{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T12:28:08Z","timestamp":1787228888763,"version":"build-2736575974"},"reference-count":18,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Infodemiology"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Multiple sclerosis (MS) is a chronic neurological disease that starts in young adulthood and can significantly affect quality of life (QoL) due to various symptoms, and the risk of disability. MS directly affects people living with the disease and indirectly affects their relatives and family caregivers.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n The objective of this social media analysis was to identify the main topics of discussion among people affected by MS and their perceptions of the impact of MS on their QoL.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Publicly available French messages, posted between January 2017 and October 2022, were retrieved using an extraction query that contained keywords related to MS. The effects on QoL were detected using a machine learning algorithm specifically trained on social media data. Five specific models covered the following health-related QoL dimensions: physical well-being, psychological well-being, daily activities (including professional and academic activities), social or relational well-being, and material well-being. Descriptive statistics were provided and illustrated with quotes from social media.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The analysis corpus for the 2017 to 2022 period included 3225 messages corresponding to 2034 different social media users, either people living with MS (654\/3225, 20%) messages or family caregivers (2571\/3225, 80%) messages, identified from 32 sources. Women represented 42.5% (864\/2034) and men represented 28.2% (574\/2034) of social media users (gender was unknown for 596\/2034, 29.3%), and their mean age was 35 (SD 6.6) years. The 2 main themes of posts were \u201cCaregivers and family members\u201d (1032\/3225, 32%) and \u201cDisability\u201d (774\/3225, 24%). Overall, 847 messages described at least one impact of MS on QoL: relational or social (n=431, 50.9%), physical (n=284, 33.5%), psychological (n=76, 9.0%), financial or material well-being (n=32, 3.8%), and daily activities (n=24, 2.8%).<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Our findings confirm the high impact of MS on everyday life and QoL for both patients and family caregivers. Caregivers were the most numerous to express themselves and post messages on social media. The most affected QoL dimension was relational or social well-being, which is probably linked to the fact that social networks and digital patient communities are places for discussion, sharing experiences, and looking for support. These findings confirm that social media is a way for people affected by MS to express themselves and look for support and understanding. They also show that social media provides an opportunity to discover the fears, questions, needs, and thoughts of those affected by the disease, particularly caregivers, who are rarely considered in research studies.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/88763","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T12:00:21Z","timestamp":1787227221000},"page":"e88763-e88763","source":"Crossref","is-referenced-by-count":0,"title":["How Social Media Analysis Offers an Opportunity to Understand the Reality of People Living With Multiple Sclerosis: Descriptive French Study"],"prefix":"10.2196","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8424-4499","authenticated-orcid":false,"given":"Emmanuelle","family":"Leray","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2642-7726","authenticated-orcid":false,"given":"St\u00e9phane","family":"Sch\u00fcck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7156-9075","authenticated-orcid":false,"given":"Pamela","family":"Voillot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6297-1748","authenticated-orcid":false,"given":"Nathalie","family":"Texier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,8,19]]},"reference":[{"issue":"8","key":"R1","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1001\/jama.2020.26858","article-title":"Diagnosis and treatment of multiple sclerosis: a review","volume":"325","author":"McGinley","journal-title":"JAMA"},{"issue":"14","key":"R2","doi-asserted-by":"publisher","first-page":"1816","DOI":"10.1177\/1352458520970841","article-title":"Rising prevalence of multiple sclerosis worldwide: insights from the Atlas of MS, third edition","volume":"26","author":"Walton","journal-title":"Mult Scler"},{"key":"R3","unstructured":"Donegan K Ovelgonne H Flores G Fuglerud P Georgescu A . Social media and m-health data subgroup report. European Medicines Agency (EMA); 2019. URL: https:\/\/www.ema.europa.eu\/en\/documents\/report\/social-media-m-health-data-subgroup-report_en.pdf [Accessed 17-08-2023]"},{"key":"R4","unstructured":"Global social media statistics. Data reportal. URL: https:\/\/datareportal.com\/social-media-users [Accessed 31-05-2026]"},{"issue":"5","key":"R5","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1136\/amiajnl-2012-000990","article-title":"Reviewing social media use by clinicians","volume":"19","author":"von Muhlen","journal-title":"J Am Med Inform Assoc"},{"key":"R6","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2024.1494018","article-title":"Efficiency and safety of cannabinoid medical use: an analysis of discussions and observed trends on Instagram","volume":"12","author":"Litvinova","journal-title":"Front Public Health"},{"key":"R7","doi-asserted-by":"publisher","DOI":"10.2196\/54321","article-title":"Combining topic modeling, sentiment analysis, and corpus linguistics to analyze unstructured web-based patient experience data: case study of modafinil experiences","volume":"26","author":"Walsh","journal-title":"J Med Internet Res"},{"issue":"1","key":"R8","doi-asserted-by":"publisher","DOI":"10.1186\/s12891-024-07687-5","article-title":"Understanding public perceptions and discussions on diseases involving chronic pain through social media: cross-sectional infodemiology study","volume":"25","author":"Valades","journal-title":"BMC Musculoskelet Disord"},{"key":"R9","unstructured":"Brandwatch. URL: https:\/\/www.brandwatch.com\/ [Accessed 31-05-2026]"},{"issue":"7","key":"R10","doi-asserted-by":"publisher","DOI":"10.2196\/25049","article-title":"Assessing patient perceptions and experiences of paracetamol in France: infodemiology study using social media data mining","volume":"23","author":"Sch\u00fcck","journal-title":"J Med Internet Res"},{"issue":"9","key":"R11","doi-asserted-by":"publisher","DOI":"10.2196\/19694","article-title":"Exploring the health-related quality of life of patients treated with immune checkpoint inhibitors: social media study","volume":"22","author":"Cott\u00e9","journal-title":"J Med Internet Res"},{"issue":"1","key":"R12","doi-asserted-by":"publisher","DOI":"10.2196\/31528","article-title":"A new method to extract health-related quality of life data from social media testimonies: algorithm development and validation","volume":"24","author":"Renner","journal-title":"J Med Internet Res"},{"issue":"1","key":"R13","doi-asserted-by":"publisher","DOI":"10.2196\/31140","article-title":"Social media platforms listening study on atopic dermatitis: quantitative and qualitative findings","volume":"24","author":"Voillot","journal-title":"J Med Internet Res"},{"issue":"9","key":"R14","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1016\/s0002-9343(02)01473-0","article-title":"Infodemiology: the epidemiology of (mis)information","volume":"113","author":"Eysenbach","journal-title":"Am J Med"},{"issue":"7","key":"R15","doi-asserted-by":"publisher","DOI":"10.2196\/17338","article-title":"Assessment of adaptive engagement and support model for people with chronic health conditions in online health communities: combined content analysis","volume":"22","author":"Green","journal-title":"J Med Internet Res"},{"key":"R16","doi-asserted-by":"publisher","DOI":"10.3233\/SHTI200294","article-title":"Analysing sentiment and topics related to multiple sclerosis on Twitter","volume":"270","author":"Giunti","journal-title":"Stud Health Technol Inform"},{"issue":"1","key":"R17","doi-asserted-by":"publisher","DOI":"10.2196\/10146","article-title":"Health topics on Facebook groups: content analysis of posts in multiple sclerosis communities","volume":"8","author":"Della Rosa","journal-title":"Interact J Med Res"},{"issue":"1","key":"R18","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-018-1247-8","article-title":"Real-world data and the patient perspective: the PROmise of social media?","volume":"17","author":"McDonald","journal-title":"BMC Med"}],"container-title":["JMIR Infodemiology"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T12:00:23Z","timestamp":1787227223000},"score":1,"resource":{"primary":{"URL":"https:\/\/infodemiology.jmir.org\/2026\/1\/e88763"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,19]]},"references-count":18,"URL":"https:\/\/doi.org\/10.2196\/88763","relation":{},"ISSN":["2564-1891"],"issn-type":[{"value":"2564-1891","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,19]]},"article-number":"v6i8e88763"}}