Transit communication via Twitter during the COVID-19 pandemic
被引:4
|
作者:
Zhang, Wenwen
论文数: 0引用数: 0
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机构:
Rutgers State Univ, Bloustein Sch Planning & Public Policy, Publ Informat, New Brunswick, NJ USA
Virginia Tech, Blacksburg, VA USA
Rutgers State Univ, Edward J Bloustein Sch Planning & Publ Policy, 33 Livingston Ave, New Brunswick, NJ 08901 USARutgers State Univ, Bloustein Sch Planning & Public Policy, Publ Informat, New Brunswick, NJ USA
Zhang, Wenwen
[1
,2
,5
]
论文数: 引用数:
h-index:
机构:
Barchers, Camille
[3
]
论文数: 引用数:
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机构:
Smith-Colin, Janille
[4
]
机构:
[1] Rutgers State Univ, Bloustein Sch Planning & Public Policy, Publ Informat, New Brunswick, NJ USA
[2] Virginia Tech, Blacksburg, VA USA
[3] Univ Massachusetts, Reg Planning, Amherst, MA USA
Transit;
social media;
Twitter;
natural language processing;
communication in times of disruption;
SOCIAL MEDIA;
PUBLIC-TRANSIT;
FRAMEWORK;
OPINIONS;
D O I:
10.1177/23998083221135609
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
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.
机构:
King Saud Univ, Coll Med, Med Educ Dept, Med Informat Unit, Riyadh, Saudi ArabiaKing Saud Univ, Coll Med, Med Educ Dept, Med Informat Unit, Riyadh, Saudi Arabia
Aldekhyyel, Raniah N.
Binkheder, Samar
论文数: 0引用数: 0
h-index: 0
机构:
King Saud Univ, Coll Med, Med Educ Dept, Med Informat Unit, Riyadh, Saudi ArabiaKing Saud Univ, Coll Med, Med Educ Dept, Med Informat Unit, Riyadh, Saudi Arabia
Binkheder, Samar
Aldekhyyel, Shahad N.
论文数: 0引用数: 0
h-index: 0
机构:
King Saud bin Abdulaziz Hlth Sci, Coll Publ Hlth & Hlth Informat, Riyadh, Saudi ArabiaKing Saud Univ, Coll Med, Med Educ Dept, Med Informat Unit, Riyadh, Saudi Arabia
Aldekhyyel, Shahad N.
Alhumaid, Nuha
论文数: 0引用数: 0
h-index: 0
机构:
King Saud bin Abdulaziz Hlth Sci, Coll Publ Hlth & Hlth Informat, Riyadh, Saudi ArabiaKing Saud Univ, Coll Med, Med Educ Dept, Med Informat Unit, Riyadh, Saudi Arabia
Alhumaid, Nuha
Hassounah, Marwah
论文数: 0引用数: 0
h-index: 0
机构:
King Saud Univ, Coll Med, Family & Community Med Dept, Riyadh, Saudi ArabiaKing Saud Univ, Coll Med, Med Educ Dept, Med Informat Unit, Riyadh, Saudi Arabia
Hassounah, Marwah
Almogbel, Alanoud
论文数: 0引用数: 0
h-index: 0
机构:King Saud Univ, Coll Med, Med Educ Dept, Med Informat Unit, Riyadh, Saudi Arabia
Almogbel, Alanoud
Jamal, Amr A.
论文数: 0引用数: 0
h-index: 0
机构:
King Saud Univ, Coll Med, Family & Community Med Dept, Riyadh, Saudi Arabia
King Saud Univ, Evidence Based Hlth Care & Knowledge Translat Res, Riyadh, Saudi Arabia
King Saud Univ, Coll Med, Evidence Based Hlth Care & Knowledge Translat Res, Family & Community Med Dept, Riyadh, Saudi ArabiaKing Saud Univ, Coll Med, Med Educ Dept, Med Informat Unit, Riyadh, Saudi Arabia