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Analysis of Posts on Social Network Service related to Panic Disorder using Text Mining (Preprint)


 BACKGROUND
 Panic attacks have different clinical characteristics among individuals and countries, characterizing time, place, and symptoms are not clearly predictable
 
 
 OBJECTIVE
 This study aimed to analyze crucial keywords related to panic disorder and identify various clinical characteristics of panic attacks
 
 
 METHODS
 We collected 8,728 Twitter posts related to panic disorder from January 1 to December 4, 2020. First, we analyzed crucial and simultaneous emergence keywords related to panic disorder. For this, Term frequency, Term Frequency-Inverse Document Frequency, degree centrality, and N-gram analyses were conducted using Rstudio and TEXTOM and visualized as word clouds. Also, we classfied results of Term frequency for panic disorder into physical symptoms, triggers, time, place, affect, pathology, person, and coping.
 
 
 RESULTS
 First, depression, drugs, respiration, and stress were keywords related to panic disorder. Next, hyperventilation, palpitations, and shaking were common physical symptoms. Stress, sound, trauma, and coffee were also ranked high in terms of triggering situations. Additionally, in terms of time, morning, night, and dawn accounted for most of the time. Meanwhile, homes, schools, subways, and companies were ranked high as places of occurrence. Regarding affect, fear, tears, and embarrassment were also common. Furthermore, anxiety and depression were ranked high in terms of pathology. Finally, drugs and hospitals were ranked high in terms of coping.
 
 
 CONCLUSIONS
 These results help to understand the main characteristics of panic disorder and various aspects of unexpected panic attacks and are expected to be a basis for identifying the characteristic clinical aspects of panic disorder among Koreans.
