Abstract
Misleading and fake news in rapidly increasing online news portals in Bangladesh has become a major concern to both the government and public lately, as a substantial amount of incidents have taken place in different cities due to unwarranted rumors over the last couple of years. However, the overall progress of research and innovation in detecting fake and satire Bangla news is yet unsatisfactory considering the prospects it would bring to the decision-makers of Bangladesh. In this study, we have amalgamated both fake and real Bangla news from quite a pool of online news portals and applied a total of seven prominent machine learning algorithms to identify real and fake Bangla news, proposing a Deep Neural Network (DNN) architecture. Using a total of five evaluation metrics: Accuracy, Precision, Recall, F1 score, and AUC, we have discovered that DNN model yields the best result with an accuracy and AUC score of 0.90 respectively while Decision Tree performs the worst.
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References
Chy, A.N., Seddiqui, M.H., Das, S.: Bangla news classification using Naive Bayes classifier. In: 16th International Conference Computer and Information Technology. IEEE (2014)
Nath Nandi, R., Arefin Zaman, M.M., Al Muntasir, T., Hosain Sumit, S., Sourov, T., Jamil-Ur Rahman, Md.: Bangla news recommendation using doc2vec. In: 2018 International Conference on Bangla Speech and Language Processing (ICBSLP). IEEE (2018)
Dhar, A., Dash, N., Roy, K.: Classification of text documents through distance measurement: an experiment with multi-domain Bangla text documents. In: 2017 3rd International Conference on Advances in Computing, Communication & Automation (ICACCA) (Fall). IEEE (2017)
Islam, Z., Rahman, R.: Readability of Bangla news articles for children. In: Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing, pp. 309–317 (2014)
Mouhoub, M., Al Helal, M.: Topic modelling in Bangla language: an LDA approach to optimize topics and news classification. CIS 11, 77 (2018)
Paul, A., et al.: Bangla news summarization. In: Nguyen, N.T., Papadopoulos, G.A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017. LNCS (LNAI), vol. 10449, pp. 479–488. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67077-5_46
Haque, Md.M., Pervin, S., Begum, Z.: Automatic Bengali news documents summarization by introducing sentence frequency and clustering. In: 2015 18th International Conference on Computer and Information Technology (ICCIT). IEEE (2015)
Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-37882-2
Mahmud, Md.R., Afrin, M., Razzaque, Md.A., Miller, E., Iwashige, J.: A rule based Bengali stemmer. In: 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE (2014)
Parves, A.B., Al Imran, A., Rahman, Md.R.: Incorporating supervised learning algorithms with NLP techniques to classify Bengali language forms. In: Proceedings of the International Conference on Computing Advancements. ACM (2020). https://doi.org/10.1145/3377049.3377110
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Al Imran, A., Wahid, Z., Ahmed, T. (2020). BNnet: A Deep Neural Network for the Identification of Satire and Fake Bangla News. In: Chellappan, S., Choo, KK.R., Phan, N. (eds) Computational Data and Social Networks. CSoNet 2020. Lecture Notes in Computer Science(), vol 12575. Springer, Cham. https://doi.org/10.1007/978-3-030-66046-8_38
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DOI: https://doi.org/10.1007/978-3-030-66046-8_38
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