On Informative Tweet Identification For Tracking Mass Events

TL;DR

This paper explores machine learning techniques to automatically identify informative tweets during mass events, combining handcrafted and learned features to improve tracking accuracy.

Contribution

It introduces a hybrid model that integrates handcrafted and automatically learned features, outperforming traditional methods in identifying informative tweets.

Findings

Hybrid model outperforms traditional approaches

Automatically learned features improve accuracy

Effective for real-time event tracking

Abstract

Twitter has been heavily used as an important channel for communicating and discussing about events in real-time. In such major events, many uninformative tweets are also published rapidly by many users, making it hard to follow the events. In this paper, we address this problem by investigating machine learning methods for automatically identifying informative tweets among those that are relevant to a target event. We examine both traditional approaches with a rich set of handcrafted features and state of the art approaches with automatically learned features. We further propose a hybrid model that leverages both the handcrafted features and the automatically learned ones. Our experiments on several large datasets of real-world events show that the latter approaches significantly outperform the former and our proposed model performs the best, suggesting highly effective mechanisms for tracking mass events.