Impact of Telemedicine through Social Media: A Study of Topics in User Comments on Twitter

Background The use of new technologies has transformed society, affecting communication, information seeking and ways of working. Telemedicine, as a remote health practice through ICTs, has grown exponentially, especially after the pandemic. Objective This qualitative study aims to explore users' perceptions and concerns about telemedicine through comments posted on Twitter by users, identifying primary, secondary and residual themes. Methods Natural Language Processing (NLP) and Machine Learning techniques, specifically the Latent Dirichlet Allocation (LDA) model, were used to analyse 156,633 comments extracted from Twitter related to telemedicine topics. Results The study revealed several issues to be addressed. Data was collected using keywords such as "teleconsultation" and "telemedicine". We can see that the most frequent words in the comments include words such as "health", "service", "doctor" and "patient". The themes identified were grouped into four dimensions: general information, benefits sought, specific information and professional issues. The results showed that 60.1% of the comments focused on generic telemedicine topics, ease of use and service information. Twitter queries were observed to be public and general in nature, focusing on benefits and accessibility, while disease or treatment specific topics were less frequent. Conclusions The results provide information for the proper development and study of telemedicine through social networks. Twitter is a platform mainly used for general telemedicine queries, with convenience and accessibility as the main benefits mentioned. The results suggest that online telemedicine interactions are complex and offer valuable insights for improving telemedicine communication strategies. Future research could explore the use of hashtags and analyse differences in interaction patterns according to user profile, providing a deeper understanding of audiences' behaviour on social networks. These findings underline the importance of considering audience preferences to improve the effectiveness of telemedicine communications.