Social Media as a Source of Information for the Detection of Adverse Drug Reactions in Post-Marketing Surveillance: A Review

INTRODUCTION. With the development of the Internet and the increasing availability of social networks and fora, patients have received an opportunity to share their medication experiences online. According to the guidelines on Good Pharmacovigilance Practices, social media can be considered an important additional source of patient-derived information in post-marketing surveillance, but the effectiveness of their use in detecting adverse drug reactions (ADRs) is still being investigated.

AIM. This study aimed to analyse the results of relevant original studies and assess the potential of using social networks and online patient fora as a source of information on ADRs associated with the use of medicinal products.

DISCUSSION. Published studies indicate that posts on  social  networks  and  patient  fora  describe  both  minor and serious ADRs, including new ADRs. The relevance of social media as a source of information about the safety of a medicinal product varies depending on several factors, including the medicinal product class and time on the market, as well as the platform demographics. Young users (18–44 years) are interested in online discussions about medicinal products for mental and reproductive system disorders. Users aged 45–64 years tend to discuss the use of medicinal products for chronic pain (including muscle pain), menopause, and gastritis. Discussions among users over 65 years old predominantly focus on medicinal products for diabetes, heart conditions, and muscle pain. People are much more likely to describe ADRs associated with the use of medicinal products for orphan diseases and cancer on fora for patients than on social networks in general, and vice versa for ADRs associated with the use of medicinal products for mental disorders. In addition, social media may be of interest as a source of information about cases of overdose, misuse and off-label use of medicinal products, and use of medicinal products during pregnancy and lactation.

CONCLUSIONS. Social media can be a source of valuable information about the safety of medicinal products and the impact of ADRs on the quality of patients’ lives. Marketing authorisation holders can obtain new information about the safety of medicinal products by extending their  safety  monitoring  strategies  to  include social media. Nevertheless, since the relevance of a particular social network or patient forum for the detection of ADR cases varies considerably, a preliminary assessment is necessary to ascertain the presence of information on the medicinal product of interest.

1. Banovac M, Candore G, Slattery J, Houÿez F, Haerry D, Genov G, Arlett P. Patient reporting in the EU: analysis of EudraVigilance data. Drug Saf. 2017;40(7):629–45. https://doi.org/10.1007/s40264-017-0534-1

2. Valinciute-Jankauskiene A, Kubiliene L. Adverse drug reaction reporting by patients in 12 European countries. Int J Environ Res Public Health. 2021;18(4):1507. https://doi.org/10.3390/ijerph18041507

3. Hjollund NHI. Fifteen years’ use of patient-reported outcome measures at the group and patient levels: trend analysis. J Med Internet Res. 2019;21(9):e15856. https://doi.org/10.2196/15856

4. Adopo D, Daynes P, Benkebil M, Debs A, Jonville-Berra AP, Polard E, et al. Patient involvement in pharmacovigilance: determinants and evolution of reporting from 2011 to 2020 in France. Eur J Clin Pharmacol. 2023;79(2):229–36. https://doi.org/10.1007/s00228-022-03422-y

5. Sinclair M, Lagan BM, Dolk H, McCullough JEM. An assessment of pregnant women’s knowledge and use of the Internet for medication safety information and purchase. J Adv Nurs. 2018;74(1):137–47. https://doi.org/10.1111/jan.13387

6. Pierce CE, Bouri K, Pamer C, Proestel S, Rodriguez HW, Van Le H, et al. Evaluation of Facebook and Twitter monitoring to detect safety signals for medical products: an analysis of recent FDA safety alerts. Drug Saf. 2017;40(4):317–31. https://doi.org/10.1007/s40264-016-0491-0

7. Freifeld CC, Brownstein JS, Menone CM, Bao W, Filice R, Kass-Hout T, Dasgupta N. Digital drug safety surveillance: monitoring pharmaceutical products in Twitter. Drug Saf. 2014;37(5):343–50. https://doi.org/10.1007/s40264-014-0155-x

8. Brajovic S, Blaser DA, Zisk M, Caligtan C, Okun S, Hall M, Pamer CA. Validating a framework for coding patient-reported health information to the Medical Dictionary for Regulatory Activities Terminology: an evaluative study. JMIR Med Inform. 2018;6(3):e42. https://doi.org/10.2196/medinform.9878

9. Dietrich J, Gattepaille LM, Grum BA, Jiri L, Lerch M, Sartori D, Wisniewski A. Adverse events in Twitter-development of a benchmark reference dataset: results from IMI WEB-RADR. Drug Saf. 2020;43(5):467–78. https://doi.org/10.1007/s40264-020-00912-9

10. Gattepaille LM, Hedfors Vidlin S, Bergvall T, Pierce CE, Ellenius J. Prospective evaluation of adverse event recognition systems in Twitter: results from the Web-RADR project. Drug Saf. 2020;43(8):797–808. https://doi.org/10.1007/s40264-020-00942-3

11. Caster O, Dietrich J, Kürzinger ML, Lerch M, Maskell S, Norén GN, et al. Assessment of the utility of social media for broad-ranging statistical signal detection in pharmacovigilance: results from the WEB-RADR project. Drug Saf. 2018;41(12):1355–69. https://doi.org/10.1007/s40264-018-0699-2

12. Brosch S, de Ferran AM, Newbould V, Farkas D, Lengsavath M, Tregunno P. Establishing a framework for the use of social media in pharmacovigilance in Europe. Drug Saf. 2019;42(8):921–30. https://doi.org/10.1007/s40264-019-00811-8

13. van Stekelenborg J, Ellenius J, Maskell S, Bergvall T, Caster O, Dasgupta N, et al. Recommendations for the use of social media in pharmacovigilance: lessons from IMI WEB-RADR. Drug Saf. 2019;42(12):1393–407. https://doi.org/10.1007/s40264-019-00858-7

14. Audeh B, Bellet F, Beyens MN, Lillo-Le Louët A, Bousquet C. Use of social media for pharmacovigilance activities: key findings and recommendations from the Vigi4Med project. Drug Saf. 2020;43(9):835–51. https://doi.org/10.1007/s40264-020-00951-2

15. Karapetiantz P, Bellet F, Audeh B, Lardon J, Leprovost D, Aboukhamis R, et al. Descriptions of adverse drug reactions are less informative in forums than in the French Pharmacovigilance Database but provide more unexpected reactions. Front Pharmacol. 2018;9:439. https://doi.org/10.3389/fphar.2018.00439

16. Audeh B, Calvier FE, Bellet F, Beyens MN, Pariente A, Lillo-Le Louet A, Bousquet C. Pharmacology and social media: Potentials and biases of web forums for drug mention analysis-case study of France. Health Informatics J. 2020;26(2):1253–72. https://doi.org/10.1177/1460458219865128

17. Tutubalina E, Alimova I, Miftahutdinov Z, Sakhovskiy A, Malykh V, Nikolenko S. The Russian drug reaction corpus and neural models for drug reactions and effectiveness detection in user reviews. Bioinformatics. 2021;37(2):243–9. https://doi.org/10.1093/bioinformatics/btaa675

18. Zhou Z, Hultgren KE. Complementing the US Food and Drug Administration Adverse Event Reporting System with adverse drug reaction reporting from social media: comparative analysis. JMIR Public Health Surveill. 2020;6(3):e19266. https://doi.org/10.2196/19266

19. Duval FV, Silva FABD. Mining in Twitter for adverse events from malaria drugs: the case of doxycycline. Cad Saude Publica. 2019;35(5):e00033417 [In English, Portuguese, Spanish]. https://doi.org/10.1590/0102-311X00033417

20. Wessel D, Pogrebnyakov N. Using social media as a source of real-world data for pharmaceutical drug development and regulatory decision making. Drug Saf. 2024;47(5):495–511. https://doi.org/10.1007/s40264-024-01409-5

21. Sadah SA, Shahbazi M, Wiley MT, Hristidis V. Demographic-based content analysis of web-based health-related social media. J Med Internet Res. 2016;18(6):e148. https://doi.org/10.2196/jmir.5327

22. Park S, Choi SH, Song YK, Kwon JW. Comparison of online patient reviews and national pharmacovigilance data for tramadol-related adverse events: comparative observational study. JMIR Public Health Surveill. 2022;8(1):e33311. https://doi.org/10.2196/33311

23. Micale C, Golder S, O’Connor K, Weissenbacher D, Gross R, Hennessy S, Gonzalez-Hernandez G. Patient-reported reasons for antihypertensive medication change: a quantitative study using social media. Drug Saf. 2024;47(1):81–91. https://doi.org/10.1007/s40264-023-01366-5

24. Oyebode O, Orji R. Identifying adverse drug reactions from patient reviews on social media using natural language processing. Health Informatics J. 2023;29(1):14604582221136712. https://doi.org/10.1177/14604582221136712

25. Schück S, Roustamal A, Gedik A, Voillot P, Foulquié P, Penfornis C, Job B. Assessing patient perceptions and experiences of paracetamol in France: infodemiology study using social media data mining. J Med Internet Res. 2021;23(7):e25049. https://doi.org/10.2196/25049

26. Natter J, Michel B. Memantine misuse and social networks: a content analysis of Internet self-reports. Pharmacoepidemiol Drug Saf. 2020;29(9):1189–93. https://doi.org/10.1002/pds.5070

27. Rezaallah B, Lewis DJ, Pierce C, Zeilhofer HF, Berg BI. Social media surveillance of multiple sclerosis medications used during pregnancy and breastfeeding: content analysis. J Med Internet Res. 2019;21(8):e13003. https://doi.org/10.2196/13003

28. Golder S, Chiuve S, Weissenbacher D, Klein A, O’Connor K, Bland M, et al. Pharmacoepidemiologic evaluation of birth defects from health-related postings in social media during pregnancy. Drug Saf. 2019;42(3):389–400. https://doi.org/10.1007/s40264-018-0731-6

29. Bhattacharya M, Snyder S, Malin M, Truffa MM, Marinic S, Engelmann R, Raheja RR. Using social media data in routine pharmacovigilance: a pilot study to identify safety signals and patient perspectives. Pharm Med. 2017;31:167–74. https://doi.org/10.1007/s40290-017-0186-6

30. Powell G, Kara V, Painter JL, Schifano L, Merico E, Bate A. Engaging patients via online healthcare fora: three pharmacovigilance use cases. Front Pharmacol. 2022;13:901355. https://doi.org/10.3389/fphar.2022.901355

31. Coloma PM, Becker B, Sturkenboom MC, van Mulligen EM, Kors JA. Evaluating social media networks in medicines safety surveillance: two case studies. Drug Saf. 2015;38(10):921–30. https://doi.org/10.1007/s40264-015-0333-5

32. Yeleswarapu S, Rao A, Joseph T, Saipradeep VG, Srinivasan R. A pipeline to extract drug-adverse event pairs from multiple data sources. BMC Med Inform Decis Mak. 2014;14:13. https://doi.org/10.1186/1472-6947-14-13

33. O’Donovan B, Rodgers RM, Cox AR, Krska J. Identifying and managing adverse drug reactions: qualitative analysis of patient reports to the UK yellow card scheme. Br J Clin Pharmacol. 2022;88(7):3434–46. https://doi.org/10.1111/bcp.15263

34. Klein AZ, Banda JM, Guo Y, Schmidt AL, Xu D, Amaro JIF, et al. Overview of the 8th Social Media Mining for Health Applications (#SMM4H) shared tasks at the AMIA 2023 annual symposium. medRxiv. 2023.11.06.23298168. https://doi.org/10.1101/2023.11.06.23298168