Aehong Min

Jessica Y. Medina, Jordyn Young, Aehong Min et al.

Epilepsy is one of the most common neurological conditions, and people living with epilepsy (PLWE) often use social media as a resource. However, a comprehensive understanding of the topics represented in epilepsy-specific communities where PLWE may be more honest is essential to designing better technologies to address epilepsy self-management. To understand the main topics and concerns of PLWE, we collected 23,944 r/Epilepsy subreddit posts and performed topic modeling, thematic, and psycho-linguistic analyses. We found five major themes for those topics: symptoms and triggers (e.g., mental health and memory, sleep/nocturnal, and photosensitivity), treatment and healthcare experience (e.g., medication, understanding epilepsy), daily functions ( e.g., perceived level of independence and finances), seizure activity (e.g., auras and ictal symptoms), and support for PLWE (assisting PLWE and support for PLWE). We highlight the top psycho-linguistic characteristics of posts across different topics. Our contributions include providing an understanding of the challenges of an online epilepsy community and their social support needs, and implications for designing technologies.

Aehong Min, Xuan Wang, Rion Brattig Correia et al.

We used a dictionary built from biomedical terminology extracted from various sources such as DrugBank, MedDRA, MedlinePlus, TCMGeneDIT, to tag more than 8 million Instagram posts by users who have mentioned an epilepsy-relevant drug at least once, between 2010 and early 2016. A random sample of 1,771 posts with 2,947 term matches was evaluated by human annotators to identify false-positives. OpenAI's GPT series models were compared against human annotation. Frequent terms with a high false-positive rate were removed from the dictionary. Analysis of the estimated false-positive rates of the annotated terms revealed 8 ambiguous terms (plus synonyms) used in Instagram posts, which were removed from the original dictionary. To study the effect of removing those terms, we constructed knowledge networks using the refined and the original dictionaries and performed an eigenvector-centrality analysis on both networks. We show that the refined dictionary thus produced leads to a significantly different rank of important terms, as measured by their eigenvector-centrality of the knowledge networks. Furthermore, the most important terms obtained after refinement are of greater medical relevance. In addition, we show that OpenAI's GPT series models fare worse than human annotators in this task.