Swarm Intelligence Inspired Approach for Dynamic Tracking of Members’ Interests in Online Discussion Groups

Abstract

With the explosive growth of online discussions nowadays, fostering interesting and satisfying group discussions for all group members has become challenging. In this work, we particularly seek to address the issue of online group formation where diverse participants with various topic interest levels gather and carry-on open-ended synchronous discussions in small groups. In these groups, members often encounter various difficulties, especially when their degree of interest in the discussed topic decreases drastically. Our proposed method is a boids-model inspired algorithm that captures group discussion dynamics in terms of the evolution of discussed topics over time, and variations in group members’ degree of interest for the discussed topics. Discussion topics are modeled as multidimensional vectors where dimensions correspond to factors that are associated with group members’ interest vectors. In this paper, we present the proposed method and discuss its potential for achieving dynamic tracking of variations in individuals’ interests and detection of left-out members. We confirmed the feasibility as well as the meaningfulness of the proposed approach through numerical simulations. In addition, we outline our future plans to investigate the meaningfulness of our approach through more complex simulations and interactions involving actual users.