Predicting Continued Participation in Online Health Forums

Farig Sadeque, Thamar Solorio, Ted Pedersen, Prasha Shrestha, Steven Bethard
2015 Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis  
Online health forums provide advice and emotional solace to their users from a social network of people who have faced similar conditions. Continued participation of users is thus critical to their success. In this paper, we develop machine learning models for predicting whether or not a user will continue to participate in an online health forum. The prediction models are trained and tested over a large dataset collected from the support group based social networking site dailystrength.org. We
more » ... find that our models can predict continued participation with over 83% accuracy after as little as 1 month observing the user's activities, and that performance increases rapidly up to 1 year of observation. We also show that features such as the time since a user's last activity are consistently predictive regardless of the length of the observation period, while other features, such as the number of times a user replies to others, decrease in predictiveness as the observation period grows.
doi:10.18653/v1/w15-2602 dblp:conf/acl-louhi/SadequeSPSB15 fatcat:zjx4g54v4ffdxms3uiqew5maaa