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Title: Identifying Informative Coronavirus Tweets using Recurrent Neural Network Document Embedding
Other Titles: تصنيف الأخبار المسهبة حول وباء الكورونا المستجد عبر تويتر باستخدام تضمين الملفات النصية بواسطة خوارزمية الشبكات العصبية المتكررة
Authors: Yousuf, Rami
Keywords: Coronavirus;Informative Tweets;Document Embedding;Recurrent Neural Network;WNUT-2020
Issue Date: 30-Mar-2022
Publisher: Palestine Technical University -Kadoorie
Citation: Yousef, R. (2022). Identifying Informative Coronavirus Tweets using Recurrent Neural Network Document Embedding. Palestine Technical University Research Journal, 10(1), 93–102.
Series/Report no.: 10(1);93–102
Abstract: The coronavirus pandemic has led to the spread of tremendous fake news and misleading information through tweets. Hence, an interesting task of classifying tweets into informative and uninformative has motivated researchers to employ machine learning techniques. The state-of-the-art studies showed high dependency on transformers architecture. However, the transformers architecture suffers from the catastrophic forgetting problem where important contextual information is being forgotten by the gradients. Therefore, this paper proposes a document embedding using Recurrent Neural Network. Lastly, three classifiers of LR, SVM and MLP have been used to classify documents into Informative and Uninformative. Using the benchmark dataset of WNUT-2020 at Task 2, LR classifier obtained the highest f-measure of 0.91. This result demonstrates the efficacyof RNN to generate sophisticated document embedding
metadata.dc.identifier.doi: 10.53671
Appears in Collections:2022

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