In this investigation, an attempt was made to present a model for distinguishing fake news from truthful news in textual data. For this purpose, using intelligent methods and based on the principles of text analysis, a bi-classification model was presented that divided the textual data into deceptive and truthful classes. Basic algorithms based on artificial intelligence (AI) used for modeling consisting of Adaboost (Ada), Support Vector Classifier (SVC), Random Forest (RF), Neural Network (NN), BERT, and Convolutional Neural Network (ConvNet). Among the methods used in this study are the TF-IDF method for vectorization of textual data; the PCA (Principal Component Analysis) technique for feature transformation; the word2index as well as word embedding models for converting the words into numbers, and the N-gram technique to create a sequence of words. Finally, by conducting a case study and by examining different evaluation indices, the comparison of the offending models was done. The outcomes of this investigation showed that despite the high similarity between the two classes (86.6% similarity in train data and 79.8% similarity in test data), the BERT model had the best result compared to the others. This model has high complexity and can better extract relationships between data. In the basic article, the best value of the Accuracy index was 0.90, which was improved to 0.93 in this study.
JiangTAOLiJPHaqAU, et al.A novel stacking approach for accurate detection of fake news. IEEE Access2021; 9: 22626–22639.
2.
AhmedAAAAljabouhADonepudiPK,et al.Detecting fake news using machine learning: A systematic literature review. ArXiv Preprint ArXiv:2102.04458. 2021.
3.
Posadas-DuránJ-PGómez-AdornoHSidorovG,et al.Detection of fake news in a new corpus for the Spanish language. J Intell Fuzzy Syst2019; 36: 4869–4876.
4.
MeelPVishwakarmaDK. A temporal ensembling based semi-supervised ConvNet for the detection of fake news articles. Expert Syst Appl2021; 177: 115002.
5.
ChoudharyMChouhanSSPilliES,et al.Berconvonet: a deep learning framework for fake news classification. Appl Soft Comput2021; 110: 107614.
6.
AhmedHTraoreISaadS. Detection of online fake news using n-gram analysis and machine learning techniques. In: Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments: First International Conference, ISDDC 2017, Vancouver, BC, Canada, October 26–28, 2017, Proceedings 1, Springer, 2017, pp.127–138.
7.
GirgisSAmerEGadallahM. Deep learning algorithms for detecting fake news in online text. In: 2018 13th International Conference on Computer Engineering and Systems (ICCES), IEEE, 2018, pp.93–97.
8.
Abdullah AllTMahirEMAkhterS,et al.Detecting Fake News using Machine Learning and Deep Learning Algorithms. In: 2019 7th International Conference on Smart Computing & Communications (ICSCC), 2019.
9.
PoddarKUmadeviKS. Comparison of various machine learning models for accurate detection of fake news. In: 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), IEEE, 2019, pp.1–5.
10.
KaliyarRKGoswamiANarangP. Multiclass fake news detection using ensemble machine learning. In: 2019 IEEE 9th International Conference on Advanced Computing (IACC), IEEE, 2019, pp.103–107.
11.
AbdulrahmanABaykaraM. Fake news detection using machine learning and deep learning algorithms. In: 2020 International Conference on Advanced Science and Engineering (ICOASE), IEEE, 2020, pp.18–23.
12.
HussainMGHasanMRRahmanM, et al.Detection of bangla fake news using mnb and svm classifier. In: 2020 International Conference on Computing, Electronics & Communications Engineering (ICCECE), IEEE, 2020, pp.81–85.
13.
AlameriSAMohdM. Comparison of fake news detection using machine learning and deep learning techniques. In: 2021 3rd International Cyber Resilience Conference (CRC), IEEE, 2021, pp.1–6.
14.
MeesadP. Thai Fake news detection based on information retrieval, natural language processing and machine learning. SN Comput Sci2021; 2: 425.
15.
GuoAYangT. Research and improvement of feature words weight based on TFIDF algorithm. In: 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, IEEE, 2016, pp.415–419.
16.
AhmedHTraoreISaadS. Detecting opinion spams and fake news using text classification. Secur Privacy2018; 1: e9.
17.
ZhuEZhangJYanJ, et al.N-gram MalGAN: evading machine learning detection via feature n-gram. Digital Commun Networks2022; 8: 485–491.
18.
QiWYanYGongY, et al.Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training. ArXiv Preprint ArXiv:2001.04063. 2020.
19.
RingnérM. What is principal component analysis?Nat Biotechnol2008; 26: 303–304.
20.
KuritaT. Principal component analysis (PCA). Comput Vision: Ref Guide2019: 1–4.
ShlensJ. A tutorial on principal component analysis. ArXiv Preprint ArXiv:1404.1100. 2014.
23.
WangSZhouWJiangC. A survey of word embeddings based on deep learning. Computing2020; 102: 717–740.
24.
AllenCHospedalesT. Analogies explained: towards understanding word embeddings. In: International Conference on Machine Learning, PMLR, 2019, pp.223–231.
25.
TangiralaS. Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm. Int J Adv Comput Sci Appl2020; 11: 612–619.
26.
LuoX. Efficient English text classification using selected machine learning techniques. Alexandria Eng J2021; 60: 3401–3409.
27.
ShajihanN. Classification of stages of Diabetic Retinopathy using Deep Learning. Bournemouth University United Kingdom, 2020.
28.
PowersDMW. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. ArXiv Preprint ArXiv:2010.16061. 2020.
BreimanL. Random forests. Mach Learn2001; 45: 5–32.
31.
HosseinNNadiriAAsghari MoghaddamA, et al.Prediction of transmissivity of malikan plain aquifer using random forest method. Water Soil Sci2017; 27: 61–75.
32.
AkarÖGüngörO. Rastgele orman algoritması kullanılarak çok bantlı görüntülerin sınıflandırılması. Jeodezi ve Jeoinformasyon Dergisi2012; 106: 139–146.
33.
SaricaACerasaAQuattroneA. Random forest algorithm for the classification of neuroimaging data in Alzheimer’s disease: a systematic review. Front Aging Neurosci2017; 9: 329.
34.
DogruNSubasiA. Traffic accident detection using random forest classifier. In: 2018 15th Learning and Technology Conference (L&T), IEEE, 2018, pp.40–45.
35.
TariqIMengQYaoS, et al.Adaboost-DSNN: an adaptive boosting algorithm based on deep self normalized neural network for pulsar identification. Mon Not R Astron Soc2022; 511: 683–690.
36.
MaziniMShiraziBMahdaviI. Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms. J King Saud UnivComput Inf Sci2019; 31: 541–553.
37.
AkandeKOOwolabiTOOlatunjiSO,et al.A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir. J Pet Sci Eng2017; 150: 43–53.
38.
FischerALangensiepenGLuigK, et al.Efficient optimization of hyper-parameters for least squares support vector regression. Optim Methods Software2015; 30: 1095–1108.
39.
De La HozEOrtizAOrtegaJ,et al.Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques. In: Hybrid Artificial Intelligent Systems: 8th International Conference, HAIS 2013, Salamanca, Spain, September 11–13, 2013. Proceedings 8, Springer, 2013, pp.103–111.
40.
NagabushanamPGeorgeSTRadhaS. EEG Signal classification using LSTM and improved neural network algorithms. Soft Comput2020; 24: 9981–10003.
41.
AlameerZAbd ElazizMEweesAA, et al.Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm. Resour Policy2019; 61: 250–260.
42.
AnsariMSMrazekVCockburnBF, et al.Improving the accuracy and hardware efficiency of neural networks using approximate multipliers. IEEE Trans Very Large Scale Integr (VLSI) Syst2019; 28: 317–328.
43.
Al-SaffarAAMTaoHTalabMA. Review of deep convolution neural network in image classification. In: 2017 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), IEEE, 2017, pp.26–31.
44.
LiZLiuFYangW, et al.A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Networks Learn Syst2021; 33: 6999–7019.
45.
KimPKimP. Convolutional neural network. In: MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence, 2017, pp.121–147.
46.
DevlinJChangM-WLeeK,et al.BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. 2018.
47.
KoroteevMV. BERT: A Review of Applications in Natural Language Processing and Understanding. 2021.
48.
OttMChoiYCardieC,et al.Finding deceptive opinion spam by any stretch of the imagination. ArXiv Preprint ArXiv:1107.4557. 2011.
