Real-time sharing and expression of migraine headache suffering on Twitter: a cross-sectional infodemiology study

Abstract

Background: Although population studies have greatly improved our understanding of migraine, they have relied on retrospective self-reports that are subject to memory error and experimenter-induced bias. Furthermore, these studies also lack specifics from the actual time that attacks were occurring, and how patients express and share their ongoing suffering.

Objective: As technology and language constantly evolve, so does the way we share our suffering. We sought to evaluate the infodemiology of self-reported migraine headache suffering on Twitter.

Methods: Trained observers in an academic setting categorized the meaning of every single "migraine" tweet posted during seven consecutive days. The main outcome measures were prevalence, life-style impact, linguistic, and timeline of actual self-reported migraine headache suffering on Twitter.

Results: From a total of 21,741 migraine tweets collected, only 64.52% (14,028/21,741 collected tweets) were from users reporting their migraine headache attacks in real-time. The remainder of the posts were commercial, re-tweets, general discussion or third person's migraine, and metaphor. The gender distribution available for the actual migraine posts was 73.47% female (10,306/14,028), 17.40% males (2441/14,028), and 0.01% transgendered (2/14,028). The personal impact of migraine headache was immediate on mood (43.91%, 6159/14,028), productivity at work (3.46%, 486/14,028), social life (3.45%, 484/14,028), and school (2.78%, 390/14,028). The most common migraine descriptor was "Worst" (14.59%, 201/1378) and profanity, the "F-word" (5.3%, 73/1378). The majority of postings occurred in the United States (58.28%, 3413/5856), peaking on weekdays at 10:00h and then gradually again at 22:00h; the weekend had a later morning peak.

Conclusions: Twitter proved to be a powerful source of knowledge for migraine research. The data in this study overlap large-scale epidemiological studies, avoiding memory bias and experimenter-induced error. Furthermore, linguistics of ongoing migraine reports on social media proved to be highly heterogeneous and colloquial in our study, suggesting that current pain questionnaires should undergo constant reformulations to keep up with modernization in the expression of pain suffering in our society. In summary, this study reveals the modern characteristics and broad impact of migraine headache suffering on patients' lives as it is spontaneously shared via social media.

Keywords: Twitter; epidemiology; headache; migraine; social media.

Figure 1

Classification of Tweets in Categories. Only 65% (14,028) of the 21,741 tweets were classified as self-reported migraine headache attacks.

Figure 2

Gender distribution disclosed by users who reported their migraine headache attacks (n=14,028). 73.47% female (10,306 subjects), 17.40% male (2441 subjects), 0.01% (2 subjects) self-reported as transgender, and 9.12% was not provided (1279 subjects).

Figure 3

Impact and expression of migraine headache suffering (n=14,028). Personal impact (Internal) was predominantly on mood (43.91%, 6159 tweets). External impact was on productivity and absenteeism at work (3.46%, 486 tweets), social events (3.45%, 484 tweets), and school (2.78%, 390 tweets).

Figure 4

Most common pain descriptors used (n=1378). The most frequently used word from the McGill Pain Questionnaire (MPQ) was "horrible" (6.97%, 96 uses). Additional migraine headache adjectives (“Not McGill” words) included "worst" (14.59%, 201 uses) and profanity, the "F-word" (5.30%, 73 uses) being most frequently used.

Figure 5

Percentage of migraine headache tweets by continent (n=5865). Most represented was North America with 65.57% (3840); United States alone represented 58.28% of overall data (3413 tweets).

Figure 6

Temporal patterns of migraine headache tweets in the United States. Tweets were converted to local times and corrected for daylight savings time. The averaged flow of migraine tweets accumulated at 10h, persisted and gradually peaked later at night (22h). During Saturday and Sunday (dashed line in the top-left graph), the highest peak occurred at 18h.