Year: 2025
Type: article
Abstract: The use of online social platforms and networks has surged over the past decade and continues to grow in popularity. In many social networks, volunteers play a central role, and their behavior in volunteer-based networks has been studied extensively. Here, we explore the development of volunteer-based social networks, focusing on the activities and behaviors of the most influential users. We introduce two innovative algorithms: the first outlines the evolution of volunteers’ behavior patterns over time, while the second employs machine learning techniques to forecast their future behavior, including whether they will remain active donors or become mainly recipients, and vice-versa. These algorithms allowed us to analyze the factors that significantly influence behavior predictions. We utilized data from over 2.4 million users on a peer-to-peer food-sharing online platform. Using our algorithm, we identified four key user behavior patterns over time to evaluate our algorithms. Moreover, we succeeded in forecasting future active donor key users and predicting the key users that would change their behavior toward donors, with an accuracy of up to 89.6%. The insights gained from our analysis not only shed light on the behavioral patterns of key users in volunteer-driven networks but also highlight the potential of machine learning in enhancing community engagement and building strategies for the future.
Source: Journal Of Big Data
Authors: Nofar Piterman, Tamar Makov, Michael Fire
Institution: Ben-Gurion University of the Negev
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,290
Funder: Israel Science Foundation
Award: G6879147875
OpenAlex ID: W4410089065