Literature DB >> 28078449

Social media responses to heat waves.

Jihoon Jung1, Christopher K Uejio2,3.   

Abstract

Social network services (SNSs) may benefit public health by augmenting surveillance and distributing information to the public. In this study, we collected Twitter data focusing on six different heat-related themes (air conditioning, cooling center, dehydration, electrical outage, energy assistance, and heat) for 182 days from May 7 to November 3, 2014. First, exploratory linear regression associated outdoor heat exposure to the theme-specific tweet counts for five study cities (Los Angeles, New York, Chicago, Houston, and Atlanta). Next, autoregressive integrated moving average (ARIMA) time series models formally associated heat exposure to the combined count of heat and air conditioning tweets while controlling for temporal autocorrelation. Finally, we examined the spatial and temporal distribution of energy assistance and cooling center tweets. The result indicates that the number of tweets in most themes exhibited a significant positive relationship with maximum temperature. The ARIMA model results suggest that each city shows a slightly different relationship between heat exposure and the tweet count. A one-degree change in the temperature correspondingly increased the Box-Cox transformed tweets by 0.09 for Atlanta, 0.07 for Los Angeles, and 0.01 for New York City. The energy assistance and cooling center theme tweets suggest that only a few municipalities used Twitter for public service announcements. The timing of the energy assistance tweets suggests that most jurisdictions provide heating instead of cooling energy assistance.

Entities:  

Keywords:  ARIMA; Cooling center; Energy assistance; Heat wave; Social media; Time series

Mesh:

Year:  2017        PMID: 28078449     DOI: 10.1007/s00484-016-1302-0

Source DB:  PubMed          Journal:  Int J Biometeorol        ISSN: 0020-7128            Impact factor:   3.787


  36 in total

1.  More intense, more frequent, and longer lasting heat waves in the 21st century.

Authors:  Gerald A Meehl; Claudia Tebaldi
Journal:  Science       Date:  2004-08-13       Impact factor: 47.728

2.  The effects of temperature and use of air conditioning on hospitalizations.

Authors:  Bart Ostro; Stephen Rauch; Rochelle Green; Brian Malig; Rupa Basu
Journal:  Am J Epidemiol       Date:  2010-09-09       Impact factor: 4.897

3.  Epidemiologic study of mortality during the Summer 2003 heat wave in Italy.

Authors:  Susanna Conti; Paola Meli; Giada Minelli; Renata Solimini; Virgilia Toccaceli; Monica Vichi; Carmen Beltrano; Luigi Perini
Journal:  Environ Res       Date:  2004-12-08       Impact factor: 6.498

4.  Models for the relationship between ambient temperature and daily mortality.

Authors:  Ben Armstrong
Journal:  Epidemiology       Date:  2006-11       Impact factor: 4.822

5.  Heat effects on mortality in 15 European cities.

Authors:  Michela Baccini; Annibale Biggeri; Gabriele Accetta; Tom Kosatsky; Klea Katsouyanni; Antonis Analitis; H Ross Anderson; Luigi Bisanti; Daniela D'Ippoliti; Jana Danova; Bertil Forsberg; Sylvia Medina; Anna Paldy; Daniel Rabczenko; Christian Schindler; Paola Michelozzi
Journal:  Epidemiology       Date:  2008-09       Impact factor: 4.822

6.  Lights out: impact of the August 2003 power outage on mortality in New York, NY.

Authors:  G Brooke Anderson; Michelle L Bell
Journal:  Epidemiology       Date:  2012-03       Impact factor: 4.822

7.  The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic.

Authors:  Alessio Signorini; Alberto Maria Segre; Philip M Polgreen
Journal:  PLoS One       Date:  2011-05-04       Impact factor: 3.240

8.  Heat waves in the United States: mortality risk during heat waves and effect modification by heat wave characteristics in 43 U.S. communities.

Authors:  G Brooke Anderson; Michelle L Bell
Journal:  Environ Health Perspect       Date:  2010-10-07       Impact factor: 9.031

9.  Who tweets? Deriving the demographic characteristics of age, occupation and social class from twitter user meta-data.

Authors:  Luke Sloan; Jeffrey Morgan; Pete Burnap; Matthew Williams
Journal:  PLoS One       Date:  2015-03-02       Impact factor: 3.240

Review 10.  High ambient temperature and mortality: a review of epidemiologic studies from 2001 to 2008.

Authors:  Rupa Basu
Journal:  Environ Health       Date:  2009-09-16       Impact factor: 5.984

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  5 in total

Review 1.  Supporting sustainability initiatives through biometeorology education and training.

Authors:  Michael J Allen; Jennifer Vanos; David M Hondula; Daniel J Vecellio; David Knight; Hamed Mehdipoor; Rebekah Lucas; Chris Fuhrmann; Hanna Lokys; Angela Lees; Sheila Tavares Nascimento; Andrew C W Leung; David R Perkins
Journal:  Int J Biometeorol       Date:  2017-07-19       Impact factor: 3.787

Review 2.  A scoping review of the use of Twitter for public health research.

Authors:  Oduwa Edo-Osagie; Beatriz De La Iglesia; Iain Lake; Obaghe Edeghere
Journal:  Comput Biol Med       Date:  2020-05-16       Impact factor: 4.589

Review 3.  A scoping review of the use of Twitter for public health research.

Authors:  Oduwa Edo-Osagie; Beatriz De La Iglesia; Iain Lake; Obaghe Edeghere
Journal:  Comput Biol Med       Date:  2020-05-16       Impact factor: 4.589

4.  Using web data to improve surveillance for heat sensitive health outcomes.

Authors:  Jihoon Jung; Christopher K Uejio; Chris Duclos; Melissa Jordan
Journal:  Environ Health       Date:  2019-07-09       Impact factor: 5.984

5.  Overcooling of offices reveals gender inequity in thermal comfort.

Authors:  Thomas Parkinson; Stefano Schiavon; Richard de Dear; Gail Brager
Journal:  Sci Rep       Date:  2021-12-08       Impact factor: 4.379

  5 in total

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