Abstract
Knowledge dissemination had never before been hampered in the history of humanity until the World Wide Web's development and the rapid adoption of social media outlets. As a result of the growing usage of social media platforms, fake news is increasingly common in all kinds of circumstances. After the internet evolved, most of the people are utilizing Internet for their personal purpose only at the same time they are uncontrolled to read many of fake news, also. Automated classification of a text article as real or fake is a challenging task. In this situation, to detect such types of fake news and to provide well verified news to our society, the machine learning (ML) techniques such as support vector machine, linear regression, K-nearest neighbor, neuro-evolution of augmenting topologies (NEAT) and boosting NEAT are applied in this research. After preprocesses over the actual dataset methods effectively identify the fake news with collected dataset and evaluated by the metrics such as accuracy, precision, recall and F1-score.
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References
Liu H, Wang L, Han X, Zhang W, He X (2020) Detecting fake news on social media: a multi-source scoring framework. In: 2020 IEEE 5th international conference on cloud computing and big data analytics (ICCCBDA), pp 524–531
Granik M, Mesyura V (2017) Fake news detection using naive bayes classifier. In: 2017 IEEE first ukraine conference on electrical and computer engineering (UKRCON). IEEE, pp 900–903
Ahmed H, Traore I, Saad S (2017) Detection of online fake news using n-gram analysis and machine learning techniques. In: International conference on intelligent, secure, and dependable systems in distributed and cloud environments. Springer, pp 127–138
Sauvageau F (2018) Les faussesnouvelles, nouveaux visages, nouveaux défis. Comment déterminer la valeur de l’informationdans les sociétésdémocratiques? Presses de l’Université Laval
Vivek Singh DSKR, Dasgupta R, Automated fake news detection using linguistic analysis and machine learning
Stančin, Jović A (2019) An overview and comparison of free Python libraries for data mining and big data analysis. In: 2019 42nd international convention on information and communication technology, electronics and microelectronics (MIPRO), pp 977–982
Lang S, Reggelin T, Schmidt J, Müller M, Nahhas A (2021) NeuroEvolution of augmenting topologies for solving a two-stage hybrid flow shop scheduling problem: a comparison of different solution strategies. Expert Syst Appl 172:114666, ISSN 0957-4174. Elsevier
Ibrahim MY, Sridhar R, Geetha TV, Deepika SS (2019) Advances in neuroevolution through augmenting topologies—a case study. In: 2019 11th international conference on advanced computing (ICoAC), pp 111–116
Wang K, Liu X, Zhao J, Gao H, Zhang Z (2020) Application research of ensemble learning frameworks. Chin Autom Congr (CAC) 2020:5767–5772
Yang F-J (2018) An implementation of naive bayes classifier. Int Conf Comput Sci Computl Intell (CSCI) 2018:301–306
Wang P, Zhang Y, Jiang W (2021) Application of K-Nearest neighbor (knn) algorithm for human action recognition. In: 2021 IEEE 4th advanced information management, communicates, electronic and automation control conference (IMCEC), pp 492–496
Zou X, Hu Y, Tian Z, Shen K (2019) Logistic regression model optimization and case analysis. In: 2019 IEEE 7th international conference on computer science and network technology (ICCSNT), pp 135–139
Xiao Y, Huang W, Wang J (2020) A random forest classification algorithm based on dichotomy rule fusion. In: 2020 IEEE 10th international conference on electronics information and emergency communication (ICEIEC), pp 182–185
Bhaskaruni D, Hu H, Lan C (2019) Improving prediction fairness via model ensemble. In: 2019 IEEE 31st international conference on tools with artificial intelligence (ICTAI), pp 1810–1814
Radja M, Emanuel AWR (2019) Performance evaluation of supervised machine learning algorithms using different data set sizes for diabetes prediction. In: 2019 5th international conference on science in information technology (ICSITech), pp 252–258
Wang WY (2017) Liar, liar pants on fire: a new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648
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Suhasini, V., Vimala, N. (2024). Prediction of Fake Twitters Using AdaBoost-Based Neuro-Evolution of Augmenting Topologies Algorithm. In: Shetty, N.R., Prasad, N.H., Nalini, N. (eds) Advances in Computing and Information. ERCICA 2023. Lecture Notes in Electrical Engineering, vol 1104. Springer, Singapore. https://doi.org/10.1007/978-981-99-7622-5_2
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DOI: https://doi.org/10.1007/978-981-99-7622-5_2
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