{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T17:29:41Z","timestamp":1786469381421,"version":"build-2736575974"},"reference-count":43,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T00:00:00Z","timestamp":1668038400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Environment and Planning B: Urban Analytics and City Science"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:p>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\u2019 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\u2019 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.<\/jats:p>","DOI":"10.1177\/23998083221135609","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T14:59:00Z","timestamp":1668092340000},"page":"1244-1261","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Transit communication via Twitter during the COVID-19 pandemic"],"prefix":"10.1177","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2517-8601","authenticated-orcid":false,"given":"Wenwen","family":"Zhang","sequence":"first","affiliation":[{"name":"Rutgers, the State University of New Jersey, New Brunswick, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7653-2949","authenticated-orcid":false,"given":"Camille","family":"Barchers","sequence":"additional","affiliation":[{"name":"University of Massachusetts, Amherst, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0297-4154","authenticated-orcid":false,"given":"Janille","family":"Smith-Colin","sequence":"additional","affiliation":[{"name":"Southern Methodist University, Dallas, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2022,11,10]]},"reference":[{"key":"bibr1-23998083221135609","doi-asserted-by":"publisher","DOI":"10.1016\/j.trd.2021.102856"},{"key":"bibr2-23998083221135609","volume-title":"Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit","author":"Bird S","year":"2009"},{"key":"bibr3-23998083221135609","first-page":"993","volume":"3","author":"Blei DM","year":"2003","journal-title":"The Journal of Machine Learning Research"},{"key":"bibr4-23998083221135609","doi-asserted-by":"publisher","DOI":"10.17226\/14666"},{"key":"bibr5-23998083221135609","unstructured":"Bregman S (2012b) Uses of social media in public transportation: summary of findings from TCRP synthesis SB-20 (No. 12\u20132733). 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