Giunti Guido, Claes Maëlick, Dorronzoro Zubiete Enrique, Rivera-Romero Octavio, & Gabarron Elia. (2020). Analysing Sentiment and Topics Related to Multiple Sclerosis on Twitter [JB]. Studies in Health Technology and Informatics, 270(Digital Personalized Health and Medicine), 911–915. https://doi.org/10.3233/SHTI200294
Analysing sentiment and topics related to multiple sclerosis on twitter
|Author:||Giunti, Guido1; Claes, Maëlick1; Dorronzoro Zubiete, Enrique2;|
1University of Oulu, Oulu, Finland
2Universidad de Sevilla, Seville, Spain
3Norwegian Centre for E-health Research, University Hospital North Norway, Tromsø, Norway
|Online Access:||PDF Full Text (PDF, 0.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020110689455
|Publish Date:|| 2020-11-06
Background and objective: Social media could be valuable tools to support people with multiple sclerosis (MS). There is little evidence on the MS-related topics that are discussed on social media, and the sentiment linked to these topics. The objective of this work is to identify the MS-related main topics discussed on Twitter, and the sentiment linked to them.
Methods: Tweets dealing with MS in the English language were extracted. Latent-Dirilecht Allocation (LDA) was used to identify the main topics discussed in these tweets. Iterative inductive process was used to group the tweets into recurrent topics. The sentiment analysis of these tweets was performed using SentiStrength.
Results: LDA’ identified topics were grouped into 4 categories, tweets dealing with: related chronic conditions; condition burden; disease-modifying drugs; and awareness-raising. Tweets on condition burden and related chronic conditions were the most negative (p%lt;0.001). A significant lower positive sentiment was found for both tweets dealing with disease-modifying drugs, condition burden, and related chronic conditions (p%lt;0.001). Only tweets on awareness-raising were most positive than the average (p%lt;0.001).
Discussion: The use of both tools to identify the main discussed topics on social media and to analyse the sentiment of these topics, increases the knowledge of the themes that could represent the bigger burden for persons affected with MS. This knowledge can help to improve support and therapeutic approaches addressed to them.
Studies in health technology and informatics
|Pages:||911 - 915|
Digital Personalized Health and Medicine
|Host publication editor:||
Pape-Haugaard, L. B.
Madsen, I. C.
Nielsen, P. H.
Medical Informatics Europe Conference
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
3141 Health care science
EDZ receives funding and is supported by the V Plan Propio de Investigación de la
Universidad de Sevilla, Spain.
© 2020 European Federation for Medical Informatics (EFMI) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms
of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).