Abstract

User response to contributed content in online social media depends on many factors. These include how the site lays out new content, how frequently the user visits the site, how many friends the user follows, how active these friends are, as well as how interesting or useful the content is to the user. We present a stochastic modeling framework that relates a user's behavior to details of the site's user interface and user activity and describe a procedure for estimating model parameters from available data. We apply the model to study discussions of controversial topics on Twitter, specifically, to predict how followers of an advocate for a topic respond to the advocate's posts. We show that a model of user behavior that explicitly accounts for a user discovering the advocate's post by scanning through a list of newer posts, better predicts response than models that do not.

Date

September 8, 2013

Authors

Tad Hogg, Kristina Lerman, Laura M Smith

Conference

2013 International Conference on Social Computing

Pages

63-68

Publisher

IEEE