ET-LDA: Joint Topic Modeling for Aligning Events and their Twitter Feedback

TL;DR

This paper introduces ET-LDA, a unified Bayesian model that jointly performs topic modeling and event segmentation on Twitter data during broadcast events, improving over existing separate methods.

Contribution

It proposes a novel joint model that simultaneously extracts event topics and segments events from tweets, addressing their interdependence.

Findings

Significant improvement over baseline models in quantitative evaluations.

Effective qualitative analysis demonstrating better event segmentation.

Applicable to large-scale Twitter datasets from diverse domains.

Abstract

During broadcast events such as the Superbowl, the U.S. Presidential and Primary debates, etc., Twitter has become the de facto platform for crowds to share perspectives and commentaries about them. Given an event and an associated large-scale collection of tweets, there are two fundamental research problems that have been receiving increasing attention in recent years. One is to extract the topics covered by the event and the tweets; the other is to segment the event. So far these problems have been viewed separately and studied in isolation. In this work, we argue that these problems are in fact inter-dependent and should be addressed together. We develop a joint Bayesian model that performs topic modeling and event segmentation in one unified framework. We evaluate the proposed model both quantitatively and qualitatively on two large-scale tweet datasets associated with two events from different domains to show that it improves significantly over baseline models.