The use of the social media by people around the globe is widespread. This chapter discusses the contribution which social media research can offer to pharmacovigilance and medicinal product risk communication research. While the use of the social media itself and the development of social media strategies are important topics for research, this chapter focusses on the methods of social media listening and crowdsourcing of information, and provides examples of their utility. It highlights opportunities, limitations, challenges as well as ethical and legal aspects that need to be addressed for future research.
This is a preview of subscription content, access via your institution.
Tax calculation will be finalised at checkout
Purchases are for personal use onlyLearn about institutional subscriptions
Abdellaoui R, Schück S, Texier N, Burgun A (2017) Filtering entities to optimize identification of adverse drug reaction from social media: how can the number of words between entities in the messages help? JMIR Public Health Surveill 3(2):e36. Accessible at: https://publichealth.jmir.org/2017/2/e36/
Abou Taam M, Rossard C, Cantaloube L, Bouscaren N, Roche G, Pochard L, Montastruc F, Herxheimer A, Montastruc JL, Bagheri H (2014) Analysis of patients’ narratives posted on social media websites on benfluorex’s (Mediator®) withdrawal in France. J Clin Pharm Ther. 39:53–55. Accessible at: https://onlinelibrary.wiley.com/doi/abs/10.1111/jcpt.12103
Adams S, Schiffers P (2017) Co-constructed health narratives during a ‘media event’: the case of the first Dutch Twitter heart operation. Digit Health 3:2055207617712046. Accessible at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6001202/.
Albarqouni L, Hoffmann T, McLean K, Price K, Glasziou P (2019). Role of professional networks on social media in addressing clinical questions at general practice: a cross-sectional study of general practitioners in Australia and New Zealand. BMC Fam Pract 20(1):43. Accessible at: https://bmcfampract.biomedcentral.com/articles/10.1186/s12875-019-0931-x
Alvaro N, Conway M, Doan S, Lofi C, Overington J, Collier N (2015) Crowdsourcing Twitter annotations to identify first-hand experiences of prescription drug use. J Biomed Inform 58:280–287. Accessible at: https://www.sciencedirect.com/science/article/pii/S1532046415002415?via%3Dihub
Anderson LS, Bell HG, Gilbert M, Davidson JE, Winter C, Barratt MJ, Win B, Painter JL, Menone C, Sayegh J, Dasgupta N (2017) Using social listening data to monitor misuse and nonmedical use of bupropion: a content analysis. JMIR Public Health Surveill 3:e6
Bahk C, Goshgarian M, Donahue K, Freifeld CC, Menone CM et al (2015) Increasing patient engagement in pharmacovigilance through online community outreach and mobile reporting applications: an analysis of adverse event reporting for the Essure device in the US. Pharm Med 29:331–341
Barry F (2014) Pfizer: how Facebook can ‘unblind’ a clinical trial. Outsourcing-pharma.com. June 9. Accessible at: https://www.outsourcing-pharma.com/Article/2014/06/09/Pfizer-How-Facebook-can-unblind-a-clinical-trial#
Bian J, Topaloglu U, Yu F (2012) Towards large-scale Twitter mining for drug-related adverse events. SHB12. Accessible at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5619871/
Bousquet C, Audeh B, Bellet F, Lillo-LeLouet A (2018) Comment on “Assessment of the utility of social media for broad-ranging statistical signal detection in pharmacovigilance: results from the WEB-RADR Project”. Drug Saf 41:1371–1373. Accessible at: https://link.springer.com/article/10.1007%2Fs40264-018-0747-y
Carbonell P, Mayer MA, Bravo A (2015) Exploring brand-name drug mentions on Twitter for pharmacovigilance. Stud Health Technol Inform, 210. Accessible at: https://www.ncbi.nlm.nih.gov/pubmed/?term=25991101
Caster O, Dietrich J, Kurzinger ML, Lerch M, Maskell S, Noren GN, Tcherny-Lessenot S, Vroman B, Wisniewski A, van Stekelenborg J (2018) Assessment of the utility of social media for broad-ranging statistical signal detection in pharmacovigilance: results from the WEB-RADR project. Drug Saf 41:1355–1369
Charlie AM, Gao Y, Heller SL (2018) What do patients want to know? Questions and concerns regarding mammography expressed through social media. J Am Coll Radiol 15(10):1478–1486. Accessible at: https://www.jacr.org/article/S1546-1440(17)31170-5/fulltext
Chary M, Genes N, McKenzie A, Manini AF (2013) Leveraging social networks for toxicovigilance. J Med Toxicol 9:184–191. Accessible at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657021/
Chen X, Faviez C, Schuck S, Lillo-Le-Louët A, Texier N et al (2018) Mining patients’ narratives in social media for pharmacovigilance: adverse effects and misuse of methylphenidate. Front Pharmacol 9:541
Cocos A, Fiks AG, Masino AJ (2017) Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts. J Am Med Inform Assoc 24:813–821. Accessible at: https://academic.oup.com/jamia/article-abstract/24/4/813/3041102?redirectedFrom=fulltext
Comfort S, Perena S, Hudson Z, Dorrell D, Meireis S, Nagarajan M, Ramakrishnan C, Fine J (2018) Sorting through the safety data haystack: using machine learning to identify individual case safety reports in social-digital media. Drug Saf 41:579–590. Accessible at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5966485/
Convertino I, Ferraro S, Blandizzi C, Tuccori M (2018) The usefulness of listening social media for pharmacovigilance purposes: a systematic review. Expert Opin Drug Saf 17:1081–1093. Accessible at: https://www.ncbi.nlm.nih.gov/pubmed/?term=30285501
Demner-Fushman D, Elhadad N (2016) Aspiring to unintended consequences of natural language processing: a review of recent developments in clinical and consumer-generated text processing. Yearb Med Inform 10:224–233. Accessible at: https://www.ncbi.nlm.nih.gov/pubmed/?term=27830255
Dizon D, Graham D, Thompson M, Johnson L, Johnston C et al (2012) Practical guidance: the use of social media in oncology practice. Bus Oncol 8(5):e113–e124
Donahue M (2012) Patient recruitment via social media: lessons learned. Pharm Exec. February 13. Accessible at: http://www.pharmexec.com/patient-recruitment-social-media-lessons-learned
Edwards IR, Lindquist M (2011) Social media and networks in pharmacovigilance: boon or bane? Drug Saf 34:267–271. Accessible at: https://www.ncbi.nlm.nih.gov/pubmed/?term=21417499
Emadzadeh E, Sarker A, Nikfarjam A, Gonzalez G (2018) Hybrid semantic analysis for mapping adverse drug reaction mentions in tweets to medical terminology. AMIA Annu Symp Proc 16:679–688. Accessible at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977584/
Eshleman R, Singh R (2016) Leveraging graph topology and semantic context for pharmacovigilance through twitter-streams. BMC Bioinformatics 17(Suppl 13):335. Accessible at: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1220-5
Falzon D et al (2016) Digital health for the End TB Strategy: developing priority products and making them work. Eur Respir J 26(48):29–45
Freifeld CC, Brownstein JS, Menone CM, Bao W, Filice R, Kass-Hout T, Dasgupta N (2014) Digital drug safety surveillance: monitoring pharmaceutical products in twitter. Drug Saf 37:343–350. Accessible at: https://link.springer.com/article/10.1007%2Fs40264-014-0155-x
Ghosh R, Lewis D (2015) Aims and approaches of Web-RADR: a consortium ensuring reliable ADR reporting via mobile devices and new insights from social media. Exp Opin Drug Saf 14:1845–1853. Accessible at: https://www.tandfonline.com/doi/abs/10.1517/14740338.2015.1096342?journalCode=ieds20
Golder S, Norman G, Loke YK (2015) Systematic review on the prevalence, frequency and comparative value of adverse events data in social media. Br J Clin Pharmacol 80(4):878–888. Accessible at: https://bpspubs.onlinelibrary.wiley.com/doi/full/10.1111/bcp.12746
Graff SL, Close J, Cole S, Matt-Amaral L, Beg R, Markham MJ (2018) Impact of closed Facebook group participation on female hematology/oncology physicians. J Oncol Pract 4(12):e758–e769
Grajaless F III, Sheps S, Ho K, Novak-Lauscher H, Eysenbach G (2014) Social media: a review and tutorial of applications in medicine and health care. J Med Internet Res 16(2):e13
Gupta S, Pawar S, Ramrakhiyani N, Palshikar GK, Varma V (2018) Semi-supervised recurrent neural network for adverse drug reaction mention extraction. BMC Bioinformatics 19(Suppl 8):212. Accessible at: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2192-4
International Telecommunication Union (ICU). ICT Facts and Figures The World in 2015. Accessible at: http://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2015.pdf
Jiang K, Chen T, Calix RA, Bernard GR (2018a) Identifying consumer health terms of side effects in Twitter posts. Stud Health Technol Inform 251:273–276. Accessible at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6041256/
Jiang K, Feng S, Song Q, Calix RA, Gupta M, Bernard GR (2018b) Identifying tweets of personal health experience through word embedding and LSTM neural network. BMC Bioinformatics 19(Suppl 8):210. Accessible at: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2198-y
Kang GJ, Ewing-Nelson SR, Mackey L, Schlitt JT, Marathe A, et al (2017) Semantic network analysis of vaccine sentiment in online social media. Vaccine 35:3621–3638. Accessible at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5548132/
Karapetiantz P, Bellet F, Audeh B, Lardon J, Leprovost D et al (2018) Descriptions of adverse drug reactions are less informative in forums than in the French pharmacovigilance database but provide more unexpected reactions. Front Pharmacol 9:439
Keller MS, Mosadeghi S, Cohen ER, Kwan J, Spiegel BMR (2018) Reproductive health and medication concerns for patients with inflammatory bowel disease: thematic and quantitative analysis using social listening. J Med Internet Res 20(6):e206. Accessible at: https://www.jmir.org/2018/6/e206/
Kheloufi F, Default A, Blin O, Micallef J (2017) Investigating patient narratives posted on Internet and their informativeness level for pharmacovigilance purpose: The example of comments about statins. Therapie 72:483–490. Accessible at: https://www.ncbi.nlm.nih.gov/pubmed/?term=28065444
Knezevic MZ, Bivolarevic IC, Peric TS, Jankovic SM (2011) Using Facebook to increase spontaneous reporting of adverse drug reactions. Drug Saf 34:351–352
Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P (2010) A side effect resource to capture phenotopic effects of drugs. Mol Syst Biol 6:343. Accessible at: https://www.embopress.org/doi/full/10.1038/msb.2009.98
Kuhn M, Letunic I, Jensen LJ, Bork P (2016) The SIDER database of drugs and side effects. Nucleic Acids Res 44(D1):D1075–D1079. Accessible at: https://academic.oup.com/nar/article/44/D1/D1075/2502602
Kurzinger ML, Schuck S, Texier N, Abdellaoui R, Faviez C, Pouget J, Zhang L, Tcherny-Lessenot S, Lin S, Juhaeri J (2018) Web-based signal detection using medical forums data in France: comparative analysis. J Med Internet Res 20:e10466. Accessible at: https://www.jmir.org/2018/11/e10466/
Lardon J, Abdellaoui R, Bellet F, Asfari H (2015) Adverse drug reaction identification and extraction in social media: a scoping review. J Med Internet Res 17:e171. Accessible at: https://www.ncbi.nlm.nih.gov/pubmed/?term=26163365
Lardon J, Bellet F, Aboukhamis R, Asfari H, Souvignet J, Jaulent MC, Beyens MN, Lillo-LeLouet A, Bousquet C (2018) Evaluating Twitter as a complementary data source for pharmacovigilance. Exp Opin Drug Saf 17:763–774. Accessible at: https://www.tandfonline.com/doi/abs/10.1080/14740338.2018.1499724?journalCode=ieds20
Larkin M (2014) Social media for pharma: an expert’s view. Elsevier. December 2. Accessible at: http://www.elsevier.com/connect/social-media-for-pharma-an-expertsview
Lengsavath M, Dal Pra A, de Ferran AM, Brosch S, Härmark L, Newbould V, Goncalves S (2017) Social media monitoring and adverse drug reaction reporting in pharmacovigilance: an overview of the regulatory landscape. Ther Innov Regul Sci 51(1):125–131
Lipset C (2014) Engage with research participants about social media. Nat Med 20:231
Liu J, Wang G (2018) Pharmacovigilance from social media: an improved random subspace method for identifying adverse drug events. Int J Med Inform 117:33–43. Accessible at: https://www.sciencedirect.com/science/article/abs/pii/S1386505618304416?via%3Dihub
Liu J, Zhao S, Zhang X (2016) An ensemble method for extracting adverse drug events from social media. Artif Intell Med 70:62–76. Accessible at: https://linkinghub.elsevier.com/retrieve/pii/S0933-3657(15)30037-3
Liu J, Zhao S, Wang G (2018) SSEL-ADE: a semi-supervised ensemble learning framework for extracting adverse drug events from social media. Artif Intell Med 84:34–49. Accessible at: https://www.sciencedirect.com/science/article/pii/S0933365717301847?via%3Dihub
Medical Dictionary for Regulatory Activities (MedDRA). Accessible at: http://www.meddra.org
Naik P, Umrath T, van Stekelenborg J, Ruben R, Abdul-Karim N, et al (2015) Regulatory definitions and good pharmacovigilance practices in social media: challenges and recommendations. Ther Innov Regul Sci 49:840–851. Accessible at: https://journals.sagepub.com/doi/abs/10.1177/2168479015587362?rfr_dat=cr_pub%3Dpubmed&url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&journalCode=dijc
Nikfarjam A, Sarker A, O’Connor K, Ginn R, Gonzalez G (2015) Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. J Med Am Inform Assoc 22:671–681
O’Connor A, Jackson L, Goldsmith L, Skirton H (2014) Can I get a retweet please?: health research recruitment and the Twittersphere. J Adv Nurs 70:599–609
Park HA, Jung H, On J, Park SK, Kang H (2018) Digital epidemiology: use of digital data collected for non-epidemiological purposes in epidemiological studies. Healthc Inform Res 24:253–262. Accessible at: https://www.ncbi.nlm.nih.gov/pubmed/?term=30443413
Patel R, Belousov M, Jani M, Dasgupta N, Winokur C, Nenandic G, Dixon WG (2018) Frequent discussion of insomnia and weight gain with glucocorticoid therapy: an analysis of Twitter posts. NPJ Digit Med, 1. Accessible at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6364798/
PatientsLikeMe. About us. patientslikeme.com. 2019. And: Okun S, Goodwin K (2017) Building a learning health community: By the people, for the people. Learn Health Sys 1:e10028. Both accessible at: https://www.patientslikeme.com/about
Peacock E (2014) Global forum special section: transforming recruitment for clinical trials via patient social networks. DIA. October 1. Accessible at: http://www.diaglobal.org/en/resources/news#article=65afd337-6fc3-4abf-8d43-797172fc1314
Peacock E (2015) Engaging patient social networks in clinical trials and burden of disease studies. Drug Information Association (DIA). October 1
Perrin A (2015) Social media usage: 2005–2015. Pew Research Center. Accessible at: http://www.pewinternet.org/2015/10/08/social-networking-usage-2005-2015/
Pierce CE, Bouri K, Pamer C, Proestel S, Rodriguez HW, Ven Le H, Freifeld CC, Brownstein JS, Walderhaug M, Edwards IR, Dasgupta N (2017) Evaluation of Facebook and Twitter monitoring to detect safety signals for medical products: an analysis of recent FDA safety alerts. Drug Saf 40:317–331. Accessible at: https://link.springer.com/article/10.1007%2Fs40264-016-0491-0
Powell GE, Seifert HA, Reblin T, Burstein PJ, Blowers J et al (2015) Social media listening for routine post-marketing safety surveillance. Drug Saf 39:443–454
Rees S, Mian S, Grabowski N (2018) Using social media in safety signal management: is it reliable? Ther Adv Drug Saf 9:591–599. Accessible at: https://www.ncbi.nlm.nih.gov/pubmed/?term=30283627
Rothman M, Gnanaskathy A, Wicks P, Papadopoulos E (2015) Can we use social media to support content validity of patient-reported outcome instruments in medial product development? Value Health 18:1–4
Sarker A, Ginn R, Nikfarjam A, O’Connor K, Smith K et al (2015) Utilizing social media data for pharmacovigilance: a review. J Biomed Inform 54:202–212
Sarker A, Nikfarjam A, Gonzalez G (2016) Social media mining shared task workshop. Pac Symp Biocomput 21:581–592. Accessible at: https://www.worldscientific.com/doi/abs/10.1142/9789814749411_0054
Segura-Bedmar I, Martinez P, Revert R, Moreno-Schneider J (2015) Exploring Spanish health social media for detecting drug effects. BMC Med Inform Decis Mak 15(Suppl 2):S6. Accessible at: https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/1472-6947-15-S2-S6
Sharpe T (2014) Global forum special section: patient perspective on social media. Drug Information Association (DIA). October 1. Accessible at: http://www.diaglobal.org/en/resources/news#article=7b3df92f-82b7-443a-a759-ad46b203f0b3
Sloane R, Osanlou O, Lewis D, Bollegala D, Maskell S, Pirmohamed M (2015) Social media and pharmacovigilance: a review of the opportunities and challenges. Br J Clin Pharmacol 80:910–920. Accessible at: https://www.ncbi.nlm.nih.gov/pubmed/?term=26147850
Smart Patients, Inc. (2015) Accessible at: https://www.smartpatients.com/about
Smith M, Benattia I (2016) The patient’s voice in pharmacovigilance: pragmatic approaches to building a patient-centric drug safety organization. Drug Saf 39:779–785
Snipes K (2015) Using social media and digital media to increase patient recruitment and retention. Clinical Leader. June 15
Stergiopoulos S (2014) Global forum special section: social listening to enhance clinical research. Drug Information Association (DIA). October 1. Accessible at: http://www.diaglobal.org/en/resources/news#article=c0736ec3-2280-4261-b621-f756d3a4bf6e
Sullivan R, Sarker A, O’Connor K, Goodin A, Karlsrud M, Gonzalez G (2016) Finding potentially unsafe nutritional supplements from user reviews with topic modeling. Pac Symp Biocomput 21:528–539
Thompson M (2014) Social media in clinical trials. ASCO p E101. Accessible at: https://www.researchgate.net/publication/262608279_Social_Media_in_Clinical_Trials
Tricco AC, Zarin W, Lillie E, Jeblee S, Warren R, Khan PA, Robson R, Hirst G, Straus SE (2018) Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review. BMC Med Inform Decis Mak 18:38. Accessible at: https://www.ncbi.nlm.nih.gov/pubmed/?term=29898743
Tufts Center for the Study of Drug Development (2014) Industry usage of social and digital media communities in clinical research. White Paper, Boston. Accessible at: http://csdd.tufts.edu/files/uploads/TCSDD_Social_Media_Final.pdf
Wong A, Plasek JM, Montecalvo SP, Zhou L (2018) Natural language processing and its implications for the future of medication safety: a narrative review of recent advances and challenges. Pharmacotherapy 38:822–841. Accessible at: https://www.ncbi.nlm.nih.gov/pubmed/?term=29884988
Wu H, Fang H, Stanhope SJ (2013) Exploiting online discussions to discover unrecognized drug side effects. Methods Inf Med 52:152–159
The authors thank Lorna M Woods at the School of Law, University of Essex, United Kingdom for the review of this appendix.
Editors and Affiliations
Appendix 11.1: Legal Aspects Relevant to Internet-Based and Social Media Research
Researchers making use of data from the internet and the social media need to consider legal aspects. The applicable law will vary depending on where relevant actors, e.g. internet and social media users and researchers, are located. The relevant rules are usually those of the country where researchers are based. The European Union (EU) data protection rules, found in General Data Protection Regulation (GDPR) (Regulation (EU) 2016/679), can have “extra-territorial effect”—that is they bind researchers outside the EU when they target those within the EU.
1.1 Types of Law to Consider
In addition to adhereing to legislation on personal data protection, confidentiality and privacy, other legal aspect may be of relevance to the research project. Other legal concerns include contracts (e.g. with data vendors), intellectual property (e.g. onwership of digital content, reproduction and transfer rights, ownership of algortihms developed), sector-specific regulation (e.g. medical product marketers), as well as civil and criminal law (e.g. stalking, bullying, etc.).
1.2 Personal Data Protection Law
Personal data protection law—discussed here in more detail as the most relevant law to consider for internet-based and social media research related to health matters—does in general not prohibit the processing of data, but it lays down conditions for when, on what basis and how the processing of personal data should take place, and it gives enforceable rights to persons who are data subjects. Reference is made here to the EU GDPR, which is recognised by many—consumer organisations notably too—as a global standard. There, personal data are defined as any information relating to an identified or identifiable natural person. Researchers will often work with data that have been pseudo-anonymised by the data provider. That means that the data subject is not identified but there can still be a risk of possibly identifying the person through combining data or using additional information. This is particularly a risk when a patient has a rare disease. Where however information is truly anonymous, i.e. where the information does not relate to an identified or identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable, data protection legislation is not necessary to be applied. Statistics on the number and the length of visits of people on a website, stratified by country, age and sex, are examples of data likely to be anonymous data.
1.3 Grounds for Processing of Personal Data Relevant to Health Research
The EU GDPR specifies the grounds on which personal data may be processed—consent, performance of a contract, performance of a legal obligation, protecting the vital interests of the data subject, necessary for the performance of a task in the public interest, and the legitimate interests of the processor (subject to fundamental interests of the data subject). The EU GDPR also specifies strict rules as to what consent means. The EU GDPR prohibits the processing for special categories of personal data, including ethnic data, genetic and biometric data for the purpose of uniquely identifying a natural person, as well as data concerning health, sex life and sexual orientation. Such data are however allowed to be processed on defined exempting grounds, which include:
explicit consent by the data subject has been given; or
the personal data have manifestly been made public by the data subject; or
the data processing is necessary for the purposes of preventive or occupational medicine and provision of care, whether for an individual or populations; or
processing is necessary for reasons of public health, including ensuring high quality and safety of healthcare, medicinal products or medical devices.
These exemptions can be given for medicinal product risk communication research making use of data from the internet and the social media for understanding, planning, evaluating or improving communication. For example, patients may have identified themselves in comments on websites or publically accessible social media posts, or patients of a closed social media group may have given consent for their data to be used for the purpose of such research, to, e.g. identify their risk perceptions or questions for the safe use of medicines. Where patients publish their information under a pseudonym, researchers should not make attempts to identify that person through combining data, but may attempt to contact them if needed for a specific research project.
1.4 Principles for the Processing of Personal Data
Where the processing of personal data is allowed, the EU GDPR requires the data processing (i.e. collection, recording, organisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction) to be:
lawful, fair and transparent in relation to the data subject (principle of lawfulness, fairness and transparency);
for the specified purpose only (principle of purpose limitation);
adequate, relevant and limited to what is needed (principle of data minimisation);
based on accurate data (principle of accuracy);
performed in a way that permits identification of data subjects for no longer than is necessary (principle of storage limitation);
secure, which includes that the data should be protected against unauthorised or unlawful processing and accidental loss, destruction or damage (principle of integrity and confidentiality).
1.5 Rights of Data Subjects
As mentioned before, data protection law gives enforceable rights to persons who are data subjects towards the data controller. When planning research, the protocol needs to guarantee the following rights of data subjects, either because locally applicable legislation requires this or because it can be considered ethical good research practice:
right of access to the data subject’s data and information on the conditions of data processing;
right to rectification in order to correct or complete data;
right to erasure of data, i.e. the right to be forgotten;
right to restriction of processing;
right to data portability, i.e. to obtain the data in a readable format and to transfer them to another data controller; and
right to object to data processing at any time.
The rights of data subjects—here the users of social media—may be limited in respect of processing for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes. This will be specified by each EU member state (which could mean that the position will be different across member states) and must be subject to safeguards—again these will be specified by each member state.
1.6 Concluding Remarks
Researchers making use of data from the internet and the social media need to consider various types of law applicable in the given jurisdictions of all actors involved. Researchers need to in particular adhere to personal data protection, confidentiality and privacy legislation and are accountable in this respect towards data subjects. In jurisdictions where such legislation does not exist, the principles presented here can be considered good research practice. Research protocols and data processing need to be designed accordingly (Woods 2017). Regularly updated guidance on the EU GPRD is provided by the European Data Protection Board (EDPB) (European Data Protection Board (EDPB) 2018).
European Data Protection Board (EDPB) (2018) [guidance documents published on website]. Brussels: EDPB; Accessible at: https://edpb.europa.eu/edpb_en.
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Official Journal of the European Union; 4 May 2016: L 119/1-88.
Woods LM (2017) Legal considerations relevant to social media and health [lecture]. Pharmacovigilance and social media [training course on 15 October 2017]. Liverpool: 17th Annual Meeting of the International Society of Pharmacovigilance; 15-18 October 2017.
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Dasgupta, N., Winokur, C., Pierce, C. (2020). Social Media Research. In: Bahri, P. (eds) Communicating about Risks and Safe Use of Medicines. Adis, Singapore. https://doi.org/10.1007/978-981-15-3013-5_11
Publisher Name: Adis, Singapore
Print ISBN: 978-981-15-3012-8
Online ISBN: 978-981-15-3013-5