Literature DB >> 31104869

Reactions to foodborne Escherichia coli outbreaks: A text-mining analysis of the public's response.

Elizabeth M Glowacki1, Joseph B Glowacki2, Arnold D Chung3, Gary B Wilcox3.   

Abstract

Foodborne illnesses caused by bacteria are being reported at an increasing rate in the United States. We performed a text-mining analysis to look at nearly 13,000 tweets from two foodborne Escherichia coli outbreaks in 2018. Concerns from the public included staying informed about contaminated lettuce, recognizing signs of infection, and holding responsible farms accountable. At the end of the second outbreak, comments were focused on assessing symptoms, using the traceback process to locate outbreak sources, and calling for better food labeling practices.
Copyright © 2019 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Food safety; Foodborne illness; Health communication; Public health; Social media; Twitter

Year:  2019        PMID: 31104869     DOI: 10.1016/j.ajic.2019.04.004

Source DB:  PubMed          Journal:  Am J Infect Control        ISSN: 0196-6553            Impact factor:   2.918


  2 in total

1.  Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study.

Authors:  Joanne Chen Lyu; Garving K Luli
Journal:  J Med Internet Res       Date:  2021-02-09       Impact factor: 5.428

2.  Identifying public concerns and reactions during the COVID-19 pandemic on Twitter: A text-mining analysis.

Authors:  Zainab Toteh Osakwe; Izuagie Ikhapoh; Bhavleen Kaur Arora; Omonigbo Michael Bubu
Journal:  Public Health Nurs       Date:  2020-11-30       Impact factor: 1.770

  2 in total

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