Moral Panic through the Lens of Twitter: An Analysis of Infectious Disease Outbreaks

This paper presents an in-depth qualitative analysis of n=13,373 tweets that relate to the peak of the Swine Flu outbreak of 2009, and the Ebola outbreak of 2014. Tweets were analysed using thematic analysis and a number of themes and sub-themes were identified. The results were brought together in an abstraction phase and the commonalities between the cases were studied. An interesting similarity which emerged was the rate at which Twitter users expressed intense fear and panic akin to that of the sociological concept of "moral panic". Moreover, a number of discussions were found to emerge which were not reported in previous literature. Our study is the largest in-depth analysis of tweets on infectious diseases. Our results will inform public health strategies for future infectious disease outbreaks. Future work will seek to conduct further comparisons and explore relevant health theory.

[1]  Mark Achtman,et al.  Distinct Clones of Yersinia pestis Caused the Black Death , 2010, PLoS pathogens.

[2]  J. Taubenberger,et al.  1918 Influenza: the Mother of All Pandemics , 2006, Emerging infectious diseases.

[3]  Thomas Kenyon,et al.  Ebola 2014--new challenges, new global response and responsibility. , 2014, The New England journal of medicine.

[4]  David McCandless,et al.  Information is beautiful , 2009 .

[5]  Anne Gulland,et al.  Ebola outbreak in west Africa is officially over , 2016, BMJ : British Medical Journal.

[6]  G. Eysenbach,et al.  Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak , 2010, PloS one.

[7]  Gianluca Demartini,et al.  Measuring the Effect of Public Health Campaigns on Twitter: The Case of World Autism Awareness Day , 2018, iConference.

[8]  Dave Boreham,et al.  Folk Devils and Moral Panics, 3rd edn [Book Review] , 2007 .

[9]  Alan D. Lopez,et al.  The global burden of disease: a comprehensive assessment of mortality and disability from diseases injuries and risk factors in 1990 and projected to 2020. , 1996 .

[10]  M E Woolhouse,et al.  Risk factors for human disease emergence. , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[11]  Alberto Maria Segre,et al.  The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic , 2011, PloS one.

[12]  Robert J. Taylor,et al.  Global Mortality Estimates for the 2009 Influenza Pandemic from the GLaMOR Project: A Modeling Study , 2013, PLoS medicine.

[13]  Sunmoo Yoon,et al.  What can we learn about the Ebola outbreak from tweets? , 2015, American journal of infection control.

[14]  David W. Gutzke,et al.  Folk devils and moral panics , 2013 .

[15]  Elia Gabarron,et al.  Ebola, Twitter, and misinformation: a dangerous combination? , 2014, BMJ : British Medical Journal.

[16]  J. Taubenberger,et al.  Influenza : the Mother of All Pandemics , 2022 .

[17]  Gianluca Demartini,et al.  Topics discussed on twitter at the beginning of the 2014 Ebola epidemic in United States , 2017 .

[18]  David Garland,et al.  On the concept of moral panic , 2008 .

[19]  Chang-Tien Lu,et al.  Misinformation Propagation in the Age of Twitter , 2014, Computer.

[20]  Rebecca Eynon,et al.  The Ethics of Online Research , 2017 .

[21]  P. Berthon,et al.  Luxury wine brand visibility in social media: an exploratory study , 2011 .

[22]  V. Braun,et al.  Using thematic analysis in psychology , 2006 .

[23]  P. Palese,et al.  Unraveling the Mystery of Swine Influenza Virus , 2009, Cell.