Bi-directional Long Short-Term Memory Network for Fake News Detection from Social Media
2nd International Conference on Intelligent and Cloud Computing, ICICC 2021
; 286:463-470, 2022.
Article
in English
| Scopus | ID: covidwho-1826299
ABSTRACT
These days’ web-based media is one of the main news hotspots for individuals throughout the planet for its minimal expense, simple openness, and quick spreading. This web-based media can in some cases include uncertain messages and has a critical danger of openness to counterfeit or fake news, which may elude the pursuers. Therefore, finding fake news from social media is one of the important natural language processing tasks. In this work, we have proposed a bi-directional long short-term memory (Bi-LSTM) network to identify COVID-19 fake news posted on Twitter. The performance of the proposed Bi-LSTM network is compared to six different popular classical machine learning classifiers such as Naïve Bayes, KNN, Decision Tree, Gradient Boosting, Random Forest, and AdaBoost. In the case of classical machine learning classifiers uni-gram, bi-gram, and tri-gram word TF-IDF features are used whereas in the case of the Bi-LSTM model word embedding features are used. The proposed Bi-LSTM network performed best in comparison to other implemented models and achieved a weighted F1-score of 0.94 in identifying COVID-19 fake news from Twitter. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Bi-LSTM; COVID-19; Fake news; Rumors; Social media; Adaptive boosting; Brain; Decision trees; Fake detection; Natural language processing systems; Social networking (online); Bi-directional; Bi-directional long short-term memory; Hotspots; Memory network; Rumor; Simple++; Web based; Long short-term memory
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2nd International Conference on Intelligent and Cloud Computing, ICICC 2021
Year:
2022
Document Type:
Article
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