Evidence map›Paper›PMID 38603201›Full record

ArticleEnvironment and planning. B, urban analytics and city science2023

Transit communication via Twitter during the COVID-19 pandemic.

Wenwen Zhang, Camille Barchers, Janille Smith-Colin

Abstract read
In one paragraph

Article in Environment and planning. B, urban analytics and city science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. An overview of urban analytical approaches to combating the Covid-19 pandemic.Environment and planning. B, urban analytics and city science · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Wenwen ZhangRutgers, the State University of New Jersey, New Brunswick, NJ, USA.ORCID https://orcid.org/0000-0002-2517-8601
Camille BarchersUniversity of Massachusetts, Amherst, MA, USA.ORCID https://orcid.org/0000-0002-7653-2949
Janille Smith-ColinSouthern Methodist University, Dallas, TX, USA.ORCID https://orcid.org/0000-0002-0297-4154

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

communication in times of disruptionnatural language processingsocial mediaTransitTwitter

Identifiers

PMID38603201
PMCPMC9659705

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.