Impact of Social Media on Travel Behaviors during the COVID-19 Pandemic: Evidence from New York City

2021 ◽  
pp. 036119812110338
Author(s):  
Qian Ye ◽  
Kaan Ozbay ◽  
Fan Zuo ◽  
Xiaohong Chen
Keyword(s):  
New York ◽  
Social Media ◽  
New York City ◽  
York City ◽  
Information Exchange ◽  
Public Transit ◽  
Time Lag ◽  
Online Communication ◽  
Travel Behaviors ◽  
Travel Decisions

During the outbreak of COVID-19, people’s reliance on social media for pandemic-related information exchange, daily communications, and online professional interactions increased because of self-isolation and lockdown implementation. Most of the published research addresses the performance of nonpharmaceutical interventions (NPIs) and measures on the issues impacted by COVID-19, such as health, education, and public safety; however, not much is known about the interplay between social media use and travel behaviors. This study aims to determine the effect of social media on human mobility before and after the COVID-19 outbreak, and its impact on personal vehicle and public transit use in New York City (NYC). Apple mobility trends and Twitter data are used as two data sources. The results indicate that Twitter volume and mobility trend correlations are negative for both driving and transit categories in general, especially at the beginning of the COVID-19 outbreak in NYC. A significant time lag (13 days) between the online communication rise and mobility drop can be observed, thereby providing evidence of social networks taking quicker reactions to the pandemic than the transportation system. In addition, social media and government policies had different impacts on vehicular traffic and public transit ridership during the pandemic with varied performance. This study provides insights on the complex influence of both anti-pandemic measures and user-generated content, namely social media, on people’s travel decisions during pandemics. The empirical evidence can help decision-makers formulate timely emergency responses, prepare targeted traffic intervention policies, and conduct risk management in similar outbreaks in the future.

Social dynamics of Twitter usage in London, Paris, and New York City

First Monday ◽  
2014 ◽  
Author(s):  
Muhammad Adnan ◽  
Paul A. Longley ◽  
Shariq M. Khan
Keyword(s):  
New York ◽  
Social Media ◽  
New York City ◽  
Ethnic Identity ◽  
York City ◽  
Social Dynamics ◽  
Activity Patterns ◽  
Twitter Users ◽  
And Gender ◽  
Use Of Social Media

The penetration and use of social media services differs from city to city. This paper investigates the social dynamics of Twitter social media usage in three ethnically diverse cities — London, Paris, and New York City. We present a spatial analysis of Tweeting activity in the three cities, broken down by ethnicity and gender. We model the ethnic identity of Twitter users using their paired forenames and surnames. The geo–tagged Tweets provide an insight into the geography of their activity patterns across the three cities. The gender of each Twitter user is identified through classification of forenames, suggesting that, irrespective of the ethnic identity, the majority of Twitter users are male. Taken together, the results present a window on the activity patterns of different ethnic groups.

scholarly journals Spatio-Temporal Distribution of Negative Emotions in New York City After a Natural Disaster as Seen in Social Media

2018 ◽  
Vol 15 (10) ◽  
pp. 2275 ◽  
Author(s):  
Oliver Gruebner ◽  
Sarah Lowe ◽  
Martin Sykora ◽  
Ketan Shankardass ◽  
SV Subramanian ◽  
...  
Keyword(s):  
New York ◽  
Social Media ◽  
New York City ◽  
Natural Disaster ◽  
York City ◽  
Negative Emotions ◽  
Temporal Distribution ◽  
Census Tracts ◽  
Post Disaster ◽  
Over Time

Disasters have substantial consequences for population mental health. We used Twitter to (1) extract negative emotions indicating discomfort in New York City (NYC) before, during, and after Superstorm Sandy in 2012. We further aimed to (2) identify whether pre- or peri-disaster discomfort were associated with peri- or post-disaster discomfort, respectively, and to (3) assess geographic variation in discomfort across NYC census tracts over time. Our sample consisted of 1,018,140 geo-located tweets that were analyzed with an advanced sentiment analysis called ”Extracting the Meaning Of Terse Information in a Visualization of Emotion” (EMOTIVE). We calculated discomfort rates for 2137 NYC census tracts, applied spatial regimes regression to find associations of discomfort, and used Moran’s I for spatial cluster detection across NYC boroughs over time. We found increased discomfort, that is, bundled negative emotions after the storm as compared to during the storm. Furthermore, pre- and peri-disaster discomfort was positively associated with post-disaster discomfort; however, this association was different across boroughs, with significant associations only in Manhattan, the Bronx, and Queens. In addition, rates were most prominently spatially clustered in Staten Island lasting pre- to post-disaster. This is the first study that determined significant associations of negative emotional responses found in social media posts over space and time in the context of a natural disaster, which may guide us in identifying those areas and populations mostly in need for care.

scholarly journals Spatiotemporal Patterns of Human Mobility and Its Association with Land Use Types during COVID-19 in New York City

2021 ◽  
Vol 10 (5) ◽  
pp. 344
Author(s):  
Yuqin Jiang ◽  
Xiao Huang ◽  
Zhenlong Li
Keyword(s):  
New York ◽  
Social Media ◽  
Land Use ◽  
New York City ◽  
York City ◽  
Local Governments ◽  
Human Mobility ◽  
Federal State ◽  
Mobility Patterns ◽  
Land Use Types

The novel coronavirus disease (COVID-19) pandemic has impacted every facet of society. One of the non-pharmacological measures to contain the COVID-19 infection is social distancing. Federal, state, and local governments have placed multiple executive orders for human mobility reduction to slow down the spread of COVID-19. This paper uses geotagged tweets data to reveal the spatiotemporal human mobility patterns during this COVID-19 pandemic in New York City. With New York City open data, human mobility pattern changes were detected by different categories of land use, including residential, parks, transportation facilities, and workplaces. This study further compares human mobility patterns by land use types based on an open social media platform (Twitter) and the human mobility patterns revealed by Google Community Mobility Report cell phone location, indicating that in some applications, open-access social media data can generate similar results to private data. The results of this study can be further used for human mobility analysis and the battle against COVID-19.

scholarly journals Evaluation of a health information exchange system for microcephaly case-finding — New York City, 2013—2015

PLoS ONE ◽  
2020 ◽  
Vol 15 (8) ◽  
pp. e0237392
Author(s):  
Eugenie Poirot ◽  
Carrie W. Mills ◽  
Andrew D. Fair ◽  
Krishika A. Graham ◽  
Emily Martinez ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Health Information ◽  
York City ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Case Finding ◽  
Exchange System

Time lag effects of COVID-19 policies on transportation systems: A comparative study of New York City and Seattle

2021 ◽  
Vol 145 ◽  
pp. 269-283
Author(s):  
Zilin Bian ◽  
Fan Zuo ◽  
Jingqin Gao ◽  
Yanyan Chen ◽  
Sai Sarath Chandra Pavuluri Venkata ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Comparative Study ◽  
York City ◽  
Time Lag ◽  
Transportation Systems ◽  
Lag Effects

scholarly journals Notes from the Field: Use of Social Media as a Communication Tool During a Mumps Outbreak — New York City, 2015

2017 ◽  
Vol 66 (02) ◽  
pp. 60-61 ◽  
Author(s):  
Beth M. Isaac ◽  
Jane R. Zucker ◽  
Jennifer MacGregor ◽  
Mekete Asfaw ◽  
Jennifer L. Rakeman ◽  
...  
Keyword(s):  
New York ◽  
Social Media ◽  
New York City ◽  
York City ◽  
Communication Tool ◽  
Use Of Social Media

Identifying urban crowds using geo-located Social media data: a Twitter experiment in New York City

2016 ◽  
Vol 48 (2) ◽  
pp. 287-308 ◽  
Author(s):  
Mohamed ben Khalifa ◽  
Rebeca P. Díaz Redondo ◽  
Ana Fernández Vilas ◽  
Sandra Servia Rodríguez
Keyword(s):  
New York ◽  
Social Media ◽  
New York City ◽  
York City ◽  
Social Media Data ◽  
Media Data

scholarly journals Hospital Closure and Insights into Patient Dispersion

2015 ◽  
Vol 06 (01) ◽  
pp. 185-199 ◽  
Author(s):  
N. Garg ◽  
G. Husk ◽  
T. Nguyen ◽  
A. Onyile ◽  
S. Echezona ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Medical Center ◽  
The United States ◽  
Hospital System ◽  
Hospital Closures ◽  
Hospital Closure

SummaryBackground: Hospital closures are becoming increasingly common in the United States. Patients who received care at the closing hospitals must travel to different, often farther hospitals for care, and nearby remaining hospitals may have difficulty coping with a sudden influx of patients.Objectives: Our objectives are to analyze the dispersion patterns of patients from a closing hospital and to correlate that with distance from the closing hospital for three specific visit types: emergency, inpatient, and ambulatory.Methods: In this study, we used data from a health information exchange to track patients from Saint Vincent’s Medical Center, a hospital in New York City that closed in 2010, to determine where they received emergency, inpatient, and ambulatory care following the closure.Results: We found that patients went to the next nearest hospital for their emergency and inpatient care, but ambulatory encounters did not correlate with distance.Discussion: It is likely that patients followed their ambulatory providers as they transitioned to another hospital system. Additional work should be done to determine predictors of impact on nearby hospitals when another hospital in the community closes in order to better prepare for patient dispersion.Citation: Garg N, Husk G, Nguyen T, Onyile A, Echezona S, Kuperman G, Shapiro JS. Hospital closure and insights into patient dispersion: the closure of Saint Vincent’s Catholic Medical Center in New York City. Appl Clin Inf 2015; 6: 185–199http://dx.doi.org/10.4338/ACI-2014-10-RA-0090

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