Abstract
With the rapid growth of Online Social Networks (OSNs), information can spread rapidly and widely more than ever. Besides, rumors could also be easily posted and propagated in OSNs which can lead to serious social issues. The problem of identifying rumors on social networks has received considerable attention in recent years. In this research, we propose a novel automatic rumor detection method based on a Long Short-Term Memory (LSTM) classified. Our proposed method not only achieves higher accuracy, F1 score and precision but also has lower false positive rate value. Extensive experiments conducted on Twitter show that the accuracy of the proposed method is 92.45% and F1 score is 89.95%. Meanwhile, false positive rate is less than 5.01%.
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Torshizi, A.S., Ghazikhani, A. (2019). Automatic Twitter Rumor Detection Based on LSTM Classifier. In: Grandinetti, L., Mirtaheri, S., Shahbazian, R. (eds) High-Performance Computing and Big Data Analysis. TopHPC 2019. Communications in Computer and Information Science, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-33495-6_22
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DOI: https://doi.org/10.1007/978-3-030-33495-6_22
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