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 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, cascade duration, and cascade similarity. Additionally, the contributed prediction framework showed satisfactory prediction results compared to a baseline predictor. Supported by empirical evidence, the main findings of this work are: (1) the decay process is not governed by only one network measure; it is better described using multiple measures; (2) the expert members of the StackExchange sub-websites were mainly responsible for the activity or inactivity of the StackExchange sub-websites; (3) the Statistics sub-website is going through decay dynamics that may lead to it becoming fully-decayed; and (4) decayed sub-websites were originally less resilient to inactivity decay, unlike the alive sub-websites

Community Aliveness: Discovering Interaction Decay Patterns in Online Social Communities

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.Comment: pre-print for the 4th European Network Intelligence Conference - 11-12 September 2017 Duisburg, German

Learning From Networked-data: Methods and Models for Understanding Online Social Networks Dynamics

Abstract Nowadays, people and systems created by people are generating an unprecedented amount of data. This data has brought us data-driven services with a variety of applications that affect people’s behavior. One of these applications is the emergent online social networks as a method for communicating with each other, getting and sharing information, looking for jobs, and many other things. However, the tremendous growth of these online social networks has also led to many new challenges that need to be addressed. In this context, the goal of this thesis is to better understand the dynamics between the members of online social networks from two perspectives. The first perspective is to better understand the process and the motives underlying link formation in online social networks. We utilize external information to predict whether two members of an online social network are friends or not. Also, we contribute a framework for assessing the strength of friendship ties. The second perspective is to better understand the decay dynamics of online social networks resulting from the inactivity of their members. Hence, we contribute a model, methods, and frameworks for understanding the decay mechanics among the members, for predicting members’ inactivity, and for understanding and analyzing inactivity cascades occurring during the decay. The results of this thesis are: (1) The link formation process is at least partly driven by interactions among members that take place outside the social network itself; (2) external interactions might help reduce the noise in social networks and for ranking the strength of the ties in these networks; (3) inactivity dynamics can be modeled, predicted, and controlled using the models contributed in this thesis, which are based on network measures. The contributions and the results of this thesis can be beneficial in many respects. For example, improving the quality of a social network by introducing new meaningful links and removing noisy ones help to improve the quality of the services provided by the social network, which, e.g., enables better friend recommendations and helps to eliminate fake accounts. Moreover, understanding the decay processes involved in the interaction among the members of a social network can help to prolong the engagement of these members. This is useful in designing more resilient social networks and can assist in finding influential members whose inactivity may trigger an inactivity cascade resulting in a potential decay of a network

CRATER: Case-based Reasoning Framework for Engineering an Adaptation Engine in Self-Adaptive Software Systems

Self-adaptation allows software systems to autonomously adjust their behavior during run-time by handling all possible operating states that violate the requirements of the managed system. This requires an adaptation engine that receives adaptation requests during the monitoring process of the managed system and responds with an automated and appropriate adaptation response. During the last decade, several engineering methods have been introduced to enable self-adaptation in software systems. However, these methods lack addressing (1) run-time uncertainty that hinders the adaptation process and (2) the performance impacts resulted from the complexity and the large number of the adaptation space. This paper presents CRATER, a framework that builds an external adaptation engine for self-adaptive software systems. The adaptation engine, which is built on Case-based Reasoning, handles the aforementioned challenges together. This paper is braced with an experiment illustrating the benefits of this framework. The experimental results shows the potential of CRATER in terms handling run-time uncertainty and adaptation remembrance that enhances the performance for large number of adaptation space

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 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

Learning From Networked-data: Methods and Models for Understanding Online Social Networks Dynamics

Abstract Nowadays, people and systems created by people are generating an unprecedented amount of data. This data has brought us data-driven services with a variety of applications that affect people’s behavior. One of these applications is the emergent online social networks as a method for communicating with each other, getting and sharing information, looking for jobs, and many other things. However, the tremendous growth of these online social networks has also led to many new challenges that need to be addressed. In this context, the goal of this thesis is to better understand the dynamics between the members of online social networks from two perspectives. The first perspective is to better understand the process and the motives underlying link formation in online social networks. We utilize external information to predict whether two members of an online social network are friends or not. Also, we contribute a framework for assessing the strength of friendship ties. The second perspective is to better understand the decay dynamics of online social networks resulting from the inactivity of their members. Hence, we contribute a model, methods, and frameworks for understanding the decay mechanics among the members, for predicting members’ inactivity, and for understanding and analyzing inactivity cascades occurring during the decay. The results of this thesis are: (1) The link formation process is at least partly driven by interactions among members that take place outside the social network itself; (2) external interactions might help reduce the noise in social networks and for ranking the strength of the ties in these networks; (3) inactivity dynamics can be modeled, predicted, and controlled using the models contributed in this thesis, which are based on network measures. The contributions and the results of this thesis can be beneficial in many respects. For example, improving the quality of a social network by introducing new meaningful links and removing noisy ones help to improve the quality of the services provided by the social network, which, e.g., enables better friend recommendations and helps to eliminate fake accounts. Moreover, understanding the decay processes involved in the interaction among the members of a social network can help to prolong the engagement of these members. This is useful in designing more resilient social networks and can assist in finding influential members whose inactivity may trigger an inactivity cascade resulting in a potential decay of a network