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dc.contributor.authorYousuf, Rami-
dc.identifier.citationYousef, R. (2022). Identifying Informative Coronavirus Tweets using Recurrent Neural Network Document Embedding. Palestine Technical University Research Journal, 10(1), 93–102.
dc.description.abstractThe 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 embeddingen_US
dc.publisherPalestine Technical University -Kadoorieen_US
dc.subjectInformative Tweetsen_US
dc.subjectDocument Embeddingen_US
dc.subjectRecurrent Neural Networken_US
dc.titleIdentifying Informative Coronavirus Tweets using Recurrent Neural Network Document Embeddingen_US
dc.title.alternativeتصنيف الأخبار المسهبة حول وباء الكورونا المستجد عبر تويتر باستخدام تضمين الملفات النصية بواسطة خوارزمية الشبكات العصبية المتكررةen_US
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