Development and evaluation of an organizational model for digital media monitoring in the pharmacovigilance system (on the example of the VKontakte social network)

Background. Digital media monitoring is a requirement of Good Pharmacovigilance Practices (GVP) of the Eurasian Economic Union (EAEU). However, standardized approaches for processing unstructured social media data on adverse reactions (ARs) associated with the medical use of drugs are currently lacking.

Objective. To develop and test an organizational model for digital media monitoring aimed at identifying AR reports, adapted to the requirements of EAEU GVP, as well as to assess its feasibility on the example of the Russian-language segment of the VKontakte social network.

Materials and methods. A retrospective analysis of content published in seven VKontakte communities for patients with rheumatoid arthritis (with a combined audience of more than 54,000 subscribers) during the period from January to December 2025 was conducted. A total of 58,884 posts and comments were analyzed. Searches were performed using Russian keywords corresponding to “side effects” in English. The developed data-processing protocol includes assessment of the minimum validity criteria of posts and comments (presence of an identifiable reporter, patient, suspected drug, and description of an AR); coding of identified ARs using the Medical Dictionary for Regulatory Activities (MedDRA, version 27.0) by two independent experts; comparison with the approved prescribing information for drug use; and calculation of inter-rater agreement using Cohen’s kappa coefficient.

Results. A total of 1,463 reports mentioning ARs associated with drug use were identified. Of these, 108 reports (7.4%) met the established validity criteria. Coding of the 108 valid reports yielded 76 unique preferred terms for ARs consistent with MedDRA terminology. Comparison of these terms with the approved prescribing information showed that 59 ARs were classified as listed (expected) ARs, whereas 17 were not included in the prescribing information and were, therefore, classified as unlisted (unexpected) ARs. These data served as a basis for generating hypotheses for subsequent validation using signal management. Inter-rater agreement between the two independent experts regarding the presence of ARs was high (Cohen’s kappa = 0.87). The proportion of publications that could not be coded because of incomplete or nonspecific descriptions was 92.6% of those mentioning ARs, confirming the high level of informational noise in social media.

Conclusion. The proposed organizational model enables formalization of the social media monitoring process, ensuring reproducibility and compliance with EAEU GVP regulatory requirements. The identification of ARs not described in the approved prescribing information demonstrates the potential of social networks as an additional source of drug safety hypotheses and highlights the need to integrate such monitoring into the routine pharmacovigilance activities of pharmaceutical organizations.

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