UCD-CS at TREC 2021 Incident Streams Track

TL;DR

This paper describes UCD-CS's participation in the TREC 2021 Incident Streams Track, employing various machine learning approaches to classify and prioritize crisis-related social media posts, achieving top performance.

Contribution

The paper introduces multiple machine learning techniques, including multi-task learning and ensemble methods, applied to crisis tweet classification and prioritization, with publicly available code for reproducibility.

Findings

Achieved top scores in multiple evaluation metrics.

Demonstrated effectiveness of multi-task learning approaches.

Provided reproducible code for crisis tweet classification.

Abstract

In recent years, the task of mining important information from social media posts during crises has become a focus of research for the purposes of assisting emergency response (ES). The TREC Incident Streams (IS) track is a research challenge organised for this purpose. The track asks participating systems to both classify a stream of crisis-related tweets into humanitarian aid related information types and estimate their importance regarding criticality. The former refers to a multi-label information type classification task and the latter refers to a priority estimation task. In this paper, we report on the participation of the University College Dublin School of Computer Science (UCD-CS) in TREC-IS 2021. We explored a variety of approaches, including simple machine learning algorithms, multi-task learning techniques, text augmentation, and ensemble approaches. The official evaluation results indicate that our runs achieve the highest scores in many metrics. To aid reproducibility, our code is publicly available at https://github.com/wangcongcong123/crisis-mtl.