Analyzing Key Users' behavior trends in Volunteer-Based Networks
Nofar Piterman Tamar Makov Michael Fire
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.