The MIE2020 conference planned end of April 2020 has been cancelled due to the SARS-CoV-2 pandemyBackground 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.V Plan Propio de Investigación de la Universidad de Sevilla, Spai