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Personas in Online Health Communities

Published in Journal of Biomedical Informatics (JBI), 2016

Many researchers and practitioners use online health communities (OHCs) to influence health behavior and provide patients with social support. One of the biggest challenges in this approach, however, is the rate of attrition. OHCs face similar problems as other social media platforms where user migration happens unless tailored content and appropriate socialization is supported. To provide tailored support for each OHC user , we developed personas in OHCs illustrating user s’ needs and requirements in OHC use. To develop OHC personas, we first interviewed 16 OHC users and administrators to qualitatively understand varying user needs in OHC. Based on their responses, we developed an online survey to systematically investigate OHC personas. We received 184 survey responses from OHC users, which informed their values and their OHC use patterns. We performed open coding analysis with the interview data and cluster analysis with the survey data and consolidated the analyses of the two datasets. Four personas emerged—Caretakers, Opportunists, Scientists, and Adventurers. The results inform users’ interaction behavior and attitude patterns with OHCs. We discuss implications for how these personas inform OHCs in delivering personalized informational and emotional support.

Recommended citation: Huh, J., Kwon, B. C., Kim, S. H., Lee, S., Choo, J., Kim, J., Choi, M. J., & Yi, J. S. (2016). Personas in online health communities. Journal of biomedical informatics, 63, 212–225. https://doi.org/10.1016/j.jbi.2016.08.019. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5268468/

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

Published in IEEE/ CVF International Conference on Computer Vision and Pattern Recognition (CVPR), 2018

Existing approaches on image-to-image translation, while capable of producing high-quality translation images, were limited to one-on-one translations and also less robust on multiple domains. To address this limitation, we introduced StarGAN, a model that uses a simple yet effective approach, one-hot masking, and show through facial image translation tasks that our model is capable of performing multiple forms of image translations.

Recommended citation: Y. Choi, M. Choi, M. Kim, J. Ha, S. Kim and J. Choo, "StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 8789-8797, doi: 10.1109/CVPR.2018.00916. https://ieeexplore.ieee.org/document/8579014

RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records

Published in IEEE Transactions on Visualization and Computer Graphics (TVCG), 2019

Electronic medical records (EMRs) are increasingly being coupled with deep learning models for predicting the future condition of a patient. In this study, we introduce RetainVis, a visual analytics tool that can be used for interpreting how each individual risk code in EMR data can contribute to the predicted outcomes such as heart failure or cataract symptoms. We provide both quantitative results of the model performance as well as qualitative use cases.

Recommended citation: B. C. Kwon et al., "RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records," in IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 299-309, Jan. 2019, doi: 10.1109/TVCG.2018.2865027. https://ieeexplore.ieee.org/document/8440842

Ten Social Dimensions of Conversations and Relationships

Published in Proceedings of The Web Conference 2020 (WWW 2020), 2020

In this paper, based on 10 different social dimensions built on several literature, we collected crowdsourced data and trained classifiers for extracting social dimensions from textual conversation data. We used our models to examine the existence of different dimensions in various datasets of text conversations, and how they relate to actual community-wide or societal outcomes.

Recommended citation: Minje Choi, Luca Maria Aiello, Krisztián Zsolt Varga, and Daniele Quercia. 2020. Ten Social Dimensions of Conversations and Relationships. In Proceedings of The Web Conference 2020 (WWW 2020). Association for Computing Machinery, New York, NY, USA, 1514–1525. DOI:https://doi.org/10.1145/3366423.3380224 https://dl.acm.org/doi/abs/10.1145/3366423.3380224/

More than Meets the Tie: Examining the Role of Interpersonal Relationships in Social Networks

Published in ICWSM 2021, 2021

In this study, we collect 9.6M different types of social ties from Twitter, which we group into five categories: social, romance, family, organizational, and parasocial. Using these categories, we show that the interpersonal relationship type leads to notable differences in (1) word and linguistic patterns, (2) shared topic diversity, and (3) network proximity. Using these labels as training data, we train classification models using the interaction data between two users to show that relationship types can be inferred with high F-1 scores. Finally, we show that these features can be used for predicting future diffusion of information, as in predicting whether a future retweet will occur.

Recommended citation: N/A https://arxiv.org/abs/2105.06038

Analyzing the Engagement of Social Relationships During Life Event Shocks in Social Media

Published in ICWSM 2023, 2022

In this study, we collect 9.6M different types of social ties from Twitter, which we group into five categories: social, romance, family, organizational, and parasocial. Using these categories, we show that the interpersonal relationship type leads to notable differences in (1) word and linguistic patterns, (2) shared topic diversity, and (3) network proximity. Using these labels as training data, we train classification models using the interaction data between two users to show that relationship types can be inferred with high F-1 scores. Finally, we show that these features can be used for predicting future diffusion of information, as in predicting whether a future retweet will occur.

Recommended citation: N/A TBA