Abstract
The penetration of social and online platforms has opened a new substantial domain of Fake news dissemination in the current time. Also, this dynamic form of data opens up new dimensions for researchers to detect Fake news from the ocean of data. Therefore, Fake news detection has attracted both academia and industry indifferently as research or analytical domain in the concurrent time. Due to data availability, the classification tasks have been tested in different sets and types of data. Detecting Fake news evolves as an actual potential domain to explore with more efficient algorithms and parameter-based modified algorithms. In this work, an analytical sketch has been drawn to compare the performances of different classifiers depending on accuracy and time. Seven classifiers of four different types have been implemented and tested namely, Multilayer Perceptron, Sequential Minimal Optimization, Logistic Regression, Decision Tree, J48, Random Forest and Naïve Bayes Classifier. The analytical evaluation process has been designed with three experimental setups, 10-fold cross-validation, 70% split and 80% split. The separate setups show distinctive outcomes across the algorithms. Naïve-Bayes classifier model shows its prominence along with the Random Forest classifier. However, the and Decision Tree-based classifiers perform differently from earlier knowledge. Furthermore, this paper identifies a different aspect of using testing-training splitting in classifier tasks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tandoc, E.C., Lim, Z.W., Ling, R.: Defining “Fake News”: a typology of scholarly definitions. Dig. J. 6(2), 137–153 (2017). https://doi.org/10.1080/21670811.2017.1360143
Zhou, X., Reza, Z.: A survey of fake news. ACM Comput. Surv. 53(1), 1–40 (2020). https://doi.org/10.1145/3395046
Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor. Newslett. 19(1), 22–36 (2017). https://doi.org/10.1145/3137597.3137600
Lazer, D.M.J., et al.: The science of fake news. Science 359(6380), 1094–1096 (2018). https://doi.org/10.1126/science.aao2998
Zhang, X., Ghorbani, A.A.: An overview of online fake news: characterization, detection, and discussion. Inf. Process. Manag. 57, 102025 (2020). https://doi.org/10.1016/J.IPM.2019.03.004
Gupta, A., Kumaraguru, P.: Credibility ranking of tweets during high impact events. ACM Int. Conf. Proceeding Ser. (2012). https://doi.org/10.1145/2185354.2185356
Mohd Shariff, S., Zhang, X., Sanderson, M.: User perception of information credibility of news on Twitter. In: de Rijke, M., et al. (eds.) ECIR 2014. LNCS, vol. 8416, pp. 513–518. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06028-6_50
Finding true and credible information on Twitter | IEEE Conference Publication | IEEE Xplore. https://ieeexplore.ieee.org/abstract/document/6915989. Accessed 30 Aug 2021
Kareem, I., Awan, S.M.: Pakistani media fake news classification using machine learning classifiers. In: 3rd International Conference Innovative Computing (ICIC 2019). (2019). https://doi.org/10.1109/ICIC48496.2019.8966734
Liu, S., Liu, S., Ren, L.: Trust or Suspect? An Empirical Ensemble Framework for Fake News Classification
Hakak, S., Alazab, M., Khan, S., Gadekallu, T.R., Maddikunta, P.K.R., Khan, W.Z.: An ensemble machine learning approach through effective feature extraction to classify fake news. Futur. Gener. Comput. Syst. 117, 47–58 (2021). https://doi.org/10.1016/J.FUTURE.2020.11.022
Bin Othman, M.F., Yau, T.M.S.: Comparison of different classification techniques using WEKA for breast cancer. In: Ibrahim, F., Osman, N.A.A., Usman, J., Kadri, N.A. (eds.) 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. IP, vol. 15, pp. 520–523. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-68017-8_131
Rubin, V.L., Conroy, N.J., Chen, Y., Cornwell, S.: Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News, pp. 7–17 (2016)
Komarek, P., Moore, A.: Fast Logistic Regression for Data Mining, Text Classification and Link Detection, pp. 1–8 (2003)
Alsari, A.B.A.: Short Text Classification using Machine Learning Techniques (2018)
Al Qadi, L., El Rifai, H., Obaid, S., Elnagar, A.: Arabic text classification of news articles using classical supervised classifiers. In: Proceedings of the 2nd International Conference on New Trends in Computing Sciences (ICTCS 2019) (2019). https://doi.org/10.1109/ICTCS.2019.8923073
Thornton, C., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Part F1288, pp. 847–855 (2013). https://doi.org/10.1145/2487575.2487629
Ahmed, A.A.A., Aljabouh, A., Donepudi, P.K., Choi, M.S.: Detecting fake news using machine learning: a systematic literature review. Psychol. Educ. J. 58, 1932–1939 (2021)
Castillo, C., Mendoza, M., Poblete, B.: Information credibility on Twitter. In: Proceedings of the 20th International Conference on Companion World Wide Web (WWW 2011), pp. 675–684 (2011). https://doi.org/10.1145/1963405.1963500
Buntain, C., Golbeck, J.: Automatically identifying fake news in popular Twitter Threads. In: Proceedings of the 2nd IEEE International Conference on Smart Cloud, SmartCloud 2017, pp. 208–215 (2017). https://doi.org/10.1109/SMARTCLOUD.2017.40
Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018). https://doi.org/10.1126/science.aap9559
Fakenews Dataset (csv and arff) | Kaggle. https://www.kaggle.com/abkafi/fakenews-datasetcsv-and-arff. Accessed 10 Sept 2021
Bisaillon. Clément: Fake and real news dataset | Kaggle. https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset. Accessed 24 Aug 2021
Granik, M., Mesyura, V.: Fake news detection using naive Bayes classifier. In: Proceedings of the IEEE 1st Ukraine Conference on Electrical and Computer Engineering (UKRCON 2017), pp. 900–903 (2017). https://doi.org/10.1109/UKRCON.2017.8100379
Keskar, D., Palwe, S., Gupta, A.: Fake news classification on Twitter using flume, N-gram analysis, and decision tree machine learning technique. In: Bhalla, S., Kwan, P., Bedekar, M., Phalnikar, R., Sirsikar, S. (eds.) Proceeding of International Conference on Computational Science and Applications. AIS, pp. 139–147. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0790-8_15
Ali, S., Smith, K.A.: On learning algorithm selection for classification. Appl. Soft Comput. 6(2), 119–138 (2006). https://doi.org/10.1016/j.asoc.2004.12.002
Goldberg, Y.: Neural network methods for natural language processing. Synth. Lect. Hum. Lang. Technol. 10(1), 1–309 (2017). https://doi.org/10.2200/S00762ED1V01Y201703HLT037
Saritas, M.M., Yasar, A.: Performance analysis of ANN and Naive Bayes classification algorithm for data classification. Int. J. Intell. Syst. Appl. Eng. 7, 88–91 (2019). https://doi.org/10.18201//IJISAE.2019252786
Pregibon, D.: Logistic regression diagnostics. Ann. Statist. 9, 705–724 (1981). https://doi.org/10.1214/AOS/1176345513
Kleinbaum, D.G., Klein, M.: Logistic Regression. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-1742-3
Garner, S.R.: WEKA: the Waikato environment for knowledge analysis. Proc. New Zeal. Comput. Sci. Res. Stud. Conf. 57–64 (1995)
Flake, G.W., Flake, G.W., Lawrence, S.: Efficient SVM regression training with SMO. Mach. Learn. 46, 1–3 (2000)
Lee, T.-H., Ullah, A., Wang, R.: Bootstrap aggregating and random forest. In: Fuleky, P. (ed.) Macroeconomic Forecasting in the Era of Big Data. ASTAE, vol. 52, pp. 389–429. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-31150-6_13
Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21, 660–674 (1991). https://doi.org/10.1109/21.97458
Ivan, N., Ahishakiye, E., Omulo, E.O., Taremwa, D.: Crime prediction using decision tree (J48) classification algorithm. Int. J. Comput. Inf. Technol. (2017)
Ozbay, F.A., Alatas, B.: Fake news detection within online social media using supervised artificial intelligence algorithms. Phys. A Stat. Mech. Appl. 540, 123174 (2020). https://doi.org/10.1016/J.PHYSA.2019.123174
Kaur, G., Chhabra, A.: Improved J48 classification algorithm for the prediction of diabetes. Int. J. Comput. Appl. 98, 975–8887 (2014)
Bouckaert, R.R.: Properties of Bayesian belief network learning algorithms. Uncertain. Proc. 1994, 102–109 (1994). https://doi.org/10.1016/B978-1-55860-332-5.50018-3
Buntine, W.: Theory refinement on Bayesian networks. Uncertain. Proc. 1991, 52–60 (1991). https://doi.org/10.1016/B978-1-55860-203-8.50010-3
Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20, 37–46 (2016). https://doi.org/10.1177/001316446002000104
Ben-David, A.: Comparison of classification accuracy using Cohen’s Weighted Kappa. Expert Syst. Appl. 34, 825–832 (2008). https://doi.org/10.1016/J.ESWA.2006.10.022
Landis, J.R., Koch, G.G.: An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics 33, 363 (1977). https://doi.org/10.2307/2529786
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Abdullah-Al-Kafi, M., Tasnova, I.J., Wadud Islam, M., Banshal, S.K. (2022). Performances of Different Approaches for Fake News Classification: An Analytical Study. In: Woungang, I., Dhurandher, S.K., Pattanaik, K.K., Verma, A., Verma, P. (eds) Advanced Network Technologies and Intelligent Computing. ANTIC 2021. Communications in Computer and Information Science, vol 1534. Springer, Cham. https://doi.org/10.1007/978-3-030-96040-7_53
Download citation
DOI: https://doi.org/10.1007/978-3-030-96040-7_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-96039-1
Online ISBN: 978-3-030-96040-7
eBook Packages: Computer ScienceComputer Science (R0)