Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction
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 anal...
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| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/3873601 |
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| author | Mohammed Abufouda |
| author_facet | Mohammed Abufouda |
| author_sort | Mohammed Abufouda |
| collection | DOAJ |
| description | 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 subwebsites from the Stack Exchange 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 subwebsites. These patterns are mainly cascade size, cascade virality, cascade duration, and cascade similarity. Additionally, the contributed prediction framework showed satisfactorily prediction results compared to a baseline predictor. Supported by empirical evidence, the main findings of this work are (1) there are significantly different decay patterns in the alive and the decayed subwebsites of the Stack Exchange; (2) the cascade’s node degrees contribute more to the decay process than the cascade’s virality, which indicates that the expert members of the Stack Exchange subwebsites were mainly responsible for the activity or inactivity of the Stack Exchange subwebsites; (3) the Statistics subwebsite is going through decay dynamics that may lead to it becoming fully-decayed; (4) the decay process is not governed by only one network measure, it is better described using multiple measures; (5) decayed subwebsites were originally less resilient to inactivity decay, unlike the alive subwebsites; and (6) network’s structure in the early stages of its evolution dictates the activity/inactivity characteristics of the network. |
| format | Article |
| id | doaj-art-83da45d532664725ba59be8585d03a11 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-83da45d532664725ba59be8585d03a112025-02-03T06:44:39ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/38736013873601Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and PredictionMohammed Abufouda0Algorithm Accountability Lab, Computer Science Department, University of Kaiserslautern, GermanyRecently, 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 subwebsites from the Stack Exchange 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 subwebsites. These patterns are mainly cascade size, cascade virality, cascade duration, and cascade similarity. Additionally, the contributed prediction framework showed satisfactorily prediction results compared to a baseline predictor. Supported by empirical evidence, the main findings of this work are (1) there are significantly different decay patterns in the alive and the decayed subwebsites of the Stack Exchange; (2) the cascade’s node degrees contribute more to the decay process than the cascade’s virality, which indicates that the expert members of the Stack Exchange subwebsites were mainly responsible for the activity or inactivity of the Stack Exchange subwebsites; (3) the Statistics subwebsite is going through decay dynamics that may lead to it becoming fully-decayed; (4) the decay process is not governed by only one network measure, it is better described using multiple measures; (5) decayed subwebsites were originally less resilient to inactivity decay, unlike the alive subwebsites; and (6) network’s structure in the early stages of its evolution dictates the activity/inactivity characteristics of the network.http://dx.doi.org/10.1155/2018/3873601 |
| spellingShingle | Mohammed Abufouda Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction Complexity |
| title | Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction |
| title_full | Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction |
| title_fullStr | Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction |
| title_full_unstemmed | Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction |
| title_short | Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction |
| title_sort | postmortem analysis of decayed online social communities cascade pattern analysis and prediction |
| url | http://dx.doi.org/10.1155/2018/3873601 |
| work_keys_str_mv | AT mohammedabufouda postmortemanalysisofdecayedonlinesocialcommunitiescascadepatternanalysisandprediction |