Sentiment and emotion analysis of dental pain posts on X (formerly Twitter)

Social media data are increasingly used in health research for communication, surveillance, and behavioral insights, and have also been applied to dentistry. The objective of this study is to describe sentiment and emotion patterns in English/Arabic tweets in the X platform about dental pain and summarize care pathways, including home remedies. This is a cross-sectional analysis of public tweets retrieved for three years. After de-duplication and eligibility screening, tweets were classified (sentiment; Ekman emotion categories) and assigned to 11 themes using a large language model with dual human validation. A total of 4,051 tweets were screened, and 659 met the study inclusion criteria. Negative sentiment predominated study tweets. Fear (29.7%) and sadness (19.1%) were the most frequent emotions. Negative sentiment was highest for pain complaints, advice-seeking, emergency access, and post-treatment pain (p < 0.001). Mentions of professional dental care (47.4%) exceeded home-remedy mentions (1.1-17.0%). Arabic tweets trended more negative than English (p = 0.07). Dental pain discourse on X is largely negative and fear-laden, with professional-care mentions outnumbering home remedies; translating these signals into practice suggests prioritizing reassurance, offering triage cues, and directing users to urgent-care pathways.