Planning and Social Media: A Case Study of Public Transit and Stigma on Twitter

A New Market Segmentation Approach: Evidence from Two Canadian Cities

“…Transit agencies should ensure that these users develop a positive perception of the system with regard to efficiency, travel time, and reliability (Carreira et al 2014;Chou et al 2014;de Oña et al 2013). In the long term, transit agencies should focus on improving the common negative cultural image that is often attributed to transit (Schweitzer 2014). Transit's cultural stigma can be changed by the implementation of policies that promote the service as being more comfortable and more efficient than using a private motorized vehicle (Chou and Kim 2009;Chou et al 2014;Lee et al 2009).…”

Measuring the Impacts of Social Media on Advancing Public Transit

“…However, the change in sentiments from 2010 to 2014 indicated that agencies that were "interactive" and responded directly to questions, concerns and comments of other social media users (instead of "blasting" announcements) received more positive statements and fewer slurs directed at patrons, independent of actual service quality, even if agencies responded only to a select number of tweets each day. One case example is the Southeastern Pennsylvania Transportation Authority (SEPTA), which consistently ranked at the bottom of negative Twitter comments in 2010 and improved its opinion score by 70% only one year after it introduced its customer service dialog feed to run parallel with its blast feed (Schweitzer, 2014). However, customer service representatives do not have specific social media rules and regulations.…”

“…Methods used included "text mining," scanning text for positive and negative opinion words, and "machine learning" how a computer selects information based on a programmed algorithm (Schweitzer, 2014). There are many advantages in using sentiment analysis to measure transit rider satisfaction (instead of transit surveys): minimal cost of data collection, data collected in real time, user-specific needs can be assessed, and the data can provide context to why a particular sentiment is felt.…”

“…Furthermore, attracting allies (planners, businesses, government agencies, enthusiasts, etc.) could also boost the overall sentiment expressed on social media (Schweitzer, 2014).…”

“…Schweitzer (2014) analyzed transit rider sentiments towards public transportation agencies. Results showed that public transit received more negative sentiments than other agencies (parks departments and airlines), with a similar sentiment pattern of racist, ageist and other discriminatory comments as police departments and social welfare programs.…”