Analyzing Key Users' behavior trends in Volunteer-Based Networks

• URL: http://arxiv.org/abs/2310.05978v1
• Date: Wed, 4 Oct 2023 06:42:21 GMT
• Title: Analyzing Key Users' behavior trends in Volunteer-Based Networks
• Authors: Nofar Piterman, Tamar Makov, and Michael Fire
• Abstract summary: The behavior of volunteers in volunteer-based social networks has been studied extensively in recent years. We develop two novel algorithms: the first reveals key user behavior patterns over time; the second utilizes machine learning methods to generate a forecasting model. To evaluate our algorithms, we utilized data from over 2.4 million users on a peer-to-peer food-sharing online platform.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online social networks usage has increased significantly in the last decade and continues to grow in popularity. Multiple social platforms use volunteers as a central component. The behavior of volunteers in volunteer-based networks has been studied extensively in recent years. Here, we explore the development of volunteer-based social networks, primarily focusing on their key users' behaviors and activities. We developed two novel algorithms: the first reveals key user behavior patterns over time; the second utilizes machine learning methods to generate a forecasting model that can predict the future behavior of key users, including whether they will remain active donors or change their behavior to become mainly recipients, and vice-versa. These algorithms allowed us to analyze the factors that significantly influence behavior predictions. To evaluate our algorithms, we utilized data from over 2.4 million users on a peer-to-peer food-sharing online platform. Using our algorithm, we identified four main types of key user behavior patterns that occur over time. Moreover, we succeeded in forecasting future active donor key users and predicting the key users that would change their behavior to donors, with an accuracy of up to 89.6%. These findings provide valuable insights into the behavior of key users in volunteer-based social networks and pave the way for more effective communities-building in the future, while using the potential of machine learning for this goal.

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