Laboratoire Informatique, Image et Interaction (L3i)

Title : Predicting group success in Meetup
Abstract : Success of Meetup groups is of utmost importance for the members who organize them. Given a wide variety of such groups, a single metric may not be indicative of success for different groups ; rather, success measure should be specific to the interest of a group. In this work, accounting for the group diversity, we systematically define Meetup group success metrics and use them to generate targets for our machine learnt models. The data study reveals the key players (such as core members, new members etc) behind the success of the Meetup groups. This study leverages semantic, syntactic, temporal and location based features to discriminate between successful and unsuccessful groups. Finally, we present a model to predict success of the Meetup groups with high accuracy (81% with AUC=0.86). Our approach generalizes well across groups, categories and cities. Additionally, the model performs reasonably well for new groups with little history (cold start problem), exhibiting high accuracy for the cross city validation.