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Social media and multiple sclerosis: measuring narrations and Italian community users

Author

Listed:
  • Domenica Fioredistella Iezzi

    (Tor Vergata University)

  • Angelica Spampinato

    (Sapienza University)

  • Andrea Volterrani

    (Tor Vergata University)

Abstract

Communicating with social media has become a widely used mode among individuals and stakeholders. In this research, we investigate the relationship between the use of social media in the Italian community of patients with multiple sclerosis. To this end, we collected two kinds of data: (1) information through a structured questionnaire administered to multiple sclerosis patients and (2) tweets about multiple sclerosis from 2018 to 2023. We analysed data from the questionnaire using clustering methods to identify the social profiles of multiple sclerosis patients. The tweets, however, were examined using a double strategy: text analysis techniques and models to identify sentiment and topics and the use of social network analysis algorithms to reconstruct the community of the most active users and topics. The results highlighted a significant presence on social media platforms, particularly Facebook, which protects participants and promotes communication through closed groups. Furthermore, tweeting is the preferred tool for communication among trade associations, hospitals, politicians, and traditional media, which promote awareness of new therapies or opportunities for patients.

Suggested Citation

  • Domenica Fioredistella Iezzi & Angelica Spampinato & Andrea Volterrani, 2025. "Social media and multiple sclerosis: measuring narrations and Italian community users," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(4), pages 3703-3721, August.
    • Handle: RePEc:spr:qualqt:v:59:y:2025:i:4:d:10.1007_s11135-025-02127-5
    DOI: 10.1007/s11135-025-02127-5
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    References listed on IDEAS

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    1. Grün, Bettina & Hornik, Kurt, 2011. "topicmodels: An R Package for Fitting Topic Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i13).
    2. Lê, Sébastien & Josse, Julie & Husson, François, 2008. "FactoMineR: An R Package for Multivariate Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i01).
    3. Wang, Yuxi & McKee, Martin & Torbica, Aleksandra & Stuckler, David, 2019. "Systematic Literature Review on the Spread of Health-related Misinformation on Social Media," Social Science & Medicine, Elsevier, vol. 240(C).
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