IRL ab@ IITBHU at WNUT -2020 Task 2: Identification of informative COVID -19 E nglish Tweets using BERT (2020)

Abstract: This paper reports our submission to the shared Task 2: Identification of informative COVID-19 English tweets at W-NUT 2020. We attempted a few techniques, and we briefly explain here two models that showed promising results in tweet classification tasks: DistilBERT and FastText. DistilBERT achieves a F1 score of 0.7508 on the test set, which is the best of our submissions.

## introduction the www and then web 2.0 enabled people to express their views and opinions through blog posts, online forums, product review websites, and social media. millions of people use social network sites like facebook, twitter, linkedin, and google plus to express their emotions, opinions, and share views on different issues that matter in their lives. the present pandemic situation has severely reduced physical interaction and forced us to use virtual platforms where people inform and influence others. social media generates a large volume of sentiment-rich data every day through tweets, status updates, blog posts, comments, reviews and so on. we often depend upon such user-generated content online to a great extent for decision making. as the pandemic is spreading worldwide, online media is flooded with uncontrolled user-generated content, lot of which are not at all moderated or quality-checked. it, therefore, becomes more important to understand this spread, get aware of the differences between informative and uninformative news, and act upon it wisely. coronavirus causes illness, which can vary from common cold and cough to sometimes more severe disease. sars-cov-2 (n-coronavirus) is the new virus of this family, which has led to more than 1 million deaths worldwide foot_0 . regularly, we find thousands of tweets by users worldwide, expressing their sentiments on covid-19. although these tweets often carry valuable information, they are unstructured and, are, therefore, challenging to process. so, analyzing these tweets is quite essential to understand whether they are informative ones or not. we used a few machine learning approaches to classify test tweets as either informative or noninformative. before the classification, we cleaned the tweets, constructed a representation of tweets with different word embedding techniques, and then built the classification model. ## results and analysis we have used scikit-learn 8 machine learning package for the implementation. we have observed that distilbert and lr (fig 1 ) gave better f 1 score than others as shown in ## conclusion this study reports our system for shared task 2: identification of informative covid-19 english tweets in w-nut 2020. we performed a comparative analysis of machine learning models: logistic regression (lr), support vector machine (svm), and xg-boost (xgb), deep learning models: bilstm, gru with few embedding techniques: word2vec, glove, fasttext, bert, and distilbert. based on a series of experiments, we find that distilbert outperforms on this task. however, there is room for improvement. in the fu-ture, we plan to use other pre-trained models with some fine-tuning.