User fluctuation in communities: a forum case

TL;DR

This paper investigates user fluctuation in online communities by detecting community evolution and modeling user joining and staying behaviors using logistic regression, aiming to improve community support strategies.

Contribution

It introduces an experimental framework for analyzing user dynamics in communities, focusing on feature-based modeling of user joining and retention behaviors.

Findings

Identified features influencing user retention and joining.

Developed logistic regression models for user behavior prediction.

Established a foundation for future experiments to enhance model accuracy.

Abstract

Understanding fluctuation of users help stakeholders to provide a better support to communities. Below we present an experiment where we detect communities, their evolution and based on the data characterize users that stay, leave or join a community. Using a resulted feature set and logistic regression we operate with models of users that are joining and users that are staying in a community. In the related work we emphasize a number of features we will include in our future experiments to enhance train accuracy. This work represents a first from a series of experiments devoted to user fluctuation in communities.