Public health surveillance of dental pain via Twitter

Abstract

On Twitter, people answer the question, "What are you doing right now?" in no more than 140 characters. We investigated the content of Twitter posts meeting search criteria relating to dental pain. A set of 1000 tweets was randomly selected from 4859 tweets over 7 non-consecutive days. The content was coded using pre-established, non-mutually-exclusive categories, including the experience of dental pain, actions taken or contemplated in response to a toothache, impact on daily life, and advice sought from the Twitter community. After excluding ambiguous tweets, spam, and repeat users, we analyzed 772 tweets and calculated frequencies. Of the sample of 772 tweets, 83% (n = 640) were primarily categorized as a general statement of dental pain, 22% (n = 170) as an action taken or contemplated, and 15% (n = 112) as describing an impact on daily activities. Among the actions taken or contemplated, 44% (n = 74) reported seeing a dentist, 43% (n = 73) took an analgesic or antibiotic medication, and 14% (n = 24) actively sought advice from the Twitter community. Twitter users extensively share health information relating to dental pain, including actions taken to relieve pain and the impact of pain. This new medium may provide an opportunity for dental professionals to disseminate health information.

Figure 1.

Frequencies of primary categories (PC) and secondary categories (SC), n (%). Gray-shaded boxes contain PCs, while non-shaded boxes contain SCs. The PCs are ordered from most frequent to least frequent. The categories are not mutually exclusive, and therefore the percentages of PCs may not total 100% and the percentages of SCs may not total 100%. The denominator used for PC was the sample size following the removal of tweets that met exclusion criteria (PC denominator n = 772). The denominator used for SC was the number of tweets coded in the respective PC (e.g., SC denominator for all SC under Statement = 640, for SC under Action = 170, for SC under Impact = 112, etc.). Re-tweets, unrelated or ambiguous tweets, tweets from repeat users, and any uninterpretable tweets were excluded from the sample (n = 228).

Figure 2.

Word cloud created from tweet content for the 772 randomly collected tweets (http://www.wordle.net/). Font size correlates with the greater frequency of the word used in tweets related to dental pain.