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Social Media Listening for Routine Post-Marketing Safety Surveillance

Author

Listed:
  • Gregory E. Powell

    (GlaxoSmithKline)

  • Harry A. Seifert

    (GlaxoSmithKline Vaccines)

  • Tjark Reblin

    (GlaxoSmithKline)

  • Phil J. Burstein

    (GlaxoSmithKline)

  • James Blowers

    (GlaxoSmithKline)

  • J. Alan Menius

    (GlaxoSmithKline)

  • Jeffery L. Painter

    (GlaxoSmithKline)

  • Michele Thomas

    (Blue Zone Industries)

  • Carrie E. Pierce

    (Epidemico Inc.)

  • Harold W. Rodriguez

    (Epidemico Inc.)

  • John S. Brownstein

    (Epidemico Inc.)

  • Clark C. Freifeld

    (Epidemico Inc.)

  • Heidi G. Bell

    (Zero Chaos)

  • Nabarun Dasgupta

    (Epidemico Inc.)

Abstract

Introduction Post-marketing safety surveillance primarily relies on data from spontaneous adverse event reports, medical literature, and observational databases. Limitations of these data sources include potential under-reporting, lack of geographic diversity, and time lag between event occurrence and discovery. There is growing interest in exploring the use of social media (‘social listening’) to supplement established approaches for pharmacovigilance. Although social listening is commonly used for commercial purposes, there are only anecdotal reports of its use in pharmacovigilance. Health information posted online by patients is often publicly available, representing an untapped source of post-marketing safety data that could supplement data from existing sources. Objectives The objective of this paper is to describe one methodology that could help unlock the potential of social media for safety surveillance. Methods A third-party vendor acquired 24 months of publicly available Facebook and Twitter data, then processed the data by standardizing drug names and vernacular symptoms, removing duplicates and noise, masking personally identifiable information, and adding supplemental data to facilitate the review process. The resulting dataset was analyzed for safety and benefit information. Results In Twitter, a total of 6,441,679 Medical Dictionary for Regulatory Activities (MedDRA®) Preferred Terms (PTs) representing 702 individual PTs were discussed in the same post as a drug compared with 15,650,108 total PTs representing 946 individual PTs in Facebook. Further analysis revealed that 26 % of posts also contained benefit information. Conclusion Social media listening is an important tool to augment post-marketing safety surveillance. Much work remains to determine best practices for using this rapidly evolving data source.

Suggested Citation

  • Gregory E. Powell & Harry A. Seifert & Tjark Reblin & Phil J. Burstein & James Blowers & J. Alan Menius & Jeffery L. Painter & Michele Thomas & Carrie E. Pierce & Harold W. Rodriguez & John S. Brownst, 2016. "Social Media Listening for Routine Post-Marketing Safety Surveillance," Drug Safety, Springer, vol. 39(5), pages 443-454, May.
  • Handle: RePEc:spr:drugsa:v:39:y:2016:i:5:d:10.1007_s40264-015-0385-6
    DOI: 10.1007/s40264-015-0385-6
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    Cited by:

    1. Lucie M. Gattepaille & Sara Hedfors Vidlin & Tomas Bergvall & Carrie E. Pierce & Johan Ellenius, 2020. "Prospective Evaluation of Adverse Event Recognition Systems in Twitter: Results from the Web-RADR Project," Drug Safety, Springer, vol. 43(8), pages 797-808, August.
    2. IOSIF, Alina Natalia & GURGU, ELENA, 2019. "Provisions Concerning The Organization Of Events For The Promotion Of Products On The Pharmaceutical Market In Romania," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 19(3), pages 111-126.
    3. Corina Buzoianu & Monica Bîră, 2021. "Using Social Media Listening in Crisis Communication and Management: New Methods and Practices for Looking into Crises," Sustainability, MDPI, vol. 13(23), pages 1-15, November.
    4. Susan Colilla & Elad Yom Tov & Ling Zhang & Marie-Laure Kurzinger & Stephanie Tcherny-Lessenot & Catherine Penfornis & Shang Jen & Danny S. Gonzalez & Patrick Caubel & Susan Welsh & Juhaeri Juhaeri, 2017. "Validation of New Signal Detection Methods for Web Query Log Data Compared to Signal Detection Algorithms Used With FAERS," Drug Safety, Springer, vol. 40(5), pages 399-408, May.

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