Application of the LDA model to identify topics in telemedicine conversations on the X social network

The evolution experienced by global society, in the post-COVID 19 era, is marked by the quite obligatory use of digital media in many sectors, as is the case for the health sector. Quite frequently, both patients and health professionals use social media to express their telemedicine concerns or interests. The present research focuses on these social media comments as they represent a very interesting data source for researchers. In the present analysis, we focus on unstructured tweet texts written by Internet users and apply both machine learning and the Latent Dirichlet Allocation algorithm to model X databases and identify tweet topics. The results gathered provide professionals with information on the most important issues and factors of influence for telemedecine consumers.BackgroundThe 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.ObjectiveWe do apply a mixed methodology in our study and use both qualitative and quantitative techniques to explore the conversational topics generated about telemedicine through comments posted by users on X. This allows us to identify primary, secondary, and residual themes.MethodsNatural Language Processing (NLP) and Machine Learning techniques, specifically the Latent Dirichlet Allocation (LDA) model, were used to analyse 156,633 comments extracted from "X" related to telemedicine topics.ResultsThe 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. "X" queries were observed to be public and general in nature, focusing on benefits and accessibility, while disease or treatment specific topics were less frequent.ConclusionsThe results provide information for the proper development and study of telemedicine through social networks. "X" 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.