Community Aliveness: Discovering Interaction Decay Patterns in Online Social Communities
Mohammed Abufouda

TL;DR
This paper introduces models to predict interaction decay in online social communities, using network attributes to identify inactive members and potentially prevent community obsolescence.
Contribution
It presents two novel prediction models for community member inactivity, validated with real data, achieving high accuracy and F1-scores, and offers insights into community decay patterns.
Findings
Prediction models achieve up to 0.91 F1-score and 0.83 accuracy.
Network attributes correlate with member activity and decay patterns.
Community structure can identify inactive members to support community sustainability.
Abstract
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 or 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 of decayed communities extracted from the…
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