Dynamic Seed Analysis in a Social Network for Maximizing Efficiency of Data Collection

Applying data mining techniques to social media can yield interesting perspectives to understanding individual and human behavior, detecting hot issues and topics, or discovering a group and community. However, it is difficult to gather the data related to a specific topic due to the main characteristics of social media data sets: data is large, noisy, and dynamic. To collect the data related to a specific topic and keyword efficiently, we propose a new algorithm that selects the best seed nodes with limited resources and time. The algorithm also evaluates various user influence and activity factors, and updates the seed nodes dynamically during the gathering process. Furthermore, we compare two data sets collected by the algorithm and existing approaches.