Differences in emotional and pain-related language in tweets about dentists and medical doctors: Text-analysis of Twitter content

Background: Social media provides people with easy ways to communicate their attitudes and feelings to a wide audience. Many people unfortunately have negative associations and feelings about dental treatment due to former painful experiences. Former research indicates that there might exist a pervasive and negative occupational stereotype related to dentists, and that this stereotype is expressed in many different venues, including in movies and in literature. Objective: This study investigates the language used in relation to dentists and medical doctors in the social media channel Twitter. The purpose is to compare the professions concerning the use of emotional words and pain-related words, which might underlie the pervasive negative stereotype identified in relation to dentists. We hypothesize that (A) tweets about dentists will have more negative emotion words than tweets about medical doctors, and that (B) pain related words are used more frequently in tweets about dentists than medical doctors. Methods: Twitter content (“tweets”) about dentists and medical doctors were collected scanning the keywords “dentist” and “doctor” using the Twitter API 140Dev. Word content of the selected tweets were analysed using the Linguistic Inquiry and Word Count software. The research hypotheses were investigated using non-parametric Wilcoxon-Mann-Whitney tests. Results: Over 2.3 million tweets were collected in total, of which about 1/3 contained the word “dentist” and about 2/3 contained the word “doctor”. Hypothesis A was supported as there were a higher proportion of negative words used in tweets about dentists than in tweets about medical doctors; W = 634925.00, P < .001. Similarly, tests showed a difference in proportions of anger words (W = 582087.00, P < .001), anxiety words (W = 660532.00, P < . 001), and sadness words (W = 617011.00, P < .001), with higher proportions in tweets about dentists than tweets about doctors. Also, Hypothesis B was supported as there were a higher proportion of pain related words used in tweets about dentists than about doctors; W = 590139.00, P < .001. Conclusions: The results from this study support the existence of a negative stereotype for dentists among Twitter-users. The impact of expression of this stereotype on Twitter needs to be further explored with other study designs.