Competition

WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets

If the task and/or its data is useful for your work, please cite the task description paper:

@inproceedings{covid19tweet,
title = {{WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets}},
author = {Dat Quoc Nguyen and Thanh Vu and Afshin Rahimi and Mai Hoang Dao and Linh The Nguyen and Long Doan},
booktitle = {Proceedings of the 6th Workshop on Noisy User-generated Text},
year = {2020}
}

For this task, participants are asked to develop systems that automatically identify whether an English Tweet related to the novel coronavirus (COVID-19) is informative or not. Such informative Tweets provide information about recovered, suspected, confirmed and death cases as well as location or travel history of the cases.

Data is released on June 21, 2020!

Official evaluation will be between August 17, 2020 and August 21, 2020 (anytime on earth) !

PLEASE REGISTER HERE TO PARTICIPATE (we do not accept submissions from unregistered emails/teams).

There is a mailing list for future announcements.

Introduction

The goals of our shared task are: (1) To develop a language processing task that potentially impacts research and downstream applications, and (2) To provide the community with a new dataset for identifying informative COVID-19 English Tweets.

As of mid-June 2020, the COVID-19 outbreak has led to about 445K deaths and 8.2M+ infected patients from 215 regions & countries, creating fear and panic for people all around the world. Recently, much attention has been paid to building monitoring systems (e.g. The Johns Hopkins Coronavirus Dashboard) to track the development of the outbreak and to provide users the information related to the virus, e.g. any new suspicious/confirmed cases near/in the users' regions. Note that most of the "official" sources used in the tracking tools are not kept up to date with the current outbreak situation, e.g. WHO updates the outbreak information only once a day. Those monitoring systems thus use social network data, e.g. from Twitter, as a real-time alternative source for updating the outbreak information, generally by crowdsourcing or searching for related information manually. However, the outbreak has been spreading rapidly, we observe a massive amount of data on social networks, e.g. about 4 millions of COVID-19 English Tweets daily on the Twitter platform in which the majority of these Tweets are uninformative. Thus it is important to be able to select the informative ones (e.g. COVID-19 Tweets related to new cases or suspicious cases) for downstream applications. However, manual approaches to identify the informative Tweets require significant human efforts, and thus are costly.

To help handle the problem, this shared task is to automatically identify whether a COVID-19 English Tweet is informative or not. Such informative Tweets provide information about recovered, suspected, confirmed and death cases as well as location or travel history of the cases. To be able to achieve the goals of this shared task, we construct a dataset of 10K COVID19 English tweets. We believe our dataset and systems developed for this shared task will be beneficial for the development of COVID-19 related monitoring systems.

Important Dates

• Data available: June 21, 2020
• Registration closed: August 14, 2020
• Evaluation window: August 17, 2020 - August 21, 2020
• System description papers submitted: September 6, 2020
• Notification of acceptance: September 28 2020
• Papers camera ready: October 09, 2020
• Workshop day: November 19, 2020

Task Organizers

• Dat Quoc Nguyen (VinAI Research, Vietnam)
• Thanh Vu (AEHRC, CSIRO, Australia)
• Afshin Rahimi (The University of Queensland, Australia)
• Mai Hoang Dao (VinAI Research, Vietnam)
• Linh The Nguyen (VinAI Research, Vietnam)
• Long Doan (VinAI Research, Vietnam)

Contact Us: If you have any questions, please feel free to contact us at wnut.sharedtask.covid19tweet@gmail.com.