NIT _ COVID -19 at WNUT -2020 Task 2: Deep Learning Model R o BERT a for Identify Informative COVID -19 E nglish Tweets (2020)

Abstract: This paper presents the model submitted by NIT COVID-19 team for identified informative COVID-19 English tweets at WNUT-2020 Task2. This shared task addresses the problem of automatically identifying whether an English tweet related to informative (novel coronavirus) or not. These informative tweets provide information about recovered, confirmed, suspected, and death cases as well as location or travel history of the cases. The proposed approach includes pre-processing techniques and pre-trained RoBERTa with suitable hyperparameters for English coronavirus tweet classification. The performance achieved by the proposed model for shared task WNUT 2020 Task2 is 89.14% in the F1-score metric.

## introduction present the world is suffering from a novel coronavirus @xcite from december-2019 to till date. it spreads all the continents and almost all countries in the world. now a days many online tools or resources @xcite provide coronavirus information to people in the world, but these resources are not continuously up to date. they are updating in a particular time interval. the world's major trusted resources like who (world health organization) @xcite also update this covid-19 related information once a day. this pandemic covid-19 has been spreading rapidly @xcite . in this situation world is looking for an automatic monitoring system for identifying useful information about covid-19. one of the most popular and trusted online social network platform @xcite is twit-ter, an alternative source for updating the present pandemic information. twitter is getting nearly 4 million covid-19 tweets daily @xcite , but only a few of them are informative. twitter api provides tweet information openly to the research community. this is very useful to identify automatically informative covid-19 tweets. in this wnut-2020 shared task-2, we proposed a model for automatically identifying covid-19 informative tweets, which describes information about confirmed, recovered, and suspected and death cases as well as location or travel history of the covid-19 affected patients. we have used pretrained deep learning "roberta" @xcite with word-level embedding along with some preprocessing techniques. we also experimented with cnn @xcite and pre-trained bert @xcite )-encoded sentences as input. the proposed deep learning model gives a high f1-score compares to the above approaches. after discussed the related work in section 2, section 3 discussed the methodology and the data in detail and discusses the results in section 4. we analyze the results and error analysis in section 5 and section 6 concludes the paper. ## realated work informative tweet identification has been of interest for researchers in recent years. early work in the related fields include detection of online media (alrashdi and o'keefe, 2019), racism (kabir and madria, 2019), and disaster @xcite . papers published in recent years include @xcite , which introduces the tweet classification detection dataset and experiments with different machine learning models, such as naıve bayes, logistic regression, random forests, and linear svms to investigate hate speech and disrespectful language, which experiments further on the same dataset using svms with n-grams and skip-grams features, and @xcite and @xcite , both exploring the performance of neural networks and comparing them with other machine learning ap-proaches. also, there has been published a couple of surveys covering various work addressing the identification of abusive, toxic, and offensive language, hate speech, etc., and their methodology including @xcite and @xcite . additionally, there were several workshops and shared tasks on offensive language identification and related problems, including ta-cos2, abusive language online3, and trac4 and germeval @xcite , which shows the significance of the problem. ## methodology the methodology used for wnut-2020 task 2, consists of a preprocessing phase and a deep learning model implementation phase. ## results and error analysis finally, we done experiment on the baseline models in 3.3 and the model described in section 3.2 using the covid-19 english tweets data sets.the task organizers did not provide any baseline scores for this task. for better results on the validation data set, roberta model trained on training data set, tested on validation data set and obtained results shown in the we have submitted results of our model on test data as part of wnut-2020 task 2 for recognising covid-19 english informative tweets. the organizers released unlabeled test data for the final evaluation phase. this is a large set of 12k tweets, and the actual 2k test tweets by which our model has evaluated are hidden in this large set. our model output metric values on test data are f1score is 89.14, precision is 85.94, recall is 92.35, and accuracy value is 89.35 as shown in roberta outperformed well,compare with bert, by 0.20 % f1-score. the baseline model results, however,worse than the roberta and , probably due the fact that the roberta model requires fine-tuning for more task specific representations. the majority of roberta errors are in informative class. ## conclusion and future work label f1-score accuracy informative 89.89 90.23 uninformative 88.16 88.32