Forecasting and Prevention Mechanisms Using Social Media in Health Care

Social media (SM) is establishing a new era of tools with multi-usage capabilities. Governments, businesses, organizations, as well as individuals are engaging in, implementing their promotions, sharing opinions and propagating decisions on SM. We need filters, validators and a way of weighting expressed opinions in order to regulate this continuous data stream. This chapter presents trends and attempts by the research community regarding: (a) the influence of SM on attitudes towards a specific domain, related to public health and safety (e.g. diseases, vaccines, mental health), (b) frameworks and tools for monitoring their evolution and (c) techniques for suggesting useful interventions for nudging public sentiment towards best practices. Based on the state of the art, we discuss and assess whether SM can be used as means of prejudice or esteem regarding online opinions on health care. We group the state of the art in the following categories: virus–illness outbreaks, anti-vaccination, mental health, social trends and food and environment. Furthermore, we give more weight to virus–illness outbreaks and the anti-vaccination issues/trends in order to examine disease outbreak prevention methodologies and vaccination/anti-vaccination incentives, whilst discussing their performance. The goal is to consolidate the state of the art and give well-supported directions for future work. To sum up, this chapter discusses the aforementioned concepts and related biases, elaborating on forecasting and prevention attempts using SM data.

[1]  Andrea Fronzetti Colladon,et al.  Using four different online media sources to forecast the crude oil price , 2018, J. Inf. Sci..

[2]  Qiming Liu,et al.  Vaccine scandal and crisis in public confidence in China , 2016, The Lancet.

[3]  Liang Zhao,et al.  Spatiotemporal Event Forecasting in Social Media , 2015, SDM.

[4]  Andrew Crooks,et al.  The Measles Vaccination Narrative in Twitter: A Quantitative Analysis , 2016, JMIR public health and surveillance.

[5]  Emily K. Vraga,et al.  I do not believe you: how providing a source corrects health misperceptions across social media platforms , 2017 .

[6]  Gérard Krause,et al.  Further efforts needed to achieve measles elimination in Germany: results of an outbreak investigation. , 2009, Bulletin of the World Health Organization.

[7]  Munmun De Choudhury,et al.  Characterizing Dietary Choices, Nutrition, and Language in Food Deserts via Social Media , 2016, CSCW.

[8]  Communicating vaccine science to the public. , 2014, The Journal of infection.

[9]  A. Carballo-Diéguez,et al.  Still a Hard-to-Reach Population? Using Social Media to Recruit Latino Gay Couples for an HIV Intervention Adaptation Study , 2014, Journal of medical Internet research.

[10]  Xiguang Li,et al.  A vaccine crisis in the era of social media , 2018 .

[11]  Christos Tjortjis,et al.  Social Media Types: introducing a data driven taxonomy , 2019, Computing.

[12]  Kumanan Wilson,et al.  Social Media and the Empowering of Opponents of Medical Technologies: The Case of Anti-Vaccinationism , 2013, Journal of medical Internet research.

[13]  Katherine A Bolton,et al.  Herd Immunity: Does Social Media Affect Adherence to the CDC Childhood Vaccination Schedule? , 2015 .

[14]  Neil Seeman,et al.  Assessing and responding in real time to online anti-vaccine sentiment during a flu pandemic. , 2010, Healthcare quarterly.

[15]  P. Rota,et al.  Health care-associated measles outbreak in the United States after an importation: challenges and economic impact. , 2011, The Journal of infectious diseases.

[16]  M. Paasivaara,et al.  Predicting 2016 US Presidential Election Polls with Online and Media Variables , 2016 .

[17]  Mark Dredze,et al.  Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance , 2015, PLoS Comput. Biol..

[18]  A. Caplan,et al.  The overlooked dangers of anti-vaccination groups' social media presence , 2017, Human vaccines & immunotherapeutics.

[19]  John S Brownstein,et al.  Measuring vaccine confidence: analysis of data obtained by a media surveillance system used to analyse public concerns about vaccines. , 2013, The Lancet. Infectious diseases.

[20]  Hua Wang,et al.  Text Mining and Real-Time Analytics of Twitter Data: A Case Study of Australian Hay Fever Prediction , 2018, HIS.

[21]  Chris J. Vargo,et al.  Geographic and demographic correlates of autism-related anti-vaccine beliefs on Twitter, 2009-15. , 2017, Social science & medicine.

[22]  H. Larson,et al.  Vaccine-criticism on the internet: new insights based on French-speaking websites. , 2015, Vaccine.

[23]  K. Mandl,et al.  Mapping information exposure on social media to explain differences in HPV vaccine coverage in the United States. , 2017, Vaccine.

[24]  Jiaoyan Chen,et al.  Forecasting smog-related health hazard based on social media and physical sensor , 2016, Information Systems.

[25]  P. Hotez,et al.  Public Health and Economic Consequences of Vaccine Hesitancy for Measles in the United States , 2017, JAMA pediatrics.

[26]  Henry A. Kautz,et al.  Deploying nEmesis: Preventing Foodborne Illness by Data Mining Social Media , 2016, AI Mag..

[27]  Stephen Shaoyi Liao,et al.  Leading Effect of Social Media for Financial Fraud Disclosure: A Text Mining Based Analytics , 2016, AMCIS.

[28]  Gary L. Kreps,et al.  The Power & Perspective of Mommy Bloggers: Formative Research with Social Media Opinion Leaders about HPV Vaccination , 2017, HICSS.

[29]  Panagiotis Takis Metaxas,et al.  The power of prediction with social media , 2013, Internet Res..

[30]  Patty Kostkova,et al.  Who is Spreading Rumours about Vaccines?: Influential User Impact Modelling in Social Networks , 2017, DH.

[31]  H. Carabin,et al.  The average cost of measles cases and adverse events following vaccination in industrialised countries , 2002, BMC public health.

[32]  R. M. Wolfe,et al.  Content and design attributes of antivaccination web sites. , 2002, JAMA.

[33]  Kaylan E. Stinson,et al.  Public Health Economic Burden Associated with Two Single Measles Case Investigations — Colorado, 2016–2017 , 2017, MMWR. Morbidity and mortality weekly report.

[34]  Todd Eaglin,et al.  A feasibility study on identifying drinking-related contents in Facebook through mining heterogeneous data , 2018, Health Informatics J..

[35]  J. Ward.  Rethinking the antivaccine movement concept: A case study of public criticism of the swine flu vaccine's safety in France. , 2016, Social science & medicine.

[36]  A. Kata.  Anti-vaccine activists, Web 2.0, and the postmodern paradigm--an overview of tactics and tropes used online by the anti-vaccination movement. , 2012, Vaccine.

[37]  J. Glanz,et al.  Addressing Parental Vaccine Concerns: Engagement, Balance, and Timing , 2015, PLoS biology.

[38]  Scott Counts,et al.  Understanding Anti-Vaccination Attitudes in Social Media , 2016, ICWSM.

[39]  Christos Tjortjis,et al.  Social Media Analytics, Types and Methodology , 2019, Learning and Analytics in Intelligent Systems.

[40]  Mark Dredze,et al.  Understanding Vaccine Refusal: Why We Need Social Media Now. , 2016, American journal of preventive medicine.

[41]  John S Brownstein,et al.  Publicly Available Online Tool Facilitates Real-Time Monitoring Of Vaccine Conversations And Sentiments. , 2016, Health affairs.

[42]  Keren Landsman,et al.  The Importance of Social Networking in a National Polio Vaccine Campaign , 2016, Pediatrics.

[43]  Kate Faasse,et al.  A comparison of language use in pro- and anti-vaccination comments in response to a high profile Facebook post. , 2016, Vaccine.

[44]  R. Cohen,et al.  The impact of the web and social networks on vaccination. New challenges and opportunities offered to fight against vaccine hesitancy. , 2016, Medecine et maladies infectieuses.

[45]  M. Moran,et al.  What makes anti-vaccine websites persuasive? A content analysis of techniques used by anti-vaccine websites to engender anti-vaccine sentiment , 2016 .

[46]  Christos Tjortjis,et al.  Social media prediction: a literature review , 2019, Multimedia Tools and Applications.

[47]  Mark Dredze,et al.  Examining Patterns of Influenza Vaccination in Social Media , 2017, AAAI Workshops.

[48]  Cynthia M. Lakon,et al.  Development of a Twitter-Based Intervention for Smoking Cessation that Encourages High-Quality Social Media Interactions via Automessages , 2015, Journal of medical Internet research.

[49]  L. Bode,et al.  See Something, Say Something: Correction of Global Health Misinformation on Social Media , 2018, Health communication.

[50]  H. D. de Melker,et al.  Emerging Blockininfectious Blockindiseases Blockin@bullet Blockin , 2022 .

[51]  S. Bull,et al.  Social media-delivered sexual health intervention: a cluster randomized controlled trial. , 2012, American journal of preventive medicine.

[52]  E. Brunson,et al.  The Impact of Social Networks on Parents’ Vaccination Decisions , 2013, Pediatrics.

[53]  Samarth Swarup,et al.  Semantic network analysis of vaccine sentiment in online social media. , 2017, Vaccine.

[54]  S. Omer,et al.  Go big and go fast--vaccine refusal and disease eradication. , 2013, The New England journal of medicine.

[55]  Pier Luigi Lopalco,et al.  The web and public confidence in MMR vaccination in Italy. , 2017, Vaccine.

[56]  M. Petticrew,et al.  Developing and evaluating complex interventions: the new Medical Research Council guidance , 2008, BMJ : British Medical Journal.