Factors Contributing to Dropping-out in an Online Health Community: Static and Longitudinal Analyses

Abstract

Dropping-out, which refers to when an individual abandons an intervention, is common in Internet-based studies as well as in online health communities. Community facilitators and health researchers are interested in this phenomenon because it usually indicates dissatisfaction towards the community and/or its failure to deliver expected benefits. In this study, we propose a method to identify dropout members from a large public online breast cancer community. We then study quantitatively what longitudinal factors of participation are correlated with dropping-out. Our experimental results suggest that dropout members discuss diagnosis- and treatment-related topics more than other topics. Furthermore, in the time before withdrawing from the community, dropout members tend to initiate more discussions but do not receive adequate response from the other members. We also discuss implications of our results and challenges in dropout-member identification. This study contributes to further understanding community participation and opens up a number of future research questions.

Figure 1.

How topic frequencies change through time before members’ dropping-out. X axes, which are in reserve order, represent the time point before members’ dropping-out. Y axis is the average topic frequency of all posts that are published in the corresponding time. Units of x axes in (a)(d), (b)(e), and (c)(f) are weeks, days, and post orders, respectively.

Figure 2.

How percentage of initial posts and number of replies change through time before members’ dropping-out. X axes, which are in reserve order, represent the time point before members’ dropping-out. Units of x axes in (a)(d), (b)(e), and (c)(f) are weeks, days, and post orders, respectively.

Figure 3.

How average sentiment score changes through time before members’ dropping-out. X axes, which are in reserve order, represent the time point before members’ dropping-out. The first three figures show the average score of posts including both initial and reply, and the last three figures distinguish the two. Units of x axes in (a)(d), (b)(e), and (c)(f) are weeks, days, and post orders, respectively.