Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction
Mohammed Abufouda

TL;DR
This study analyzes inactivity decay in online communities, identifying key cascade patterns and developing machine learning models to predict community collapse, with implications for understanding and preventing social network decay.
Contribution
It introduces a comprehensive analysis of decay patterns in online communities and proposes a predictive framework for cascade size and virality, validated on StackExchange data.
Findings
Decay patterns differ significantly between active and decayed communities.
Multiple network measures better explain decay than single measures.
Expert members influence community activity levels.
Abstract
Recently, many online social networks, such as MySpace, Orkut, and Friendster, have faced inactivity decay of their members, which contributed to the collapse of these networks. The reasons, mechanics, and prevention mechanisms of such inactivity decay are not fully understood. In this work, we analyze decayed and alive sub-websites from the StackExchange platform. The analysis mainly focuses on the inactivity cascades that occur among the members of these communities. We provide measures to understand the decay process and statistical analysis to extract the patterns that accompany the inactivity decay. Additionally, we predict cascade size and cascade virality using machine learning. The results of this work include a statistically significant difference of the decay patterns between the decayed and the alive sub-websites. These patterns are mainly: cascade size, cascade virality,…
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