Planning and Zoning Board - Public HearingCity of Mesa.docx

Planning and Zoning Board - Public Hearing
City of Mesa
Meeting Agenda - Final
Council Chambers
57 E. First Street
Chair Suzanne Johnson
Vice Chair Michael Clement
Boardmember Lisa Hudson
Boardmember Shelly Allen
Boardmember Michelle Dahike
Boardmember Steve Ikeda
Boardmember Dane Astle
Council Chambers - Upper Level4:00 PMWednesday, November
18, 2015
Consent Agenda - All items listed with an asterisk (*) will be
considered as a
group by the Board and will be enacted with one motion. There
will be no

separate discussion of these items unless a Boardmember or
citizen requests, in
which the item will be removed from the consent agenda, prior
to the vote, and
considered as a separate item.
Items on this agenda that must be adopted by ordinance and/or
resolution will
be on a future City Council agenda. Anyone interested in
attending the City
Council public hearing should call the Planning Division at
(480) 644-2385 or
review the City Council agendas on the City's website at
www.mesaaz.gov to
find the agenda on which the item(s) will be placed.
Call meeting to order.
1 Take action on all consent agenda items.
Items on the Consent Agenda
2 Approval of minutes from previous meetings.
PZ 15589 Minutes from the October 20 and October 21, 2015
study sessions and
regular hearing.
*2-a

City of Mesa Printed on 11/17/2015
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November 18, 2015Planning and Zoning Board - Public
Hearing
3 Take action on the following zoning case:
PZ 15590 Z15-042 District 1 1003 North Dobson Road. Located
on the east side of
Dobson Road and south of Loop 202 Freeway (3.0 ± acres). Site
Plan
Modification. This request will allow for the development of a
multi-tenant
shell retail building with a drive-thru. Chris Lindholm,
Architecture Design
Collaborative, applicant; Andrew Gracey, KIMCO Realty,
owner.
(PLN2015-00304)
Staff Planner: Wahid Alam
Staff Recommendation: Approval with Conditions

*3-a
4 Discuss and make a recommendation to the City Council on
the following
zoning cases:
PZ 15591 Z15-033 District 3. 2210 West Southern Avenue.
Located west of Dobson
Road on the north side of Southern Avenue. (1± acre). Rezone
from OC to
LC and site plan review. The request will allow the
development of a
restaurant with a drive-thru. Neil Feaser, RKAA Architects,
Inc., applicant;
Jeffrey D. Garrett, President, GDC San Jose and Southern, LLC,
owner.
(PLN2015-00347) Continued from October 21, 2015
Staff Planner: Kim Steadman
Staff Recommendation: Continuance to December 16, 2015
*4-a
PZ 15592 Z15-034 District 4. 412 North Macdonald. Located
north of University
Drive on the west side of Macdonald. (0.5± acre). Rezone from
DR-1-HD
to DR-1-HD-HL. This request will allow for historic landmark

designation.
Heather Scantlebury, applicant and owner. (PLN 2015-00276)
*4-b
Page 2 City of Mesa Printed on 11/17/2015
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Hearing
PZ 15593 Z15-038 District 3. 1126 West Medina Avenue and
2345 and 2355 South
Alma School Road. Located south of Baseline Road on the east
side of
Alma School Road (5.4± acres). Rezoning from OC to RSL-4.0
PAD and
OC-PAD with Site Plan Modification. This request will allow

for the
development of a small lot single-residence subdivision. Mike
Hare,
Ashland Properties, applicant; Munter GST EX Family
TR/Munter Non-ex
Mar TR II, Desert Schools Federal Credit Union, Terradyne,
LLC, owners.
(PLN2015-00392)
Staff Planner: Lesley Davis
*4-c
PZ 15594 Z15-039 District 3. 2200 through 2300 blocks of East
Jacinto Avenue
(south side). Located north of Baseline Road and east of Gilbert
Road
(5.4± acres). Rezoning from LC to RM-3-PAD and Site Plan
Review. This
request will allow for the development of a multi-residence
project. Jeff
Welker, Welker Development Resources, applicant; Genica
Arizona, LLC,
owner. (PLN2015-00386)

*4-d
PZ 15595 Z15-040 District 1 205 East McKellips Road. Located
on the south side of
McKellips Road and west of Mesa Drive (5.1± acres). Rezoning
from LC to
ID-2 and Site Plan Review. This request will allow for the
development of a
large vehicle rental facility, mini-storage facility and outdoor
RV and boat
storage. Ralph Pew, Pew & Lake, PLC, applicant; Paul and
Douglas
Stecker Trustees; Margaret Mulhern Revocable Trust, owner.
(PLN2015-00394)
Staff Planner: Wahid Alam
*4-e
PZ 15596 Z15-041 District 2. 2600 East Southern Avenue.
Located on the north
side of Southern Avenue and west of Lindsay Road (1.43±
acres).

Rezoning from RS-43 to OC and Site Plan Review. This request
will allow
for the development of a banquet and conference center. Reese
L.
Anderson, Pew & Lake, PLC, applicant; Shelley and Michael
McKown,
owner. (PLN2015-00388)
*4-f
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Hearing

5 Discuss and take action on the following preliminary plats:
PZ 15599 "Medina Court" District 3. 1126 West Medina Avenue
and 2345 and
2355 South Alma School Road. Located south of Baseline Road
on the
east side of Alma School Road (5.4± acres). Preliminary Plat.
This request
will allow for the development of a 14 lot subdivision. Mike
Hare, Ashland
Properties, applicant; Munter GST EX Family TR/Munter Non-
ex Mar TR
II, Desert Schools Federal Credit Union, Terradyne, LLC,
owners.
(PLN2015-00392) Companion case to #Z15-038
*5-a
PZ 15598 "Jacinto Lofts" District 3. 2200 through 2300 blocks
of East Jacinto
Avenue (south side). Located north of Baseline Road and east of
Gilbert
Road (5.4± acres). Preliminary Plat. This request will allow for
the

subdivision of approximately 5.4 acres. Jeff Welker, Welker
Development
Resources, applicant; Genica Arizona, LLC, owner. (PLN2015-
00386)
Companion case to #Z15-039
*5-b
PZ 15597 "Sunland Springs Village Unit Nine" District 6. The
11400 through
11500 blocks of East Guadalupe Road (south side). Located at
the
southwest corner of Meridian Road and Guadalupe Road.
Preliminary
Plat. This request will allow for the subdivision of
approximately 23 acres.
Jeff Giles, Clouse Engineering, Inc., applicant; Craig Ahlstrom,
Springs
Nine Development, Inc., owner. (PLN 2015-00454)

*5-c
6 Other Business.
7 Adjournment.
The City of Mesa is committed to making its public meetings
accessible to
persons with disabilities. For special accommodations, please
contact the City
Manager's Office at (480) 644-3333 or AzRelay 7-1-1 at least
48 hours in advance
of the meeting.
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PRACTICE FORUM
The New Generation of Public
Participation: Internet-based
Participation Tools
JENNIFER EVANS-COWLEY & JUSTIN HOLLANDER

Abstract
Since the advocacy planners of the 1960s first brought
widespread public participation to the
planning process, there have been innovations and
improvements. However, the participation
practices in the real world, with its face-to-face politics of
difference and unequal power relations,
are flawed. Today, technology allows for an entirely new
generation of forms and practices of
public participation that promise to elevate the public discourse
in an unprecedented manner while
providing an interactive, networked environment for decision-
making. This is occurring with
asynchronous communities interacting with one another on a
variety of planning subjects, which
allows for more democratic planning and more meaningful
participation. In this paper, we review
the ways in which today’s web-based virtual worlds, like
Facebook and Second Life, provide
platforms for public participation in planning in a manner
distinct from previous formats. We
explore the different ways that citizens and communities are
using web-based technologies for
citizen participation, including the use of Facebook for
community organizing around planning
issues and of Second Life for virtual workshops. We include
case studies of communities that are
using these tools. The paper concludes by exploring the
contribution that virtual participation can
make to planning and examines the challenges that it poses.
Introduction
As technology has proliferated, it has infiltrated
to both the neighborhood scale and the global
scale. Jacobs argues that ‘word does not move

around where public characters and sidewalk life
are lacking’ (1993, p. 69). In today’s world,
words are moving rapidly, allowing neighbors to
share news that was once passed along on
porches or stoops (Blum, 2007). Citizens are
increasingly sharing information via social
networking and virtual reality tools, rather than
from the front porch. For example, while writing
this article one of the authors communicated
with three neighbors via Facebook1 to plan a
potluck dinner. This article explores the role of
virtual tools for engaging citizens in planning
processes.
The last US presidential election highlighted
the power of social networking in politics.
Social networking as a type of ‘New Media’ is
changing the political landscape. Facebook,
Myspace, Twitter, and YouTube allow indivi-
duals to become part of the larger political
process through their laptops or personal digital
assistants. This has allowed individuals to
Jennifer Evans-Cowley, PhD, AICP, Associate Professor and
Section Head of City and Regional
Planning, Austin E. Knowlton School of Architecture, The Ohio
State University, 275 W. Woodruff,
Columbus, OH 43214, USA. Email: [email protected]
Planning Practice & Research, Vol. 25, No. 3,
pp. 397–408, June 2010
ISSN 0269-7459 print/1360-0583 online/10/030397–12 ! 2010
Taylor & Francis 397
DOI: 10.1080/02697459.2010.503432

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transcend spatial boundaries of the past, creating
increased contact and accessibility in every
certain domains (Wellman & Haythornthwaite,
2002; Boase et al., 2006). For example,
hundreds of thousands of people ‘friended’
Obama and more than a million people logged
in to watch his inauguration on Facebook.
People can use social networking sites to
watch a presidential debate and ask questions
via instant messaging or to participate in real-
time polls. They can actively participate in the
process rather than just watching what is going
on. It is possible to use the sites to organize
people for politics, advocacy, or community
awareness, all of which are great possibilities for
public planning.
Because online social networking and vir-
tual reality tools allow information to spread
quickly, it is possible to grow groups to
thousands instead of holding a planning meet-
ing for a few dozen people. Citizens may not
even realize that they are engaged in a
planning process when they ‘friend’ a planning
group on Facebook, but by doing so they are
increasing awareness among their network. But
it is useful to acknowledge that the movement
of planning processes online may not add

anything that the in-person meeting for a few
dozen people accomplished. One of the aims
of this paper is to explore empirically this very
question.
Although interactions are increasingly taking
place in private spaces at the expense of public
spaces (Lofland, 1998; Putnam, 2000; Popenoe,
1985; McPherson et al., 2006), there are now
new virtual three-dimensional (3D) public
spaces available to potentially foster a different
type of civic engagement. Among these, the
most popular are the platforms created by
massively multi-player online games (MMOGs)
like Second Life. In Second Life, anyone with a
high-speed Internet connection can log-in and
join others in a virtual environment that could be
a model of their town square, Golden Gate Park,
or the Champs Elysees. Because these MMOGs
are configured in three dimensions, this Web 2.0
technology allows ordinary citizens to under-
stand and interact with virtual models of the
built environment in uniquely 21st-century ways
(Beamish 2004; Hollander 2007; Kelton, 2007;
Hollander & Thomas, 2009).
Young people’s idealism and social network-
ing and gaming environment savvy makes them
an attractive target for planners. Facebook has
more than 60 million active users, MySpace has
more than 110 million active users, YouTube
has approximately 60 million users, and Second
Life has approximately 15 million users
(Owyang, 2008). Users for each application are
spread across the globe, but each is vast in its
own right. If Facebook was a country, it would

be the 24th most populous, with fewer users
than persons in the UK and more than in Italy.
Joinson’s (2008) research helps to explain why
people are so drawn to social networking sites:
keeping in touch with friends, virtual people
watching, re-acquiring lost contacts, and simple
communication were identified as the most
popular motives for 137 Facebook users who
completed an online survey.
This study examines the engagement of cities
and their citizens in planning processes with
social networking and virtual reality tools. It
begins with a review of literature on public
participation. This is followed by case studies on
the use of Facebook and Second Life in the
USA. The article concludes with recommenda-
tions for planners about how they can both
effectively capture the energy of online planning
activists and convert it into traditional mediums
of participation and capture planners’ energy
and convert it to online mediums of participa-
tion.
Citizen Participation and Planning
Catells (1996) describes neighborhoods as the
nexus of the ‘space of places.’ Neighborhoods
host the home and public streets and commu-
nity. Participation at the neighborhood level has
decreased with the rise of the networked society
(Guest & Wierzbicki, 1999; Putnam, 2000).
However, information and communications
technologies, as part of everyday life, can lead
to increased interaction in neighborhoods
(Hampton, 2007). People can put up their

microphones (Facebook group) or avatars (Sec-
ond Life) and share with their friends and
neighbors what is happening in their commu-
nities (Hampton, 2007). These online exchanges
may lead to offline contact (Hampton &
Wellman, 2003). One study found that local
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governments are increasingly designing their
websites from the standpoint of ‘what does the
citizen need to know’ versus ‘what information
do we have to provide,’ offering the potential for
better engagement (Conroy & Evans-Cowley,
2005). However, very few municipal websites
facilitate online public dialogue or consultation
(Scott, 2006).
Some of the research on citizen participation
focuses on why it should be incorporated into
planning (Day, 1997). Often-cited benefits of
participation include increasing the education
and awareness levels of the citizenry, civic
engagement, government responsiveness, and
citizens’ commitment to implementation (for
example, Arnstein, 1969; Berry et al., 1993).
Participation research provides evidence that

when people are involved in decision-making
processes, they are more likely to support the
implementation of related policies and projects
(Potapchuk, 1996). Participation helps to build
social capital in a community, which in turn
strengthens the community (Potapchuk &
Crocker Jr, 1999). Government entities also
note less hostility from citizens and better
policies from the partnership when there is a
high level of participation (Berry et al., 1993).
Warriner et al. claim that ‘public participation
has become formalized, institutionalized, and
some would claim, sterilized’ (1996, p. 254).
Not all participation opportunities are equal,
however. The literature includes an assessment
of the level at which ‘the citizens who choose to
participate have the opportunity to determine the
final policy outcome by means of the participa-
tion process’ (Berry et al., 1993, p. 55). As first
elucidated by Arnstein (1969), in essence,
participation assessment reflects the level of
control afforded participants, ranging from
information-based or feedback-only options to
interactive participant self-determination. The
highest level of participation opportunities hold
that all citizens must be equally empowered and
fully informed to ensure that they can exert
influence on decisions that affect them (Innes,
1996). These opportunities ensure that govern-
ment decisions are justifiable to each participant,
regardless of their social, cultural, and economic
circumstances. Programs that lack depth lead to
‘token participation.’ They tend to emphasize
simple, one-way forms of communication that

merely educate citizens to accept decisions that
have already been made.
Institutionalization of the participation pro-
cess, as found in traditional public meetings,
limits the time and extent to which an individual
can learn about a complex public issue. As a
result, participation in this environment has
become less meaningful and less effective than
it could be as either an information exchange or
a learning venue. As a result, planners are
increasingly looking for new techniques to best
engage citizens in planning, including online
techniques.
While new methods of public participation
develop, it can be a challenge to imbed these
new methods into old institutions (Innes, 2005).
For example, citizens can participate in social
networking to voice their opinions, but the
public hearing is the legal instrument of
decision-making. If citizens do not provide
letters in writing or appear at a public hearing,
their opinion may not be heard.
The citizen participation literature includes
examples of technological methods that have
been incorporated to improve the participation
process. Generally, Weber et al. find that
‘participation on the Internet exerts a positive
influence on political participation, even inde-
pendent of civic participation’ (2003, p. 39).
Conroy and Gordon (2004) found that technol-
ogy-based approaches to public meetings can
lead to greater knowledge, commitment, and
satisfaction levels than traditional public meet-

ings. However, where planners attempt to
provide more depth to the participatory process,
this can backfire. One issue is the degree of
acceptance of the validity of the citizen interface
with the technology—Padgett noted that techni-
cally savvy citizens may welcome a GIS tool,
but ‘those less knowledgeable will be difficult to
convince’ (Padgett, 1993, p. 516). In a Califor-
nia city, Innes (2005) found citizens accused the
planners of using more sophisticated methods to
manipulate them. In part, the rate of utilization
and willingness to accept new methods may be
based on the demographics of a community
(Conroy & Evans-Cowley, 2006). In some
communities, there is a growing expectation on
the part of citizens that there will be online
participation opportunities (Evans-Cowley &
Conroy, 2006).
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Simons notes that ‘the degree to which
information is truly usable by the public, and
consequently, the degree to which participation is
possible, may well be a result of how that
information was presented’ (1987, p. 9). An
active participatory environment that uses Internet

technology has the potential to engage the public,
and may therefore facilitate knowledge retention
and use by the public. Simons noted further that
visually prominent and summary-based informa-
tion, such as maps and pictures, are participants’
preferred forms of information. Visual informa-
tion, therefore, can become a common language
for participants (Al-Kodmany, 1999). In addition,
a virtual 3D environment can be of critical
importance in physical planning processes,
providing a space for participants to interact
with each other and to gain new insights into
proposed new development or urban design
guidelines. Nitsche (2009) charted new theore-
tical territory by arguing that virtual spaces in
MMOGs and video games provide new avenues
for comprehending physical environments—po-
tentially adding further evidence to how trans-
formative such applications may be for public
participation in planning.
Research in the area of online citizen participa-
tion highlights the promise of the collection of
information and technology tools to enhance the
public participation experience. Furthermore, the
planning process provides a forum for realizing
the ideals of participatory democracy as a means
for democratic discourse (Innes, 1996).
Results of Social Networking
With the rise of social networking sites, a first
step of identifying social networking groups
focusing on planning issues in the USA was
undertaken. To conduct this exploratory re-
search, keyword searches were used, including

the terms comprehensive plan, neighborhood
planning, master plan, community plan, devel-
opment plan, zoning, no development, stop
development, and new development. Addition-
ally, groups were searched, including Geogra-
phy—Cities, Geography—Neighborhood, and
Organizations—Community Organizations, all
followed by a search term. The social network-
ing sites BlackPlanet, Facebook, and MySpace
were searched using the above method.
As the searches revealed appropriate groups,
each was documented by noting the social
networking site, whether it was citizen or govern-
ment initiated the name of the group, its location,
the number of members, and the extent of the use
of the available features on the social networking
site. The searches resulted in 42 different social
networking groups related to planning, primarily
on Facebook. This represented all English lan-
guage groups that were discovered using the listed
keyword search method. The public-initiated
groups were typically focused on a neighbor-
hood-level or site-level planning issue and tended
to be opposed to the project, while the govern-
ment-initiated groups tended to focus at a
neighborhood, community or regional scale
around a particular planning process, such as
creating a comprehensive plan.
After the 42 different social networking
groups were identified, each of the adminis-
trators for the groups was contacted and asked to
complete a survey regarding their group. The
survey was sent via Facebook or by email where
an email address was provided on the Facebook

group. The survey invited the group adminis-
trators to complete a survey about their group
and its engagement in the planning process.
Administrators were asked a series of questions
about their group, including: Why did you
decide to start a group, when did the group
start, and when does group expect to end? Was
the group started by you individually, for a
client, or for an organization? How many other
groups have you started? How have you
advertised the group, was usage tracked, and
how many people did you hope to have join the
group? What features of the social networking
site were used in the group? Respondents were
also asked a series of questions comparing
traditional participation formats to the social
network, including information received, useful-
ness of input, knowledge level of participants,
and whether the social network participants were
new participants. Open-ended questions focused
on the administrators’ expectations of involve-
ment in the planning process and the degree of
participation at on-site meetings.
Eleven group administrators responded
(26%). In addition, for each citizen-initiated
group, the local government planning depart-
ment was contacted to determine the extent to
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which the group has influenced the planning
process. The information collected was utilized
to better understand the typical uses of Facebook
for planning. After examining the 11 groups
where survey responses were provided, two case
studies were selected to illustrate the use of
Facebook—the first a government-initiated
group and the second a citizen-initiated group.
These cases studies were selected as best case
examples of how governments and the public
are using Facebook.
Government-initiated Social Network—Aspen,
Colorado
The vast majority of social networking sites
focusing on planning issues were citizen in-
itiated (86%), while six were initiated by local
governments for a planning process. There are
interesting differences between the two types of
groups. The government-initiated sites had, on
average, just 29 ‘friends,’ while the citizen-
initiated sites had an average of 297 ‘friends.’
The City of Aspen, Colorado initiated a Face-
book group in October 2008 as part of its Aspen
Area Community Plan update. The City decided
to use Facebook for the first time to try to get the
word about its community plan update out to a
younger demographic, including high school
students and young professionals. The City used
their website and word of mouth to try to attract
‘friends.’ City staff also went to the high school

to collect input and encourage these students to
join the Facebook group and participate on the
City’s blog. The use of Facebook has attracted a
limited number of people—52 including staff
members—but this met the goal of making 50
‘friends.’ To put this in perspective, the City/
County is hosting large public meetings as part
of the process, and the goal is to have more than
1, 000 people attend these meetings.
The Aspen Facebook group has been used to
post information about upcoming citizen meet-
ings. Links have been provided to the City’s
plan website (http://www.aspencommunityvi-
sion.com) to allow people to register to attend
small group meetings about the plan. The group
also announced and linked to the Community
Plan blog, which started in November 2008. The
blog had generated 59 comments in 3 months.
The City has utilized a limited number of
features, primarily focusing on using the group
to notify citizens of upcoming events. Effec-
tively, the City has used Facebook as just one
piece of a larger citizen participation process.
For example, the City offered a ‘Meeting-in-a-
Box’ kit that allowed citizens to host their own
meetings. The kit included 10 colored cards on
10 topics, a document on how to build your
vision, pens and notepads, and popcorn.
The Aspen Facebook group has had limited
success. The City did not expect to get a great
deal of participation on Facebook, given that
Aspen’s average age is almost 50, but staff
reported that they have been pleasantly surprised

by the amount of interest and involvement. The
Facebook group has helped to let people know
about upcoming events and to share information,
but it has not been effective in gaining input.
The City reports that they received much more
information from traditional input than from the
Facebook group. The City has been effective in
convincing almost all of the members participat-
ing online to also participate in the traditional
public process. The City believes that traditional
input is somewhat more useful than online input
resources, and that traditional participants are
more knowledgeable and engaged than online
participants. However, the City did report that
the online participants are largely new, rather
than the same people who regularly participate
in onsite events, and that online tools have
allowed them to reach individuals who might
otherwise not participate. Although the use of
the online participation tools allowed the City to
enhance the participation experience for their
citizens, it believes that online participation tools
are most useful as a supplement to traditional
participation approaches.
Citizen-initiated Social Network—Austin, Texas
Citizen-initiated groups dominate in social
networking sites focusing on planning issues.
Most citizen-initiated groups are organized to
oppose a development proposal or plan (80%).
These groups are often able to attract hundreds
of ‘friends’ to join their cause in opposing a
project. For example, more than 400 people
joined a Facebook group called ‘No F*cking
Walmart in Canfield.’ However, these large

groups have not been effective in converting
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http://www.aspencommunityvision.com
http://www.aspencommunityvision.com
their online opposition into participation through
formal planning processes. For example, Can-
field Township, Ohio has a modest population of
14, 000 people, and with 400 people opposed to
a project one might think that this significant
level of online participation would translate into
onsite participation. However, the township
Zoning Inspector reported that the township
staff were not aware that the Facebook group
existed and reported that they had no more
participation than usual in this particular zoning
case. This situation was entirely typical, show-
ing that online organizers were not effective in
participating in formal planning processes.
This was not the case in Austin, Texas. A
developer originally proposed to demolish the
Northcross Mall and replace it with a 217, 000-
square-foot Wal-Mart Superstore with a three-
storey parking garage and additional outbuildings.
This resulted in a significant amount of online

debate. In Austin, three Facebook groups were
created in opposition to development in the
Northcross area of the city: ‘Austin Against
Walmart’ (72 members), ‘Walmart isn’t weird’
(43 members), and ‘Those Against Building a
Wal-Mart Where Our Beloved Northcross Mall
Stands’ (291 members). There is also a competing
group, ‘who cares about northcross mall?? Build
the freakin walmart already!!,’ with 23 members.
On the pro-development side, ‘who cares
about northcross mall?? Build the freakin
walmart already!!’ is made up of primarily
college students. A wall posting from a group
member presents the general idea of the group:
Yeah, I’m soooo sad all the mom
and pop stores like Bealls, Osh-
man’s, Ritz Camera, Guitar Center,
Sunglass Hut, Claire’s and Hall-
mark Gold Crown will go out of
business . . . boooo f*cking hooo . . .
bring on the wal . . . have you guys
heard about the proposed wal-mart,
it will be one for the ages, so much
better than any we have so far, with
like a sushi bar and all this sweet
new-age stuff . . . can’t wait.
The group ‘Walmart isn’t weird’ argue that
Austin is known for its uniqueness and that
citizens must ‘preserve the color of our city, and
turn our backs to those who threaten to bring us
down to dull, grey, conformity.’ The members
of this group are primarily high school students.

The group ‘Those Against Building a Wal-
Mart Where Our Beloved Northcross Mall
Stands’ is made up of a mix of people, from
high school students to adults. This group is an
off-shoot of a larger organization called ‘Re-
sponsible Growth for Northcross’ (RG4N), a
neighborhood group that represents people who
live or own businesses in the Northcross Mall
area (http://www.rg4n.org). This Facebook
group encouraged people to sign a petition
opposing the proposed Wal-Mart in their
neighborhood and to visit a website about the
initiative. The group provided informational
links, an active discussion board, wall posts,
and a photograph of the mall.
The group ‘Austin Against Wal-Mart’ is the
most sophisticated of the groups. It has an
affiliation with the RG4N organization. The
administrator started the group a month after
Walmart announced its plans to move the super-
center into the Northcross neighborhood because
she wanted to get students involved in city
planning. She is an experienced group user,
having created more than five Facebook groups
in the past. She got the word out by inviting her
existing Facebook friends to join the group and
by letting the ‘City of Austin Against Walmart’
group know about her group. However, only 72
people from this group joined ‘Against Austin
Wal-Mart’ group. The group anticipates that
their target audience was either not aware of or
familiar with Facebook, but the administrator
anticipates that usership would increase in larger
urban settings with a higher number of young

working professionals.
The administrator of the ‘Against Austin Wal-
Mart’ group primarily wanted to get information
out to people about what was going on with the
development project. Her Facebook group
provided a link in English and Spanish to a
survey about the redevelopment plan, a link to
the RG4N website, and a link about telling the
city council to suspend the site plan for the
proposed Wal-Mart. The administrator believes
that citizens receive more information from
traditional input than from online input, but
she thinks that the usefulness of the input is the
same regardless of whether it is online or
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http://www.rg4n.org
traditional. She also believes that traditional
participants are more knowledgeable and more
engaged than online participants. She was able
to generate new participants with her Facebook
group, but some of the same people who were
online had also attended past traditional meeting
formats. However, she does believe that she was
able to reach people through Facebook that

otherwise would not have participated. She
strongly disagrees that online tools are all that
is needed to do community outreach, but she
believes that online tools enhance the participa-
tion experience for citizens. Also, she believes
that they are best used as a supplement to
traditional participation tools.
The City of Austin reports that it was not
aware of the Wal-Mart Facebook groups, but it
was familiar with the RG4N group. The
responding staff member spoke with RG4N on
a regular basis. The Northcross Mall develop-
ment project had significantly higher levels of
participation than other projects. The City
believes that social networking participants are
probably less knowledgeable about planning
issues based on a review of the Facebook
groups. The City staff member was not surprised
to learn that there were Facebook groups related
to the project. He reported that RG4N used a
slew of methods to raise awareness, including
massive telephone banking, petition drives at
local retail stores and theaters, and protests. The
staff member was not aware of any Facebook
group that has had a particular impact on policy
matters in Austin. He points out that Austin has
always had a fairly active citizenry, even prior to
the implementation of most technologies (Le-
vinski, 2008).
While the City may not have been aware of
any of the Facebook groups, the significant
impact of RG4N did influence the planning
process. Facebook served as a small piece of an
overall outreach strategy. The City initially

approved an original site plan, but the developer
pursued a second site plan in 2007. In June
2008, the City of Austin approved a second site
plan for Northcross Mall. Later that month,
RG4N filed a lawsuit to invalidate the site plan.
In August, the developer came back with a third
revised proposal for a 99, 000-square-foot Wal-
Mart with one storey and no parking garage or
gas station. The plan includes enhanced design,
with additional landscaping and the addition of
more sidewalks. After meeting with RG4N, the
developer went to the City to pursue approval of
the new site plan. In December 2008, the Austin
City Council passed a big box ordinance. In
December, construction on the site was volunta-
rily suspended for 60 days to gather input from
surrounding neighborhoods. At the date of this
writing, no final proposal had been approved.
The Austin Chronicle declared RG4N as the
‘Best of Austin,’ noting that ‘The heroes of
RG4N are standing up not just for their own
neighborhood but for a whole city’ (RG4N,
2008).
Results of Second Life
To examine how Second Life aids or detracts
from public participation in planning, we
examined two examples in the Boston, Massa-
chusetts metropolitan area. For each example,
we present some background information on
why Second Life was utilized, describe its use,
and present our analysis of how Second Life
impacted on the public process undertaken.2

Hub2—Boston, Massachusetts
In 2007, faculty and students at Emerson
College, a small private college in Boston,
developed a partnership with the City of Boston
to experiment with using Second Life to engage
citizens in urban planning (Gordon & Koo,
2008). The project, dubbed Hub2, was intended
to support technological innovation in govern-
ance and fit the interests of media studies
students at Emerson. Specifically, the City of
Boston sought to use Second Life ‘to enable
local neighborhoods to participate more mean-
ingfully in the design and development of their
own public spaces’ (Hub2, 2009). The project
directors developed a guided curriculum that
was provided to a small group of community
activists and city employees in small in-person
workshop settings.
Using a framework they developed called
IDEA (Imagine, Design, Engage, and Activate),
the project directors facilitated community
members through a four-step process to explore,
understand, and contribute to the design of a
variety of spaces throughout the city (Gordon &
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Koo, 2008). For each space, participants first
went to the real-world location and imagined the
possibilities of the space through a direct
observation research approach. Next, they
imagined the space within Second Life through
a virtual exploration. Following that, each
participant re-designed the space based on their
imagining exercises and engaged with other
avatars in the space to test the space’s usability
and esthetic qualities. The designs were primar-
ily of city parks and other open space areas
where participants manipulated street furniture
and plantings, and then registered their approval
or disapproval of others’ work with the place-
ment of flags. Lastly, participants activated their
spaces by bringing their work back into the real
world through videos, posters, and, finally, an
oral presentation to the Mayor’s office.
The Hub2 project was not a sustained, open
public planning process, but an experiment in
mixing virtual and real interactions when
considering city spaces. While much can be
learned from such an endeavor, since it lacked
any actual public planning objectives its success
is hard to assess. Rather, we argue that the Hub2
experiment illustrates the potential to invent new
forms of public interaction with urban design
and planning. In many ways, the Hub2 team
created new rules in their IDEA framework
simply because the traditional rules for public
participation in planning have little relevance
when asynchronous interaction in 3D computer-
generated spaces is possible in real time.

Master Planning in Acton, Massachusetts
The second example of the use of Second Life in
a planning process was initiated by faculty and
students at Tufts University, a large private
research university, in concert with local
officials in the Town of Acton, a 20, 000-person
growing suburban community 20 miles west of
Boston. Acton has experienced growth rates of
between 5% and 10% every decade since 1970
and has been ineffective at managing the influx
of new residents (US Census, 2000). In 2007,
the town commenced a new master planning
process aimed at rethinking growth management
and developing physical planning and design
strategies to address new growth. In the interest
of trying to make this plan most comprehensible
to a lay audience, local officials sought out a tool
to aid them in computer visualization of various
growth scenarios. The Tufts-Acton team to-
gether agreed to use Second Life in an
experimental way to accomplish these visualiza-
tion goals.
The team began by selecting a key neighbor-
hood in Acton called Kelley’s Corner, which has
developed at a low to medium density and could
experience greater development in future years.
The site is also a major gateway to the town and
is located within walking distance of the town’s
only high school. The Tufts team developed a
3D model of Kelley’s Corner in Second Life
and, working closely with Acton officials,
designed a series of mechanisms to allow local

residents to engage both synchronously and
asynchronously with the virtual environment.
This effort is ongoing and we can only report
preliminary findings. Just as in the Hub2 project,
technical difficulties and the need to control
access to only local residents limited the team’s
expansion of the project to off-site users.
Computer terminals loaded with Second Life
were installed in public libraries and at a series
of workshop locations, but few users logged in
remotely due to technical hurdles. This limita-
tion severely restricted the number of users who
were able to participate in the project, essentially
invalidating one of the key rationales behind the
project. In total, approximately 75 Acton
residents logged onto the virtual space and
participated in the planning exercise. For those
participants who connected to the site, informal
observations by one of the authors suggest that
their experiences were largely meaningful and
provocative. Participants who were involved in
the Kelley’s Corner location found ample
opportunities to interact with others in the
virtual space and provide feedback to the
Tufts-Acton team about ways the neighborhood
could increase in density.
Discussion
Blum (2007) argues that social networking sites
have the opportunity to feed off neighborhoods
and vice versa in order to more fully connect
neighbors. He argues that social networking can
offer the social benefits and local ties of living in
a medium-density neighborhood to those living

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in high-density neighborhoods. These benefits
are hypothetical, and the extension of this
concept to connecting neighbors to their local
government is still in its infancy.
The Facebook and Second Life examples
show that social networking online works best
as part of a broader participatory process. As one
respondent to the Facebook administrator survey
stated:
Sometimes people mistake joining a
Facebook group as actual action for
a cause . . . On the phone many
people were excited that they had
joined the group, but were hesitant
about making a further commitment
to attend the [City Council] meeting.
Also, the cause and event the Face-
book group was advocating for was
very targeted and required members
to participate in a specific action
(attending the meeting) for me to
consider the group successful. How-

ever, those who joined the group
were located in many different
places. In spite of the indication of
location within the group title and
that only people who were from the
Jacksonville area were invited, most
members were college students who
were not actually residing in the area
at the time. Facebook was effective
at introducing people to a cause, but
was less effective at producing
actual support for the cause.
While Facebook is popular with millions of
users, and in some ways it appears to be an ideal
tool for engaging citizens in planning processes,
it is important to recognize how citizens view
what ‘friending’ means on Facebook. For the
moment, Facebook appears to be effective in
helping citizens gain knowledge about planning
processes and projects. That said, Facebook
should be used as one part of a larger group of
participatory techniques.
Likewise, the Hub2 project was conducted
outside a formal planning process—thus detract-
ing from the potential for it to be a unifying
social and political force. Whereby, the Acton
project was part of a larger planning process and
highlights the utility of gathering residents
together, electronically, to interact, debate, and
discuss their community’s future. In contrast to
Facebook, Second Life brings residents together
in a 3D virtual space and this offered new and
novel ways for planners to engage with

residents.
One reason for the slow adoption of social
networking sites by local governments may be
because of their limitations on the ability of their
employees to use Facebook or Second Life. For
example, while Toronto has numerous citizen-
initiated Facebook planning groups, employees
of the city are banned from using Facebook.
However, the City Councilors are still permitted
to use Facebook so they can use it as a com-
munications tool with constituents (Gray, 2007).
One Borough in London, England has done the
same thing, banning two-thirds of its employees
from using Facebook (Malkin, 2007). Many
cities might also ban the use of virtual environ-
ments, such as Second Life, in the workplace,
viewing them as games rather than work.
While the Facebook example shows how
useful remote participation can be, the Second
Life examples illustrate just how high the
technological hurdles are for mass participation
remotely. In Acton, a town of 20, 000 residents,
extensive outreach by university and town
officials could only attract less than 100
participants. The power of the web-based
applications described here is that people can
be involved remotely, yet the challenges of
using high-tech software can be a challenge to
implementation.
Conclusion
Public participation has been a hallmark of the
planning process for over 30 years, with each

generation trying to improve access and inter-
activity to ordinary citizens. However, as we
launch into the second decade of the 21st
century, the increasing pervasiveness of high-
speed Internet access and the proliferation of
social networking and MMOG tools have meant
that new forms and processes of public partici-
pation can truly change the way planning works.
As the cases in this paper illustrate, this
newfound power to democratize planning pro-
cesses is not absolute and must be approached
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with caution. Our findings suggest that the
Internet may be more useful for some forms of
public participation than others. The case studies
on Facebook illustrated the need to connect in-
person and online participation to effect change.
In some cases, the kind of hybrid, in-person and
online experiences experimented with on Sec-
ond Life could effectively complement the kind
of ongoing connections and ‘friendships’ sus-
tained through Facebook. Such new and creative
participation strategies could ameliorate the
potential harmful effects of an increasing digital

divide. As the poorest members of society are in
some cases shut out of high-speed web access,
their presence in regular, ongoing, online social
networking will only be limited. At the same
time, the ability of projects like Hub2 and the
Tufts-Acton initiative provide physical, place-
based forums where the most digitally disen-
franchised can access web technologies and
become involved in an unprecedented way.
We started this article discussing the potential
of information and communications technolo-
gies in planning. This research has found that
there is limited use of these new technologies to
engage in planning. In part this may be a result
of the technology being new, or in part because
planners have not yet learned how to effectively
use this technology in planning processes. As
the public popularity of social networking and
MMOGs increases, planners should be prepared
to engage the public in mediums that the public
is already using. The potential is still there,
but planners need to act to embrace these
technologies and learn to be effective in using
them. This is why Devisch (2008) argues for
planners to use tools like Second Life as part of
their engagement toolkit.
We encourage other scholars to pursue further
research on the so-called ‘digital divide’ be-
tween those with and without high-speed
Internet access and to explore the implications
of the divide for just planning processes.
Researchers have argued that inclusiveness is
critical to a truly participatory planning process
(Baker et al., 2007; Brownhill & Carpenter,

2007). We also suggest more research on the
basic effectiveness and efficiency of these new
Internet-based tools to generate meaningful
public engagement in planning processes—we
need to have stronger empirical evidence of
what really works, what does not, who is
included, who is left behind, and how the future
of planning will grapple with the persistent pro-
blems of unequal power relations both online and
offline. Kitchen and Whitney (2004) found that
planners are eager to find solutions and work on
issues of equality in access and to find ways to
engage with hard-to-reach groups. It is with opti-
mism that we see technology playing a role and
one more opportunity to engage with the public.
Notes
1. Facebook is a free social networking website that
allows users to add friends, send them messages,
post updates about themselves, share photographs,
links and videos, and participate in groups.
2. The methods employed here consist of participant
observation, interviews, and review of published
and unpublished documents from June 2008
through November 2009 for the two Second Life
projects described below.
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Problem, research strategy, and fi nd-
ings: How media portray public transit
services can affect the way voters and
stakeholders think about future transit
investments. In this study, I examine social
media content about public transit from a
large sample of Twitter comments, fi nding
that they refl ect more negative sentiments
about public transit than do the comments

about most other public services, and
include more negative material about transit
patrons. However, transit agencies may be
able to infl uence the tone of those com-
ments through the way they engage with
social media. Transit agencies that respond
directly to questions, concerns, and com-
ments of other social media users, as
opposed to merely “blasting” announce-
ments, have more positive statements about
all aspects of services and fewer slurs
directed at patrons, independent of actual
service quality. The interaction does not
have to be customer oriented. Agencies
using Twitter to chat with users about their
experiences or new service also have statisti-
cally signifi cantly more positive sentiments
expressed about them on social media. This
study’s limitations are that it covers only one
social media outlet, does not cover all transit
agencies, and cannot fully control for
differences in transit agency service.
Takeaway for practice: Planners
committed to a stronger role for public
transit in developing sustainable and equita-
ble cities have a stake in the social media
strategy of public transit agencies; moreover,
they should not let racial and sexist slurs
about patrons dominate feeds. Planners
should encourage interactive social media
strategies. Even agencies that only tweet
Planning and Social
Media
A Case Study of Public Transit and Stigma on

Twitter
Lisa Schweitzer
H
ow individuals, newspapers, and television portray public
services
can affect the way voters and stakeholders think about planning
and
public services. Attitudes and beliefs can infl uence politicians
mak-
ing budgetary and other decisions such as public transit
investment. Word of
mouth can also affect whether consumers choose a particular
service (Aday,
2010; Ajzen & Fishbein, 2005). With social media, individuals
as well as
traditional news media, businesses, and other institutions create
the content
that people view. I examine one social media outlet, Twitter, to
see what social
media users say about public transit and whether transit
planners and agencies
can infl uence the content on social media.
This study asks and answers the following questions:
• Do social media users describe transit planning, management,
and ser-
vices in a positive or negative manner?
• Do differences in social media interactions infl uence the tone
of the
discussion surrounding agency services, planning, and public
manage-

ment on social media?
• If so, should planners get more actively involved in social
media to foster
positive messages about public transit services and maintain
civil dialog
about patrons?
I fi rst discuss media effects and their potential infl uences on
sentiments,
attitudes, and choice behaviors. The third section of the study
describes my
use of a machine-learning algorithm; I show that social media
content about
transit, in a large sample of nearly 64,000 comments made on
Twitter, refl ects
more negative sentiment than comparable content about parks
and airlines.
interactively a few times a day seem to have
more civil discussions surrounding their
agencies and announcements on Twitter
than agencies that use their feed only to
blast service announcements.
Keywords: public transit, social media,
racism, sexism, planning ethics
About the author: Lisa Schweitzer
([email protected]) is an associate
218
professor at the Sol Price School of Public
Policy, University of Southern California.
She blogs at www.lisaschweitzer.com and
is on Twitter as @drschweitzer.

Journal of the American Planning Association,
Vol. 80, No. 3, Summer 2014
DOI 10.1080/01944363.2014.980439
© American Planning Association, Chicago, IL.
RJPA_A_980439.indd 218RJPA_A_980439.indd 218
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Schweitzer: Planning and Social Media 219
Sentiment surrounding public transit appears similar to
that about police departments and social welfare programs.
I also fi nd that the negative commentary on Twitter in-
cludes more negative material about transit patrons than

that of other public or private services. Comments about
race, class, gender, and other markers of discrimination
occur more frequently in the social media content on
transit than they do for any of the other services examined,
including social welfare programs. In fact, if racial, ageist,
and other slurs against patrons are removed from the
tweets, airlines and public transit agencies have statistically
indistinguishable sentiment scores on social media. Transit
advocates have long maintained that transit is viewed with
a social stigma, and the evidence from social media sug-
gests that advocates may be right.
The fourth section explores whether the ways in which
transit agencies communicate via social media might infl u-
ence the tone or sentiment of comments made by other
social media users. By hand coding 5,000 randomly selected
tweets, I test whether transit agencies that engage in more
interactive dialog with other Twitter users have more posi-
tive statements than those agencies that simply “blast” out
service and related announcements. In this study’s penulti-
mate section, I show that the answer is, tentatively, yes:
Agencies with more interaction between transit agency
representatives and other social media users have statistically
signifi cantly more positive statements about all aspects of
service—and fewer racist and sexist comments—than those
agencies that simply blast information. Even using Twitter
just for customer service is correlated with more positive
feelings about the agency on social media.
Overall, I fi nd that how transit agencies use social
media seems to infl uence user response and affects demo-
cratic engagement with public transit support and invest-
ment. If planners seek to support strong public transit
systems as a key element in building equitable and sustain-
able communities, they should encourage positive public
sentiment about the service, in part by encouraging public

transit agencies to use interactive social media approaches.
This should not require extensive resources. Even agencies
who only tweet a few times a day, but who use that pres-
ence to chat with people rather than make announcements,
seem to have more civil discussions surrounding their agen-
cies and announcements on Twitter.
Media Effects, Attitudes, and Beliefs
About People, Places, and Services
The media effects research is immense because it has
accrued over six decades in communications, journalism,
and psychology. Media in Americans’ daily lives are perva-
sive. According to Potter (2012), there are 65.5 million
hours of original programming produced for radio each
year; television produces 48 million hours. There were 13.6
billion websites in Potter’s census. Given that volume and
variety of content, exposure—the time that individuals
spend engaging with media in some way—is vast. Philips
(2010) fi nds that, on average, Americans spend 11 hours
exposed to various forms of media every day, including the
Internet.
Research on media effects examines what infl uence all
these different images, words, and ideas can have on indi-
viduals (Ball-Rokeach & DeFleur, 1976; Drew & Weaver,
1990). Infl uence is defi ned as the ability that media con-
tent has to alter people’s behaviors, emotions, attitudes,
and beliefs in such a way that they become part of the way
a person processes information and makes judgments
(Scheufele, 1999). According to Potter (2012), there are
more than 10,000 published studies on media effects that
show mixed results, as with most large research literatures.
Results range from substantial to negligible effects depend-
ing on the context, media format, message, and audience.

Nonetheless, it is fair to say that there is a consensus in the
communications research that under the right conditions,
media does infl uence what people think about and, in
turn, how they evaluate the things media brings to their
attention (Gunther, 1991).
Online services and social media represent a compara-
tively new arena for public agencies seeking to infl uence
how people think about public services such as transit.
Many public transit providers have already moved into
social media for various management and communica-
tions activities, such as service announcements and cus-
tomer service (Bregman, 2012; Collins, Hasan, & Ukku-
suri, 2012; Mai & Hranac, 2013; Moss & Kaufman,
2013). Social media, however, present a challenge for
communicating specifi c messages and values. Unlike
advertising, where transit agencies can craft their own
images and messages to disseminate, social media content
comes from many users. Understanding that content can
help us in turn to understand what people say, both good
and bad, about urban issues and public services such as
transit. We can then use that understanding to employ
social media more effectively in planning.
Table 1 displays Twitter content results from a simple
name search on SEPTA, the Southeastern Pennsylvania
Transportation Authority serving the Philadelphia region. I
selected this particular group of tweets because it displays
the issues in play here. Pronouncements from users about
how SEPTA generally runs late are interspersed with SEP-
TA’s announcements about particular service problems. The
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220 Journal of the American Planning Association, Summer
2014, Vol. 80, No. 3
content returned in this search refl ects three dimensions of
media content that can infl uence attitudes and beliefs: the
message a) highlights problems and is thus framed nega-
tively; b) is consistent; and c) comes from multiple sources
that echo the same message: Service is late. Taken together,
that consistent message about late service may not be good
marketing for the service, the agency’s public management,
or their plans. I do not use the agency’s own content in the
analyses of what users say (described below), but I show it
here to demonstrate how agencies contribute to the negative
content with their own messaging.
What messages emerge from all this disparate con-
tent? Most prior research on online content suggests that
negative comments dominate just about all online mate-

rial (Hu & Liu, 2004). Complaining online, from overly
entitled Yelp customers to misogynist 4chan users, is to
some degree expected in online commentary. Complain-
ing about transit service is hardly novel or unexpected
among those who use the service every day. People can be
unreasonable in what they say about all types of service,
transit included (Weinstein, 2000). They can also be right
in that service can and should be better. Since complain-
ing online appears to be the norm, just fi nding com-
plaints demonstrates little about the overall content. The
question is whether, relative to other topics, transit or
other public services contain disproportionately more
negative content, or particularly vivid negative images
and content, that might lead us to believe complaining
about transit is somehow worse or different than it is for
other public services.
Methods: Machine Learning and Text
Mining
I use data I collected from the social media site Twitter
(https://twitter.com/) to analyze whether comments about
transit service are more negative than comments made
other public services. Twitter is a microblogging social
media service where individuals post messages of 140
characters or less, images, and links to other content.
Twitter allows users to “follow” other users and receive
their content automatically, though anyone can view
Twitter content just by searching the site. Users can mes-
sage each other freely.
Throughout, it is important to remember that Twitter
users are not representative of wider transit patron groups,
voters, or the U.S. public. Twitter users are younger, more
affl uent, and more educated than the U.S. population as a
whole (Brenner & Smith, 2013). Nonetheless, according to

a 2013 survey, one in every 10 American adults reports
getting their news from scanning Twitter (Mitchell & Page,
2013).
In addition, Twitter users, as an audience, are only
one of many ways Twitter comments can have infl uence.
Twitter comments, unlike those on Facebook, are public
and accumulate into a searchable short-term text archive
that even those without Twitter accounts can search and
access. As a result, Twitter content may exert dispropor-
tionate infl uence on other, traditional media because
Twitter allows journalists, who are important media
content creators, to draw on Twitter content for their
stories.1 Thus, Twitter yields a sample of readily available,
readily accessed content that can cross over into other
media.
I use two related methods called machine learning and
text mining to evaluate the content of the tweets I exam-
ined. The text-mining method uses an algorithm to scan
the Twitter text to match words found in the Twitter
comments against an existing lexicon of positive and
negative opinion words. For this analysis, I take a basic
counting approach starting with an established lexicon of
positive and negative English and Spanish words developed
by computer scientists (Hu & Liu, 2004). Their dictionary
has been used extensively in prior research, and it is avail-
able via free distribution online at http://www.cs.uic.
edu/~liub/FBS/sentiment-analysis.html. The algorithm
scoring method is described via formulae in the Technical
Appendix. The programming code needed to conduct the
analysis in the open source programming language R is
available from the author.
Machine learning describes how a computer selects
information based on an algorithm that can be

Table 1. Public search results for SEPTA.
SEPTA @SEPTA Chestnut Hill West: Train #826 going to Fox
Chase is operating 12 minutes late. Last at
Chestnut Hill West.
Twitter User #1 When I say trust no one I really just mean don’t
trust septa to get you to work on time.
Bob Kelly @
bobkellytraffi c
Scattered delays on SEPTA’s Regional rails.
SEPTA @SEPTA Newark: Train #4213 going to Newark is
operating 16 minutes late. Last at Temple U.
SEPTA @SEPTA West Trenton: Train #319 going to Elwyn
Station
is operating 11 minutes late. Last at Fern Rock TC.
Twitter User #2 it don’t even be my fault why I’m late. where tf
the trolley at yo, I really hate septa.
Twitter User #3 A SEPTA bus before 6 am is the weirdest. Feel
like I’m going on a fi eld trip with a group of
senior citizens.
Note: Agency tweets are included here for display, but they are
not
included in the analyses done throughout this manuscript.
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programmed to learn, either organically from the data or
from a human that provides feedback to the algorithm
iteratively. For example, the spam fi lter for email works by
machine learning. Here, I combine machine learning to
improve the text-mining methods. I wrote a second algo-
rithm that examined frequently occurring text strings to do
two things: 1) identify tweeters who may be outliers in
terms of the amount they tweet; and 2) add words to the
lexicon that might be unique to the conversations about
transit. I used an interactive machine-learning method so
when the algorithm detected an unusually frequent text
string, it sought my input as to whether to mark that string
or not. Locating frequent tweeters can help pinpoint satire
or parody accounts that exist to mock or denigrate transit
agencies: Those do exist. Other frequent text strings are

words likely relevant to transit service but not included in
the original lexicon.
The resulting additions included four positive words:
congrats, congratulations, thanks, and upgrade. The interac-
tive machine again yielded far more negative words than
positive words, and these, too, went into to Hu and Liu’s
(2004) original listing in the sentiment scoring: brokedown,
wtf, late, wait, waiting, delay, delayed, offl ine, scam, closed,
epicfail, weirdo, pervy, crowd, crowded, jammed, skeevy,
breakdown, ghetto, transitfail, and unsuck. The modifi ed
lexicon then replaced the original lexicon used to score
opinions found in the Twitter content.
I used the text-mining algorithm to score the text
strings for opinions based on the lexicon derived from
Hu and Liu (2004). The algorithm gives a zero score to
content that has no positive or negative words, or that
offsets positive and negative words within the text (Pang
& Lee, 2008). Otherwise, the algorithm marks words as
positive or negative and then sums those scores for the
total content. “I love the metro,” for example, would be
coded 1. Conversely, “I hate the metro” would be coded
–1. “The metro is late” would be scored –1, whereas
“Great ride, Metro” would be scored 1. The algorithm
would score something like “Super awesome great ride”
a 3.
Machine coding is imperfect, so slang, misspellings,
and repetitions pose a challenge. It is not clear that “I lurve
Tri-Met!” is more or less positive than “I love Tri-Met” or
“I love love love Tri-Met!” Most opinion-mining methods,
like this one, just count positive and negative words; there-
fore, the last tweet would be counted as more positive than
the fi rst tweet. The system also does not handle sarcasm
particularly well. However, machine coding draws on such

a large body of text information that these stylistic quirks
and errors become less important than what emerges from
the large volume of data as empirical regularities.
Sampling
I used a two-part sampling strategy. The fi rst sample
selects a set of controls from two groups likely to have
extremely positive comments (“celebrities”) and extremely
negative comments (“villains”). Because text mining in-
volves thousands of text strings, we need the celebrity and
villain controls to see whether the opinion-scoring algo-
rithm properly recognizes words and then scores them the
right way for positive and negative content. If the algo-
rithm performs properly, opinion scores for celebrities
should all group together and be strongly positive, while
the opposite should be true of the villains.
The second sampling strategy compares transit against
other public and private services as test groups. Table 2 lists
all the agencies and terms used in the test groups. The
transit agencies in the test group were randomly sampled
from the American Public Transportation Association’s list
of top 30 passenger-serving transit companies.2 Airlines
serve as a control group because, though airlines are private
companies, they have multiple service parallels with public
transit. Air travel is, like transit, a ticketed service where
individuals might comment on service timing, staff con-
duct, ticketing, facilities, and other passengers. In addition,
I sampled content about urban public parks to capture
discussion about public space as an overlap with transit as a
public venue. I also randomly selected police departments
as a control, acting as another local government institution.
My fi nal control group is social welfare programs. The
latter are likely to carry a stigma within U.S. public and
political dialogs. I sampled content in winter, summer, fall,

and spring months from 2010 until 2014, as the com-
plaints related to parks and transit are seasonal. The fi nal
sample contains 63,321 comments. More specifi c informa-
tion on the machine coding used in this study appears in
the Technical Appendix.
Figure 1 shows the average sentiment score found for all
the content sampled from Twitter for each agency or person
in the sample. To make the fi gure clearer, I display only fi ve
indicators for the controls: the top three and bottom two of
each category. The dashed line in the center of the fi gure
shows the 0 score; the thicker horizontal line indicates where
average scores switch from positive to negative. Zero scores
indicate no real sentiment. Positive scores indicate positive
sentiment; negative scores refl ect entities where the content
runs, on average, negative. All tweets from all offi cial agency
Twitter accounts were removed from these data. Figure 1
suggests that the machine-coding algorithm worked properly
because it produces scores that matched my expectations for
the likely extremes. That is, in general, celebrities were at the
positive end of the distribution (William Shatner and the
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2014, Vol. 80, No. 3
Kardashians), whereas villains mark the other end of the
scoring distribution. A random sample of tweets checked by
hand also verifi ed that the algorithm scored the material
properly, so that the differences seen here refl ect differences
in the data, not coding problems.
The second set of controls involved airlines and other
public services (the police, social welfare programs, and
parks); they group together in ways that suggest that the
machine learning discovered differences in positive and
negative statements about these services. In general, people
tweet very positive things about parks. The picture is less
clear about airlines, however; Alaska Airlines and South-
west have positive sentiment scores, near those of celebri-
ties and parks. US Airways and United, however, have
negative mean sentiment scores. The airline rankings
mirror those found in more in-depth customer satisfaction
surveys reported by JD Powers and Associates, with Alaska
Airlines leading among traditional airlines and Southwest
scoring well among small regional services.3
I found signifi cant differences in the opinions ex-
pressed about these control groups in the Twitter commen-
tary. The details of the analysis are in Table A-1 in the
Technical Appendix. The results show that the sentiment

expressed about public transit on Twitter is more negative
than for parks and airlines and indistinguishable from
police and social welfare programs.
Figure 2 breaks comments into categories, the fi rst
related to aspects of service, the second to the personal
aspects of the people involved in the service. Twitter com-
menters complain about both service and fellow passen-
gers—a lot. Tweets about social welfare programs and
public transit contain comments about race, class, and
gender that do not appear as frequently in tweets about
parks or airlines, where comments about age predominate
in complaints about fellow passengers. Gendered racial
slurs about African-American women are disproportion-
ately frequent in the transit and social welfare content.
To examine the potential sources of negative commentary,
a second machine-coding exercise isolated all the sentiment,
both positive and negative, about the personal appearance of
the patrons within the data set. I found that the differences in
sentiment about transit and airlines become statistically
indistinguishable once I remove slurs from the data. (See Table
A-1 in the Technical Appendix.) In short, negative and racist
comments about transit patrons are a major component of all
negative comments about transit; otherwise, commenters
appear to be saying equally happy and unhappy things about
public transit service and airline service.
Table 2. Control groups and test groups for machine scoring.
Control groups
Test groups
Public parks Transit

Social
programs/
stigma Airlines PoliceVillains Celebrities
Transportation Security
Administration (TSA)
Serena Williams
(athlete)
Grant Park (Chicago) MBTA
(Boston)
Food stamps American Atlanta
Osama Bin Laden (leader of
terrorist strike against the U.S.)
William Shatner
(actor)
Bryant Park
(New York)
NY MTA
(New York)
AFDC Alaska Air Baltimore
Internal Revenue Service
(federal auditing service)
Kardashian
(reality TV stars)

People’s Park
(Berkeley)
CTA
(Chicago)
Social Security British
Airways
Cincinnati
Westboro Baptist Church
(organization that protests
at high-profi le funerals)
Snoop Dogg
(musician)
Prospect Park
(Brooklyn, NYC)
Translink Medicare Delta Chicago
Michael Dunn (accused killer) Nathan Fillon
(actor)
Audubon Park
(New Orleans)
TTC
(Toronto)
“minimum wage” JetBlue Detroit
Logan Circle

(Philadelphia)
SEPTA Medicaid United Los Angeles
Forest Park (St. Louis) DC Metro “welfare queen” US Airways
Miami
Golden Gate Park
(San Francisco)
BART
(San Francisco)
Affordable Care
Act
Virgin
America
Oakland
Boston Common
(Boston)
TriMet
(Portland)
Obamacare Southwest Philadelphia
Patterson Park
(Baltimore)
LA MTA Sacramento
Note: AFDC = Aid to Families with Dependent Children.

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Figure 1. Opinion score rankings that result from machine
scoring.
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2014, Vol. 80, No. 3
Figure 2. General topics covered by the Twitter comments.
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sign that reads, “For more effi cient service, please get off at
the next stop, where a team of heavily drugged sloths will
drag you to your destination.” The machine code results in
a +2 score because of the words “eloquent” and “inspiring”
in the actual tweet, but the picture sends a different mes-
sage. Hand coding scored this tweet a –1 for service tim-
ing. This error occurred more often for transit than for any
other control, so that the match between hand and ma-
chine coding was 82%, with most of the mistakes occur-
ring with images. The resulting distributions placed hand-
coding scores lower than the machine coding, but do not
affect the results of statistical analysis. Between this check
and the celebrities and villains controls, I can conclude that
the algorithm I am using is not driving the results. Instead,
there are statistically signifi cant differences among transit
agencies and between controls.
Communication Style and Potential
Infl uence on Sentiment
As Table 4 illustrates, transit agencies vary in how they
use their Twitter accounts. The fi rst or “interactive” style
entails chatting in two-way communication with individ-
ual commenters. By contrast, blast communications tend
to be one-way communications from the agency outward,
with little patron interaction. It includes many service
announcements and some social marketing and other
agency self-promotion. Some agencies, such as SEPTA and
Table 3. Basic agency performance data.

Score
rank Name
On-time
performancea
Unlinked
Trips (millions)b
1 Translink 90% to 95% 237
2 TTC 55% to 90% 471
3 TriMet 76% to 98% 99
4 LA Metro 78% 476
5 BART 88% to 94% 127
6 WMTA 79% to 91% 413
7 NY MTA 77% to 91% 3,467
8 SEPTA 87% 358
9 MBTA 98% to 100% 395
10 CTA No report 529
Notes:
a. Some agencies (e.g., LA Metro) report only systemwide
performance,
while others disaggregate by line. For the latter, a range is
reported (Bay
Area Rapid Transit, 2014; Los Angeles Metropolitan

Transportation
Authority, 2014; Massachusetts Bay Transportation Authority,
2014;
New York Metropolitan Transportation Authority, 2014; South
Coast
British Columbia Transportation Authority, 2014; SEPTA,
2014;
Toronto Transit Commission, 2014a, 2014b; Tri-Met, 2014;
Washington Metropolitan Area Transit Authority, 2014).
b. Data from the American companies come from the National
Transit
Database (2013), data compiled by the author. Translink data
come
from South Coast British Columbia Transportation Authority
(2013).
Does a difference in service quality account for the
differences in tweets about individual transit agencies?
Table 3 displays the most recent data about transit agency
service along with self-reported on-time performance data.
Neither of these two dimensions of service provision tracks
closely with the ranks that emerge from the Twitter com-
ments. It is not that poor service providers are more criti-
cized on social media while the “angel” companies provid-
ing on-time service get the appreciation they deserve. In
fact, the more service an agency provides, the more com-
plaining there is. My approach measures the content of the
conversation about the agencies, not their service quality,
per se. Thus, the differences in opinion expressed about
transit companies cannot be explained by differences in
service quality.
I hand coded the content using a random sample (n =
1,000) of the machine-coded comments to double check
how well the machine-coded approach performed against
human coding. This also allowed me to view more detail

about the source and content of the tweets. In general,
hand and machine codes matched well except for places
where the machine coding would be expected to fail, such
as with sarcasm and images. One tweet in particular illus-
trates multiple problems for machine coding: a picture of a
Table 4. Different communication styles.
“Dialog”-style communication
SEPTA_SOCIAL
@SEPTA_SOCIAL Oct 12
Good Morning! I’m Eric of SEPTA’s Social
Media team ready to assist you. Bleed
Green, Cheer Green! Go Eagles! Tweet us
if you need us!
Twitter User Oct 12 @SEPTA_SOCIAL Hi Eric! Will septa run
on a holiday schedule tomorrow?
SEPTA_SOCIAL
We’re operating on a weekend schedule
tomorrow.
Twitter User Oct 12 @SEPTA_SOCIAL thank you!
SEPTA_SOCIAL
You’re welcome! Have a pleasant Sunday!
“Blast”-style communication

cta @cta 9h Remember: The O’Hare station reopened
yesterday afternoon, following repairs.
ow.ly/vdd07
cta @cta Mar 30 The O’Hare station is open for service.
Here are some pics of repair work and the
reopening:
11/12/14 6:58 AM11/12/14 6:58 AM
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2014, Vol. 80, No. 3
the Toronto Transportation Commission (TTC), break
their customer service and service notices into separate
feeds.4 Most transit agencies have thousands of followers

who see what the agencies post, but only a few of them
(Translink, Bay Area Rapid Transit [BART], and TriMet)
subscribe or follow a large number of feeds themselves. It is
not required that agencies follow other users to interact
with them, but following is one way to signal to other users
that one wants to see their content. Translink is by far the
most active Twitter user among the transit agencies that I
studied. They tweet often, on average about 90 times per
day, while most other agencies tweet between 10 and 30
times a day. Translink interacts a great deal in conversation
with other Twitter users, and they also follow others more
than other transit agencies. TriMet also follows a compara-
tively high number of other Twitter users compared with
other transit agencies.
One agency’s approach nicely illustrates what interacting
with patrons might do to alter the content related to an
individual transit agency. I fi rst began to collect Twitter
comments in 2010; I continued to do so through 2014. In
the fi rst years of the study, the opinions expressed about
SEPTA always ranked at or near the bottom with the
Massachusetts Bay Transportation Authority (MBTA) and
Chicago Transit Authority (CTA). In December of 2011,
SEPTA introduced its customer service dialog feed
(@SEPTA_SOCIAL) to run parallel with its blast feed on
service announcements. After one year using a two-way
communication style, SEPTA’s opinion score had gone from
–0.3 (not far from the “villains” control) to ranging from 0
to –0.09. Although still negative, that is a 70% improve-
ment in a relatively short amount of time. We can see that
this change is more signifi cant when we examine the effect
on the second source of negativity: slurs directed at other
passengers. Prior to the customer service feed, the race, class,
and gender categories held nearly 7% of SEPTA’s total feed
in data collected from winter 2010 to 2011. From fall 2012

through spring 2014, that percentage had shrunk to 2.5%.
To check for differences in communication style, I
randomly sampled 60 days of the content of all Twitter
feeds that came directly from the agency itself over the
course of two years. I then identifi ed which agency Twitter
comments were “conversation” moments, in which an
agency representative tweets back and forth with another
commenter. I then developed a dialog score (D-score in
Table 5) that calculated the ratio of conversation moments
to the agency’s total content broadcast via Twitter to every-
one. Table 5 displays the D-score for each of the 10 agen-
cies sampled. Based on that score, the agencies were
grouped in levels of interaction with other Twitter users:
high (D < 60%), medium (30% to 60%), and low (less
than 30%). TriMet tweets comparatively little, but when
they do, it is often to interact with other Twitter users. It is
important to remember that the D-score is a measure of
relative effort and does not measure overall effort. It simply
refl ects what percentage of the agency’s total communica-
tion on Twitter occurs in dialog with other Tweeters as
opposed to being blasted out.
Hand Coding Twitter Content by Topic
and Sentiment
To test whether there were statistically signifi cant
differences in the communication style of various agencies,
I drew a sample of 5,000 tweets, 500 for each agency, from
the entire Twitter data set, excluding tweets from the
agencies’ own feeds. I stratifi ed the sample by Twitter user
profi le: government, elected offi cials, business accounts,
nongovernmental organizations (NGOs), patrons, transit
Table 5. Online interaction differences among transit agencies.

Machine
score ranking Agency D-score
Interaction
category
Followers
(1000s) Following
1 Translink 0.86 High 50.3 12,200
2 TTC 0.77 High 12.7 492
3 TriMet 0.40 Medium 15.0 8,350
4 LA Metro 0.62 High 26.1 329
5 BART 0.13 Low 59.5 2,246
6 WMTA 0.27 Low 65.9 228
7 NY MTA 0.00 Low 96.4 243
8 SEPTA 0.53 Medium 7.01 1,661
9 MBTA 0.41 Medium 15.9 9,030
10 CTA 0.22 Low 43.0 62
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operators, planners, enthusiasts, and watchdogs and satire
accounts. Table A-2 in the Technical Appendix provides
more detail about the sampling stratifi cation method and
how I coded by user type.
My graduate student coders and I met initially to select
a coding rubric together. The two students and I fi rst
coded the same 200 comments separately. The team then
applied multiple codes to the Twitter text to come to a
higher leveling of coding consistency. Several iterations
were required for the team to reach an acceptable Krippen-
dorf ’s alpha of .86.5 After this standard was met, the team
coded all the data and, again, backcoded a random sample
to test consistency. This check again yielded a Krippen-
dorf ’s alpha of 0.84, which is acceptable.
Table 6 shows a selection of coded Twitter comments
to illustrate the coding method, also outlined in greater
detail in the Technical Appendix. The research team coded

Twitter comments by the type of service complaints: tim-
ing, driver content, staff conduct, facilities, security, and
climate control. We also coded complaints about the
behavior of passengers as well as slurs about passengers.
Slurs include comments about age; size; race; class; gender;
disability; size; and lesbian, gay, bisexual, or transgender
(LGBT) status. Finally, the team marked content regarding
planning and public management announcements.
Table 7 shows the average for each variable by high,
medium, and low interaction groups previously described.
Opinions about service, patrons, planning, and manage-
ment, while still negative, are consistently higher for agen-
cies in the high-interaction group, those agencies that
engage more directly with individual tweeters.
Table 8 summarizes fi ndings of statistical tests of
opinion by topic and by interaction level. These tests
illustrate two important things: Transit companies that
respond to other social media users have statistically more
favorable opinions expressed about the transit agency for
just about every measure I considered. (The statistical tests
are displayed in full in Tables A-3 and A-4 in the Technical
Table 6. Examples of hand coding.
Code (score)a Commentary
Timing (–2) @wmata Slowly sucking our souls and our money
one messed up day at a time. 45 minute wait for Orange-line
train today!
Facilities (–2) Just watched a man on crutches hobble down a
broken escalator at L’Enfant. @wmata is despicable.
#unsuckdcmetro

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