Doina Caragea


Identification of Fine-Grained Location Mentions in Crisis Tweets
Sarthak Khanal | Maria Traskowsky | Doina Caragea
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Identification of fine-grained location mentions in crisis tweets is central in transforming situational awareness information extracted from social media into actionable information. Most prior works have focused on identifying generic locations, without considering their specific types. To facilitate progress on the fine-grained location identification task, we assemble two tweet crisis datasets and manually annotate them with specific location types. The first dataset contains tweets from a mixed set of crisis events, while the second dataset contains tweets from the global COVID-19 pandemic. We investigate the performance of state-of-the-art deep learning models for sequence tagging on these datasets, in both in-domain and cross-domain settings.

Multimodal Semi-supervised Learning for Disaster Tweet Classification
Iustin Sirbu | Tiberiu Sosea | Cornelia Caragea | Doina Caragea | Traian Rebedea
Proceedings of the 29th International Conference on Computational Linguistics

During natural disasters, people often use social media platforms, such as Twitter, to post information about casualties and damage produced by disasters. This information can help relief authorities gain situational awareness in nearly real time, and enable them to quickly distribute resources where most needed. However, annotating data for this purpose can be burdensome, subjective and expensive. In this paper, we investigate how to leverage the copious amounts of unlabeled data generated on social media by disaster eyewitnesses and affected individuals during disaster events. To this end, we propose a semi-supervised learning approach to improve the performance of neural models on several multimodal disaster tweet classification tasks. Our approach shows significant improvements, obtaining up to 7.7% improvements in F-1 in low-data regimes and 1.9% when using the entire training data. We make our code and data publicly available at


Multi-task Learning to Enable Location Mention Identification in the Early Hours of a Crisis Event
Sarthak Khanal | Doina Caragea
Findings of the Association for Computational Linguistics: EMNLP 2021

Training a robust and reliable deep learning model requires a large amount of data. In the crisis domain, building deep learning models to identify actionable information from the huge influx of data posted by eyewitnesses of crisis events on social media, in a time-critical manner, is central for fast response and relief operations. However, building a large, annotated dataset to train deep learning models is not always feasible in a crisis situation. In this paper, we investigate a multi-task learning approach to concurrently leverage available annotated data for several related tasks from the crisis domain to improve the performance on a main task with limited annotated data. Specifically, we focus on using multi-task learning to improve the performance on the task of identifying location mentions in crisis tweets.

Stance Detection in COVID-19 Tweets
Kyle Glandt | Sarthak Khanal | Yingjie Li | Doina Caragea | Cornelia Caragea
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

The prevalence of the COVID-19 pandemic in day-to-day life has yielded large amounts of stance detection data on social media sites, as users turn to social media to share their views regarding various issues related to the pandemic, e.g. stay at home mandates and wearing face masks when out in public. We set out to make use of this data by collecting the stance expressed by Twitter users, with respect to topics revolving around the pandemic. We annotate a new stance detection dataset, called COVID-19-Stance. Using this newly annotated dataset, we train several established stance detection models to ascertain a baseline performance for this specific task. To further improve the performance, we employ self-training and domain adaptation approaches to take advantage of large amounts of unlabeled data and existing stance detection datasets. The dataset, code, and other resources are available on GitHub.


Cross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup
Jishnu Ray Chowdhury | Cornelia Caragea | Doina Caragea
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Distinguishing informative and actionable messages from a social media platform like Twitter is critical for facilitating disaster management. For this purpose, we compile a multilingual dataset of over 130K samples for multi-label classification of disaster-related tweets. We present a masking-based loss function for partially labelled samples and demonstrate the effectiveness of Manifold Mixup in the text domain. Our main model is based on Multilingual BERT, which we further improve with Manifold Mixup. We show that our model generalizes to unseen disasters in the test set. Furthermore, we analyze the capability of our model for zero-shot generalization to new languages. Our code, dataset, and other resources are available on Github.


KSU KDD: Word Sense Induction by Clustering in Topic Space
Wesam Elshamy | Doina Caragea | William Hsu
Proceedings of the 5th International Workshop on Semantic Evaluation