Unique Characteristics of Human-Chatbot Conversations and Their Potential For Mental Health Support.
Accessibility and availability of psychological support is crucial for the mental well-being of the global population. The recent popularity of social artificial agents, such as chatbots, reveals the rising potential for using them for mental healthcare. In this work, we investigate how differently users express emotions online when interacting with a commercially available chatbot (Simsimi) and posting on a public social media platform (Twitter). Our results suggest that private conversations with a chatbot tend to be more emotionally charged and focus on personal issues more frequently than messages posted on Twitter.
This is an ongoing project in collaboration with Simsimi corporation.
A. Zhunis, G. Lima, H. Chin, M. Shin, J. Choi, C. Cha, and M. Cha. 2022, July. Unique Characteristics of Human-Chatbot Conversations and Their Potential for Mental Health Support. In 2022 한국컴퓨터종합학술대회 (KCC2022). 한국정보과학회. [PDF]
H. Chin, G. Lima, M. Shin, A. Zhunis, C. Cha, J. Choi, and M. Cha. User-Chatbot Conversations During the COVID-19 Pandemic: A Study Based on Topic Modeling and Sentiment Analysis, In Journal of Medical Internet Research (JMIR) (IF=7.08). [Paper]
Study of Simsimi Superusers is under review at CIKM 2023
While sleep positively impacts well-being, health, and productivity, the effects of societal factors on sleep remain underexplored. Here we analyze the sleep of individuals across the globe from wearable devices and attempt to understand how culture affects our sleeping habits. Defining the interplay between social norms and sleep may allow the development of targeted interventions and health policies.
Park, S., Zhunis, A., Constantinides, M. et al. Social dimensions impact individual sleep quantity and quality. Sci Rep 13, 9681 (2023). https://doi.org/10.1038/s41598-023-36762-5
Presentation at TheWebConf 2022
The COVID-19 pandemic left its unique mark on the 21st century as one of the most significant disasters in history, triggering governments all over the world to respond with a wide range of interventions. However, these restrictions come with a substantial price tag. It is crucial for governments to form anti-virus strategies that balance the trade-off between protecting public health and minimizing the economic cost. This work proposes a probabilistic programming method to quantify the efficiency of major non-pharmaceutical interventions.
We present a generative simulation model that accounts for the economic and human capital cost of adopting such strategies, and provide an end-to-end pipeline to simulate the virus spread and the incurred loss of various policy combinations. By investigating the national response in 10 countries covering four continents, we found that social distancing coupled with contact tracing is the most successful policy, reducing the virus transmission rate by 96% along with a 98% reduction in economic and human capital loss. Together with experimental results, we open-sourced a framework to test the efficacy of each policy combination.