BANANA at WNUT -2020 Task 2: Identifying COVID -19 Information on T witter by Combining Deep Learning and Transfer Learning Models (2020)

Abstract: The outbreak COVID-19 virus caused a significant impact on the health of people all over the world. Therefore, it is essential to have a piece of constant and accurate information about the disease with everyone. This paper describes our prediction system for WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets. The dataset for this task contains size 10,000 tweets in English labeled by humans. The ensemble model from our three transformer and deep learning models is used for the final prediction. The experimental result indicates that we have achieved F1 for the INFORMATIVE label on our systems at 88.81% on the test set.

## introduction the rapid spread of the coronavirus (covid-19) has caused a global health crisis. this virus is hazardous to people's health and causes a big panic all over the world. statistics show that each day there are 4 million tweets related to covid-19 on twitter @xcite . therefore, it is essential to keep track of the information associated with this disease. along with the development of many social networking platforms such as twitter and facebook. this is the primary way that helps people capture information about covid-19 regularly. however, there is much content appearing daily on these social media platforms. most of them do not have information about the status of covid-19, such as the number of suspected cases or cases near the user's area. in this article, we present our approach at wnut-2020 task 2 @xcite to identify tweets containing information about covid-19 on the social networking platform twitter or not. a tweet is believed to have information if it includes information such as recovered, suspected, confirmed, and death cases and location or travel history of the patients. specifically, we described the problem as follows. several examples are shown in ## related work during the happening of the covid-19 pandemic, the information about the number of infected cases, the number of patients is vital for governments. @xcite constructed a real-time database for tracking the covid-19 around the world. this dataset is collected by experts from the world health organization (who), us cdc, and other medical agencies worldwide and is operated by john hopkins university. also, there are many other covid-19 datasets such as multilingual data collected on twitter from january 2020 @xcite or real world worry dataset (rwwd) @xcite . besides, on social media, the spreading of covid-19 information is extremely fast and enormous and sometimes leads to misinformation. @xcite conducted a pilot study about detecting misinformation about covid-19 on twitter by analyzing tweets using standard social media analytics techniques. from the researching results, the authors want to help authorities and social media users counter misinformation. moreover, the rumors and conspiracy theories within the emergence times of covid-19 spreading had made communities feel fearmongering and panicky, which lead to racism about covid-19 patients and citizens from infected countries, and mass purchase of face masks as well as the shortage of necessaries, according to @xcite . thus it is necessary to identify the right information from the social media text. ## dataset the dataset provided by @xcite contains 10,000 english tweets about covid-19, which is used to automatically identify whether a tweet contains useful information about the covid-19 (informative) or not (uninformative). there are 4,719 informative tweets and 5,281 uninformative tweets in the dataset, and three different annotators annotate each tweet. the inter-annotator agreement calculated by fleiss' kappa score of the dataset is 81.80%. also, the dataset is split into the training, development, and test sets with proportion 7-1-2. ## methodologies in this paper, we propose an ensemble method that combines the deep learning models with the transfer learning models to identify information about covid-19 from users' tweets. ## conclusion and future work this paper has addressed our work on the wnut-2020 task 2: identifying covid-19 information on twitter. we proposed our ensemble model combining the deep learning models and the transfer learning models for detecting information about covid-19 from users' tweets. our ensemble model achieved 91.00% by accuracy and 90.65% by f1 on the development set, and achieved 89.04% by accuracy and 88.81% by f1 on the public test set, which ranked #25 in the competition. in the future, we will improve our model's performance by exploring different features of the users' tweets and transfer learning models with fine-tuning techniques. finally, we hope our study can be applied in practice for detecting covid-19 from social networks to support the covid-19 battle all over the world.