Sage Journals: Discover world-class research

Abstract

Transit providers have used social media (e.g., Twitter) as a powerful platform to shape public perception and provide essential information, especially during times of disruption and disaster. This work examines how transit agencies used Twitter during the COVID-19 pandemic to communicate with riders and how the content and general activity influence rider interaction and Twitter handle popularity. We analyzed 654,345 tweets generated by the top 40 transit agencies in the US, based on Vehicles Operated in Annual Maximum Service (VOM), from January 2020 to August 2021. We developed an analysis framework, using advanced machine learning and natural language processing models, to understand how agencies’ tweeting patterns are associated with rider interaction outcomes during the pandemic. From the transit agency perspective, we find smaller agencies tend to generate a higher percentage of COVID-related tweets and some agencies are more repetitive than their peers. Six topics (i.e., face covering, essential service appreciation, free resources, social distancing, cleaning, and service updates) were identified in the COVID-related tweets. From the followers’ interaction perspective, most agencies gained followers after the start of the pandemic (i.e., March 2020). The percentage of follower gains is positively correlated with the percentage of COVID-related tweets, tweets replying to followers, and tweets using outlinks. The average like counts per COVID-related tweet is positively correlated with the percentage of COVID-related tweets and negatively correlated with the percentage of tweets discussing social distancing and agency repetitiveness. This work can inform transportation planners and transit agencies on how to use Twitter to effectively communicate with riders to improve public perception of health and safety as it relates to transit ridership during delays and long-term disruptions such as those created by the COVID-19 public health crisis.

Keywords

Transit social media Twitter natural language processing communication in times of disruption

Get full access to this article

View all access options for this article.

References

Aman

Smith-Colin

Zhang

(2021) Listen to E-scooter riders: mining rider satisfaction factors from app store reviews. Transportation Research Part D: Transport and Environment 95: 102856.

Bird

Klein

Loper

(2009) Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. Sebastopol, CA: O’Reilly Media, Inc.

Blei

Jordan

(2003) Latent dirichlet allocation. The Journal of Machine Learning Research 3: 993–1022.

Bregman

(2012a) Uses of Social Media in Public Transportation. Washington DC: Transportation Research Board.

Bregman

(2012b) Uses of social media in public transportation: summary of findings from TCRP synthesis SB-20 (No. 12–2733). Article 12–2733. Transportation Research Board 91st Annual Meeting Transportation Research Board. Available at: https://trid.trb.org/view/1129920

Casas

Delmelle

(2017) Tweeting about public transit—Gleaning public perceptions from a social media microblog. Case Studies on Transport Policy 5(4): 634–642.

Chan

Schofer

(2014) Role of social media in communicating transit disruptions. Transportation Research Record 2415(1): 145–151. https://doi.org/10.3141/2415-16

Cheng

Y-H

(2010) Exploring passenger anxiety associated with train travel. Transportation 37(6): 875–896.

Cihon

Yasseri

(2016) Frontiers | A biased review of biases in Twitter studies on political collective action. Available at: https://www.frontiersin.org/articles/10.3389/fphy.2016.00034/full?source=post_page---------------------------

10.

Collins

Hasan

Ukkusuri

(2013) A novel transit rider satisfaction metric: rider sentiments measured from online social media data. Journal of Public Transportation 16(2): 2–45.

11.

Cottrill

Gault

Yeboah

, et al. (2017) Tweeting transit: an examination of social media strategies for transport information management during a large event. Transportation Research Part C: Emerging Technologies 77: 421–432.

12.

DART Daily (2021) DART wins two honors at the 2021 south west transit association spotlight awards. Available at: https://dartdaily.dart.org/posts/news-post/dart-wins-two-honors-at-the-2021-south-west-transit-association-spotlight-awards

13.

Diaz

Abbasi

Fuller

, et al. (2021) Canadian transit agencies response to COVID-19: Understanding strategies, information accessibility and the use of social media. Transportation Research Interdisciplinary Perspectives 12: 100465. DOI: 10.1016/j.trip.2021.100465

14.

Doyle

(2021) APTA Announces 2021 AdWheel Award First Place Winners for Industry Marketing & Communications Campaigns. Washington DC: American Public Transportation Association. Available at: https://www.apta.com/news-publications/press-releases/releases/apta-announces-2021-adwheel-award-first-place-winners-for-industry-marketing-communications-campaigns/.

15.

EBP (2021) The impact of the COVID-19 pandemic on public transit needs in the U.S. Available at: https://www.apta.com/wp-content/uploads/APTA-COVID-19-Funding-Impact-2021-01-27.pdf

16.

El-Diraby

Shalaby

Hosseini

(2019) Linking social, semantic and sentiment analyses to support modeling transit customers’ satisfaction: Towards formal study of opinion dynamics. Sustainable Cities and Society 49: 101578. DOI: 10.1016/j.scs.2019.101578

17.

Evans-Cowley

Griffin

(2012) Microparticipation with social media for community engagement in transportation planning. Transportation Research Record 2307(1): 90–98.

18.

Gordon

Baldwin-Philippi

Balestra

(2013) Why We Engage: How Theories of Human Behavior Contribute to Our Understanding of Civic Engagement in a Digital Era. Boston, MA: Berkman Center Research Publication, 21.

19.

Gupta

Zhang

Hall

(2021) Risk priorities and their co-occurrences in smart city project implementation: evidence from India’s smart city mission. Environment and Planning B: Urban Analytics and City Science 48(4): 880–894.

20.

Haghighi

Liu

Wei

, et al. (2018) Using Twitter data for transit performance assessment: a framework for evaluating transit riders’ opinions about quality of service. Public Transport 10(2): 363–377. DOI: 10.1007/s12469-018-0184-4

21.

Harazeen

(2011) Effects of Information on Decisions of Passengers during Service Disruptions. Msc, University College London.

22.

Head

MechCoder

Shcherbatyi

(2018) Scikit-Optimize/Scikit-Optimize: V0. 5.2. Switzerland: Zenodo.

23.

Hosseini

El-Diraby

Shalaby

(2018) Supporting sustainable system adoption: socio-semantic analysis of transit rider debates on social media. Sustainable Cities and Society 38: 123–136. https://doi.org/10.1016/j.scs.2017.12.025

24.

JustAnotherArchivist (2021) snscrape: a social networking service scraper in Python. Available at: https://github.com/JustAnotherArchivist/snscrape

25.

Liu

Shi

Elrahman

, et al. (2016) Understanding social media program usage in public transit agencies. International Journal of Transportation Science and Technology 5(2): 83–92. DOI: 10.1016/j.ijtst.2016.09.005

26.

Liu

Miller

Scheff

(2020) The impacts of COVID-19 pandemic on public transit demand in the United States. Plos One 15(11): e0242476. DOI: 10.1371/journal.pone.0242476

27.

Luong

Houston

(2015) Public opinions of light rail service in Los Angeles, an analysis using Twitter data. IConference 2015 Proceedings.

28.

Pedregosa

Varoquaux

Gramfort

, et al. (2011) Scikit-learn: machine learning in Python. Journal of Machine Learning Research 12(Oct): 2825–2830.

29.

Pender

Currie

Delbosc

, et al. (2014) Social media use during unplanned transit network disruptions: a review of literature. Transport Reviews 34(4): 501–521.

30.

Costin

Jia

(2020) A framework with efficient extraction and analysis of Twitter data for evaluating public opinions on transportation services. Travel Behaviour and Society 21: 10–23.

31.

Raymond

O’Hara

(2014) Communicating at the speed of light: using social media as a marketing tool. Best Practices for Transportation Agency Use of Social Media 86.

32.

Riquelme

González-Cantergiani

(2016) Measuring user influence on Twitter: a survey. Information Processing & Management 52(5): 949–975. DOI: 10.1016/j.ipm.2016.04.003

33.

Roesslein

(2020) Tweepy: Twitter for Python! Available at: https://Github.Com/Tweepy/Tweepy

34.

Schütze

Manning

Raghavan

(2008) Introduction to Information Retrieval. Cambridge, UK: Cambridge University Press, 39.

35.

Schweitzer

(2014) Planning and social media: a case study of public transit and stigma on Twitter. Journal of the American Planning Association 80(3): 218–238. DOI: 10.1080/01944363.2014.980439

36.

Sun

Yin

(2017) Discovering themes and trends in transportation research using topic modeling. Transportation Research Part C: Emerging Technologies 77: 49–66.

37.

The Urban Transportation Monitor (2011) The use of social media by transportation agencies. The Urban Transportation Monitor 25(1): 18–25.

38.

Transport Focus (2011) Information: rail passengers’ needs during unplanned disruption. Available at: https://www.transportfocus.org.uk/publication/information-rail-passengers-needs-during-unplanned-disruption/

39.

Transport Focus (2015) Social media monitoring – pilot research report. Available at: https://www.transportfocus.org.uk/publication/social-media-monitoring-pilot-research-report/

40.

Wang

Dong

(2020) Measurement of text similarity: a survey. Information 11(9): 421. DOI: 10.3390/info11090421

41.

Windasari

I. P.

Uzzi

F. N.

Satoto

K. I.

(2017, October). Sentiment analysis on Twitter posts: An analysis of positive or negative opinion on GoJek. In 2017 4th international conference on information technology, computer, and electrical engineering (ICITACEE) (pp. 266-269). IEEE.” It's not a book chapter but a conference proceeding paper.

42.

Wojcik

Hughes

(2019, April 24). Sizing up Twitter users. pew research center: internet, science & Tech. Available at: https://www.pewresearch.org/internet/2019/04/24/sizing-up-twitter-users/

43.

Yates

Paquette

(2011) Emergency knowledge management and social media technologies: a case study of the 2010 Haitian earthquake. International Journal of Information Management 31(1): 6–13.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

3.29 MB

Transit communication via Twitter during the COVID-19 pandemic

Abstract

Keywords

Get full access to this article

References

Supplementary Material