  Online Social Communities (OSCs) provide a medium for connecting people,
sharing news, eliciting information, and finding jobs, among others. The
dynamics of the interaction among the members of OSCs is not always growth
dynamics. Instead, a $\textit{decay}$ or $\textit{inactivity}$ dynamics often
happens, which makes an OSC obsolete. Understanding the behavior and the
characteristics of the members of an inactive community help to sustain the
growth dynamics of these communities and, possibly, prevents them from being
out of service. In this work, we provide two prediction models for predicting
the interaction decay of community members, namely: a Simple Threshold Model
(STM) and a supervised machine learning classification framework. We conducted
evaluation experiments for our prediction models supported by a $\textit{ground
truth}$ of decayed communities extracted from the StackExchange platform. The
results of the experiments revealed that it is possible, with satisfactory
prediction performance in terms of the F1-score and the accuracy, to predict
the decay of the activity of the members of these communities using
network-based attributes and network-exogenous attributes of the members. The
upper bound of the prediction performance of the methods we used is $0.91$ and
$0.83$ for the F1-score and the accuracy, respectively. These results indicate
that network-based attributes are correlated with the activity of the members
and that we can find decay patterns in terms of these attributes. The results
also showed that the structure of the decayed communities can be used to
support the alive communities by discovering inactive members.
