CSECU - DSG at WNUT -2020 Task 2: Exploiting Ensemble of Transfer Learning and Hand-crafted Features for Identification of Informative COVID -19 E nglish Tweets (2020)

Abstract: COVID-19 pandemic has become the trending topic on twitter and people are interested in sharing diverse information ranging from new cases, healthcare guidelines, medicine, and vaccine news. Such information assists the people to be updated about the situation as well as beneficial for public safety personnel for decision making. However, the informal nature of twitter makes it challenging to refine the informative tweets from the huge tweet streams. To address these challenges WNUT-2020 introduced a shared task focusing on COVID-19 related informative tweet identification. In this paper, we describe our participation in this task. We propose a neural model that adopts the strength of transfer learning and hand-crafted features in a unified architecture. To extract the transfer learning features, we utilize the state-of-the-art pre-trained sentence embedding model BERT, RoBERTa, and InferSent, whereas various twitter characteristics are exploited to extract the hand-crafted features. Next, various feature combinations are utilized to train a set of multilayer perceptron (MLP) as the base-classifier. Finally, a majority voting based fusion approach is employed to determine the informative tweets. Our approach achieved competitive performance and outperformed the baseline by 7% (approx.).

## introduction twitter is one of the most prominent microblogging platforms that provides a convenient way of sharing opinions and broadcasting news and information briefly among the mass community. people often prefer using twitter due to its real-time feature because compared to others, it contains most of the notable or official information. hence, information shared on this platform is very helpful during emergency situations. that is why, in the ongoing covid-19 pandemic, people are interested the first four authors have equal contributions. in seeking informative tweets related to covid-19. an informative tweet may contain the updated information of covid-19, new cases or death information, medicine or vaccine news, and updated guidelines from diverse sources. for instance, in classifying informative tweets on covid-19 is an emerging concept. nevertheless, there are some prior works on identifying informative tweets during crises or disasters. for example, a cnn based approach proposed by @xcite for informative tweets identification in disaster. later, @xcite and @xcite employed cnn with glove and word2vec features, respectively in the same context. more recently, a work regarding misinformation detection on covid-19 tweets @xcite has also been done. in this paper, we present our proposed systems submitted to the w-nut 2020 shared task 2 @xcite . the primary goal of the task is to identify informative tweets related to covid-19. we exploit the transfer learning features from bert, roberta, and infersent along with the n-gram and other hand-crafted features in a unified neural model. the rest of the contents are structured as follows: we describe our proposed framework in section 2 whereas section 3 includes the experimental details and performance analysis. we concluded this paper with plausible future notions in section 4. ## proposed framework in this section, we describe our proposed framework. our target is to identify the informative tweet based on its context. the overview of our proposed framework is presented in figure 1 . given a tweet, we explore various techniques to encode the tweet into effective feature vectors. we utilize three pre-trained embedding models including roberta, bert, and infersent to extract the effective transfer learning features. besides, we extract the n-gram and other hand-crafted features. the concatenated features are then passed to a multilayer perceptron (mlp) network for the training. we explore different feature combinations to train and extract the prediction from multiple mlp classifiers. finally, a majority voting based fusion scheme exploits all the predicted labels and determine the final one. ## conclusion and future directions in this paper, we have explored various transfer learning features along with a rich set of handcrafted features in an mlp based unified neural framework to identify the covid-19 related informative tweets. we analyzed the effect of each feature type on the classification performances. our systems achieved competitive performances among the participants' systems. in the future, we have a plan to exploit the vast amount of covid-19 related informative tweets to train and downstream fine-tune the various transfer learning models for extracting effective tweet representations. we also aim to inspect more proficient hand-crafted features for identifying covid-19 related informative tweets.