SCIFNET: Stance community identification of topic persons using friendship network analysis

A topic that involves communities with different competing viewpoints or stances is usually reported by a large number of documents. Knowing the association between the persons mentioned in the documents can help readers construct the background knowledge of the topic and comprehend the numerous topic documents more easily. In this paper, we investigate the stance community identification problem where the goal is to cluster important persons mentioned in a set of topic documents into stance-coherent communities. We propose a stance community identification method called SCIFNET, which constructs a friendship network of topic persons from topic documents automatically. Stance community expansion and stance community refinement techniques are designed to identify stance-coherent communities of topic persons in the friendship network and to detect persons who are stance-irrelevant about the topic. The results of experiments based on real-world datasets demonstrate the effectiveness of SCIFNET and show that it outperforms many well-known community detection approaches and clustering algorithms.