Content analysis of tweets for a better understanding of the context around the individual’s low back pain experience (Preprint)

BACKGROUND Although personal experiences of low back pain have traditionally been explored through qualitative studies, social media content analysis has the potential to be used to complement these studies by providing deeper understanding of how problems such as pain are perceived by those how have it, and the effect of the contextual variables on individuals and the community. OBJECTIVE The objective of this study was to perform content analysis of tweets for identifying contextual variables of the low back pain (LBP) experience from a first-person perspective to better understand individuals’ beliefs and perceptions. METHODS We analysed 896,867 cleaned tweets about low back pain between 1 January 2014 – 31 December 2018. We tested and compared Latent Dirichlet Allocation (LDA), Dirichlet Multinomial Mixture (DMM), GPU-DMM, Biterm Topic Model (BTM) and Non-negative Matrix factorization (NMF) for identifying topics associated with tweets. A coherence score was determined to identify the best model. RESULTS LDA outperformed all other algorithms resulting in the highest coherence score. The best model was LDA with 60 topics with coherence score 0.562. With input from domain experts, the 60 topics were validated and grouped into 19 contextual categories. “Emotion and Beliefs” had the largest proportion of the total tweets (17.6%), followed by “Physical Activity” (13.85%) and “Daily Life” (9%), while “Food and Drink”, “Weather” and “Not Being Understood” had the least (1.29%, 1.13% and 1.02% respectively). Of the 11 topics within “emotions and beliefs”, 72% had negative sentiment. CONCLUSIONS Using social media allows access to the data from a larger, heterogonous and geographically distributed population which is not possible using traditional qualitative methods that are generally limited to a small population. Individuals may be more inclined to express their feelings and emotions freely on social media sites, where the data is collected in an unsolicited manner, compared to common, rigid data collection methods. A content analysis of tweets identified common themes in the area of low back pain that are consistent with findings from conventional qualitative studies but provide a more granular view of the individuals’ perspectives related to low back pain. This understanding has the potential to assist with developing more effective and personalized models of care to improve treatment outcomes.