumm. Structured Public Involvement: Problems and Prospects for Improvement, In Transportation Research Record: Journal of the Transportation Research Board, No. 1858, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 95–102. 13. Facebook. Facebook Statistics. http://www.facebook.com/press/info. php?statistics, 2011. Accessed July 30, 2011. 14. BBC News. Twitter Cofounder Jack Dorsey Rejoins Company, 2011. http://www.bbc.co.uk/news/business-12889048. Accessed July 30, 2011. 15. Boyd, D., S. Golder, and G. Lotan. Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter. Proc., HICSS 2010, 2010, pp. 1–10. 16. Java, A., X. Song, T. Finin, and B. Tseng. Why We Twitter: Under standing Microblogging Usage and Communities. Proc., WebKDD/ SNA-KDD, 2007, pp. 56–65. 17. Kwak, H., C. Lee, H. Park, and S. Moon. What is Twitter, a Social Network or a News Media? Proc., 19th International Conference on World Wide Web, 2010. Accessed Dec. 18, 2010: http://portal.acm.org/citation. cfm?id=1772690.1772751. 18. Smith, A., and L. Rainie. 8% of Americans Use Twitter. Report: Social Networking, Web 2.0, Pew Internet and American Life Project, Pew Research Center, 2010. http://www.pewinternet.org/Reports/2010/ Twitter-Update-2010.aspx. Accessed Jan. 8, 2011. 19. Wright, A. Mining the Web for Feelings, Not Facts. New York Times, Aug. 8, 2009. 20. Zhao, D., and M. B. Rosson. How and Why People Twitter: The Role That Micro-blogging Plays in Informal Communication at Work. Proc., GROUP’09, 2009, pp. 243–252. 21. Lenhart, A., and S. Fox. Twitter and Status Updating. Pew Internet and American Life Project, Pew Research Center, 2009. http://fortysouth. com/wp-content/uploads/2009/05/Twitter-and-status-updating.pdf. Accessed Dec. 18, 2010. 22. Alter, R., M. Lewiecki, M. Renz-Whitmore, and D. P. Albright. Account able Public Involvement: Partnership Approach to Proposed Trans portation Project. In Transportation Research Record: Journal of the Transportation Research Board, No. 2077, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 46–53. 23. Evans-Cowley, J. S., and J. Hollander. The New Generation of Public Participation: Internet-Based Participation Tools. Planning Research and Practice, Vol. 25, No. 3, 2010, pp. 397–408. 24. Lewis, K., J. Kaufman, M. Gonzalez, A. Wimmer, and N. Christakis. Tastes, Ties, and Time: A New Social Network Dataset Using Facebook. com. Social Networks, Vol. 30, No. 4, 2008, pp. 330–342. 25. Huberman, B. A., D. M. Romero, and F. Wu. Social Networks that Matter: Twitter Under the Microscope. First Monday, Vol. 14, No. 1, 2009. http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/ view/2317/2063. 26. Bonaccorsi, A., and C. Rossi. Altruistic Individuals, Selfish Firms? The Structure of Motivation in Open Source Software. First Monday, Vol. 9, No. 1, 2004. http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/ article/view/1113/1033. 27. Ghosh, R. A. FM Interview with Linus Torvalds: What Motivates Free Software Developers? First Monday, Vol. 3, No. 3, 1998. http://first monday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/1475/1390. Accessed Aug. 2, 2012. 28. Hars, A., and S. Ou. Working for Free? Motivations for Participating in Open Source Projects. International Journal of Electronic Commerce, Vol. 6, No. 3, 2002, pp. 25–39. 29. Hertel, G., S. Niedner, and S. Hermann. Motivation of Software Develop ers in the Open Source Projects: An Internet-Based Survey of Contributors to the Linux Kernel. Research Policy, Vol. 32, No. 7, 2003, pp. 1159–77. 30. Granell, J., and D. Grachen. Strategies for Streamlined Participation by Native American Governments in Federal Transportation Projects.


In Transportation Research Record: Journal of the Transportation Research Board, No. 1931, Transportation Research Board of the National Academies, Washington, D.C., 2005, pp. 63–67. 31. Berg, N., and T. Halbur. Planetizen Picks: Top Twitter Feeds on Urban Planning. Planetizen. http://www.planetizen.com/twitterfeeds. Accessed Dec. 10, 2010. 32. Carr, D. Why Twitter Will Endure. New York Times, January 1, 2010. http://www.nytimes.com/2010/01/03/weekinreview/03carr.html. Accessed March 5, 2012. 33. Cheng, A., M. Evans, and H. Singh. Inside Twitter: An In-Depth Look Inside the Twitter World. http://www.sysomos.com/docs/Inside-TwitterBySysomos.pdf. Accessed March 5, 2012. 34. Honeycutt, C., and S. C. Herring. Beyond Microblogging: Conversation and Collaboration via Twitter. Proc., 42nd Hawaii International Conference on Systems Science, 2009. HICSS. Bloomington, Ind., Jan. 5–8, 2009, pp. 1–10. 35. The Pew Charitable Trusts. Twitter and Status Updating. http://www. pewtrusts.org/our_work_report_detail.aspx?id=48936&category=56. Accessed Dec. 15, 2009. 36. Suh, B., L. Hong, P. Pirolli, and E. H. Chi. Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network. Proc., 2nd International Conference on Social Computing, Minneapolis, Minn., 2010, pp. 177–184. 37. Zarella, D. Science of Retweets, 2009. http://danzarrella.com/the-scienceof-retweets-report.html. Accessed Dec. 19, 2010. 38. Jansen, B., M. Zhang, K. Sobel, and A. Chowdury. Twitter Power: Tweets as Electronic Word of Mouth. Journal of the American Society for Information Science and Technology, Vol. 60, 2009, pp. 2169–2188. 39. Tumasjan, A., T. O. Sprenger, P. G. Sandner, and I. M. Welpe. Elec tion Forecasts with Twitter: How 140 Characters Reflect the Political Landscape. Social Science Computer Review, December 2010, pp. 1–17. 40. Noveck, B. S. Wiki Government: How Technology Can Make Government Better, Democracy Stronger, and Citizens More Powerful. Brookings Institution Press, Washington, D.C., 2009. 41. Prevost, D. L. Geography of Public Participation: Using Geographic Information Systems to Evaluate Public Outreach Program of Trans portation Planning Studies. In Transportation Research Record: Journal of the Transportation Research Board, No. 1981, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 84–91. 42. Tausczik, Y. R., and J. W. Pennebaker. The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. Journal of Language and Social Psychology, Vol. 29, 2009, pp. 24–54. 43. Yu, B., S. Kaufmann, and D. Diermeier. Exploring the Characteristics of Opinion Expressions for Political Opinion Classification. Proc., 2008 International Conference on Digital Government Research, Montreal, Quebec, Canada, 2008, pp. 82–91. 44. LIWC, Inc. Linguistic Inquiry and Word Count. Computer software, LIWC, Inc., 2007. http://www.liwc.net/. 45. Suenami, A., and M. Yutaka. Voter Behavior Analysis and Election Campaign Strategies Using Information on the Web (in Japanese). Proc., JSAI SIG-KBS, Tokyo, No. 87, 2010, pp. 1–6. 46. Tweetminster. Is Word-of-Mouth Correlated to General Election Results? The Results Are In, 2010. http://www.scribd.com/doc/31208748/Tweet minster-Predicts-Findings. Accessed June 25, 2010. 47. Pearanalytics. Twitter Study, 2009. http://www.pearanalytics.com/blog/ wp-content/uploads/2010/05/Twitter-Study-August-2009.pdf. Accessed Dec. 15, 2009. 48. Naaman, M., J. Boase, and C.-H. Lai. Is it Really About Me? Message Content in Social Awareness Streams. Proc., CSCW 2010 ACM Conference on Computer Supported Cooperative Work, 2010, pp. 189–192. 49. Jansen, H. J., and R. Koop. Pundits, Ideologues, and Ranters: The British Columbia Election Online. Canadian Journal of Communication, Vol. 30, 2005, pp. 613–632. 50. Ewen, C. Post-Project Qualitative Interviews—Summary of Work and Results Social Networking and Planning Project. Draft 2010. http:// www.snappatx.org/documents/SNAPP-post-project%20Interviews. pdf. Accessed Aug. 2, 2012. The Public Involvement in Transportation Committee peer-reviewed this paper.