Literature DB >> 28835312

Geospatial Distribution of Local Health Department Tweets and Online Searches About Ebola During the 2014 Ebola Outbreak.

Roger Wong1, Jenine K Harris1.   

Abstract

OBJECTIVE: This study compared the geospatial distribution of Ebola tweets from local health departments (LHDs) to online searches about Ebola across the United States during the 2014 Ebola outbreak.
METHODS: Between September and November 2014, we collected all tweets sent by 287 LHDs known to be using Twitter. Coordinates for each Ebola tweet were imported into ArcGIS 10.2.2 to display the distribution of tweets. Online searches with the search term "Ebola" were obtained from Google Trends. A Pearson's correlation test was performed to assess the relationship between online search activity and per capita number of LHD Ebola tweets by state.
RESULTS: Ebola tweets from LHDs were concentrated in cities across the northeast states, including Philadelphia and New York City. In contrast, states with the highest online search queries for Ebola were primarily in the south, particularly Oklahoma and Texas. A weak, negative, non-significant correlation (r=-0.03, P=0.83, 95% CI: -0.30, 0.25) was observed between online search activity and per capita number of LHD Ebola tweets by state.
CONCLUSIONS: We recommend that LHDs consider using social media to communicate possible disease outbreaks in a timely manner, and that they consider using online search data to tailor their messages to align with the public health interests of their constituents. (Disaster Med Public Health Preparedness. 2018; 12: 287-290).

Entities:  

Keywords:  Ebola; Twitter; emergency preparedness; local health department; social media

Mesh:

Year:  2017        PMID: 28835312     DOI: 10.1017/dmp.2017.69

Source DB:  PubMed          Journal:  Disaster Med Public Health Prep        ISSN: 1935-7893            Impact factor:   1.385


  5 in total

Review 1.  'Falsehood flies, and the truth comes limping after it': social media and public health.

Authors:  Justin B Moore; Jenine K Harris; Ellen T Hutti
Journal:  Curr Opin Psychiatry       Date:  2021-09-01       Impact factor: 4.787

2.  Public reaction to Chikungunya outbreaks in Italy-Insights from an extensive novel data streams-based structural equation modeling analysis.

Authors:  Naim Mahroum; Mohammad Adawi; Kassem Sharif; Roy Waknin; Hussein Mahagna; Bishara Bisharat; Mahmud Mahamid; Arsalan Abu-Much; Howard Amital; Nicola Luigi Bragazzi; Abdulla Watad
Journal:  PLoS One       Date:  2018-05-24       Impact factor: 3.240

3.  Mining the Characteristics of COVID-19 Patients in China: Analysis of Social Media Posts.

Authors:  Chunmei Huang; Xinjie Xu; Yuyang Cai; Qinmin Ge; Guangwang Zeng; Xiaopan Li; Weide Zhang; Chen Ji; Ling Yang
Journal:  J Med Internet Res       Date:  2020-05-17       Impact factor: 5.428

4.  Chinese Public's Attention to the COVID-19 Epidemic on Social Media: Observational Descriptive Study.

Authors:  Yuxin Zhao; Sixiang Cheng; Xiaoyan Yu; Huilan Xu
Journal:  J Med Internet Res       Date:  2020-05-04       Impact factor: 5.428

5.  Discrepancies Between Classic and Digital Epidemiology in Searching for the Mayaro Virus: Preliminary Qualitative and Quantitative Analysis of Google Trends.

Authors:  Mohammad Adawi; Nicola Luigi Bragazzi; Abdulla Watad; Kassem Sharif; Howard Amital; Naim Mahroum
Journal:  JMIR Public Health Surveill       Date:  2017-12-01
  5 in total

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