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Analysing Sentiment and Topics Related to Multiple Sclerosis on Twitter

Authors :
Pape-Haugaard, Louise
Lovis, Christian
Madsen, Inge Corte
Weber, Patrick
Nielsen, Per Hostrup
Scott, Philip
Universidad de Sevilla. Departamento de Tecnología Electrónica
Universidad de Sevilla. TIC022: Tecnologías para la Asistencia, la Integración y la Salud
Universidad de Sevilla. TIC150: Tecnología Electrónica e Informática Industrial
Giunti, Guido
Claes, Maëlick
Dorronzoro Zubiete, Enrique
Rivera Romero, Octavio
Gabarrón, Elia
Pape-Haugaard, Louise
Lovis, Christian
Madsen, Inge Corte
Weber, Patrick
Nielsen, Per Hostrup
Scott, Philip
Universidad de Sevilla. Departamento de Tecnología Electrónica
Universidad de Sevilla. TIC022: Tecnologías para la Asistencia, la Integración y la Salud
Universidad de Sevilla. TIC150: Tecnología Electrónica e Informática Industrial
Giunti, Guido
Claes, Maëlick
Dorronzoro Zubiete, Enrique
Rivera Romero, Octavio
Gabarrón, Elia
Publication Year :
2020

Abstract

Background and objective: Social media could be valuable tools to support people with multiple sclerosis (MS). There is little evidence on the MSrelated 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 awarenessraising. Tweets on condition burden and related chronic conditions were the most negative (p<0.001). A significant lower positive sentiment was found for both tweets dealing with disease-modifying drugs, condition burden, and related chronic conditions (p<0.001). Only tweets on awareness-raising were most positive than the average (p<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.

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1240066520
Document Type :
Electronic Resource