Talk by Dr. Medvedev on Web and Social data modeling seminar: "Modelling structure and predicting dynamics of discussion threads in online boards using Hawkes processes"
January 26th (Friday)
National Institute of Informatics
Modelling structure and predicting dynamics of discussion threads in online boards using Hawkes processes
University of Namur
Online social platforms provide a fruitful source of information about social interaction. Depending on the platform, various tree-like cascading patterns emerge as a consequence of such interaction. For example, on Twitter or on Facebook people interact via resharing messages, which turns into cascade trees of reshares, in email networks people forward messages to their peers resulting in trees of email forwards, in online boards like Digg or Reddit people interact via discussing particular posts, which leaves a trace of discussion trees.
The two main questions arise: what is the shape of these cascades and how to predict the dynamics of their evolution? The question of evolution of discussion threads is now gradually being understood. By now researchers studied only the structural evolution of discussion trees and the dynamical properties are left out of consideration. We note there was proposed a sort of a mean-field model for dynamics and structure, however the average nature of the model has limited utility in practice. We consider cascades given by discussion trees of posts in online board Reddit. The dataset of Reddit discussion threads consists of all posts and comments submitted to Reddit from Jan, 2008 till Jan, 2015. The dataset in total contains more than 150 million posts and around 1.4 billion comments. We propose a model of discussion trees generation based on the self-exciting Hawkes processes, which represents both the tree structure and temporal information. We use the dataset of Reddit discussion threads to show that structurally trees resemble Galton-Watson trees with a special root offspring distribution, and distinct the cases when the dynamics of comments attraction can be well predicted using Hawkes processes.
Ryota Kobayashi (r-koba(at)nii.ac.jp)