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.

Lucas M. Silva, Evropi Stefanidi, Aehong Min et al.

While ubiquitous computing research has explored diverse devices for personal health tracking, we know less about multi-device designs for family informatics, where health management is inherently collaborative. To understand how families adopt and perceive ubiquitous access to shared health data across contexts, we evaluated smartwatch-only, home display-only, and combined designs for tracking moods and goals, domains central to family health behavior regulation. 44 people across 12 families alternated between these designs over nine weeks. Log analysis revealed that mood tracking and goal reporting were significantly more frequent with the home display present compared to smartwatch-only use, despite an overall decline in mood tracking over time. Tracking peaked in afternoons, dropped on weekends, and occurred 2.6X more at home, with children tracking more consistently than adults across all designs. From interview analysis, we learned how family data glanceability on smartwatches supported opportunistic tracking and awareness while apart, whereas displays reminded families to self-track and collaborate during home routines including members that avoided wearables (e.g., non-participants). Multi-device redundancy accommodated diversity in routines, mobility patterns, and device preferences among members in the same family. We discuss opportunities for multi-device family informatics that accommodates different preferences through inclusive, glanceable, and adaptable ubiquitous data sharing.

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.