Top

Arabian Journal for Science and Engineering

Published in:

01-10-2021 | Research Article-Computer Engineering and Computer Science

Detection of Turkish Fake News in Twitter with Machine Learning Algorithms

Authors: Suleyman Gokhan Taskin, Ecir Ugur Kucuksille, Kamil Topal

Published in: Arabian Journal for Science and Engineering | Issue 2/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Social media has affected people’s information sources. Since most of the news on social media is not verified by a central authority, it may contain fake news for various reasons such as advertising and propaganda. Considering an average of 500 million tweets were posted daily on Twitter alone in the year of 2020, it is possible to control each share only with smart systems. In this study, we use Natural Language Processing methods to detect fake news for Turkish-language posts on certain topics on Twitter. Furthermore, we examine the follow/follower relations of the users who shared fake-real news on the same subjects through social network analysis methods and visualization tools. Various supervised and unsupervised learning algorithms have been tested with different parameters. The most successful F1 score of fake news detection was obtained with the support vector machines algorithm with 0.9. People who share fake/true news can help in the separation of subgroups in the social network created by people and their followers. The results show that fake news propagation networks may show different characteristics in their own subject based on the follow/follower network.

previous article A Two-stage Method of Synchronization Prediction Framework in TDD

next article COLCONF: Collaborative ConvNet Features-based Robust Visual Place Recognition for Varying Environments

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Del Vicario, M.; Bessi, A.; Zollo, F.; Petroni, F.; Scala, A.; Caldarelli, G.; Stanley, H.E.; Quattrociocchi, W.: The spreading of misinformation online. Proceed. Natl. Acade. Sci. 113(3), 554–559 (2016)CrossRef

Simon Kemp. Digital 2021: Global Overview Report, 2021.

Teyit.org. teyit.org, 2016.

Dogrulukpayi.com. Dogrulukpayi.com, 2014.

Newman, N.; Fletcher, R.; Kalogeropoulos, A.; Nielsen, R.: Digital News Report 2018. Technical report, Reuters Institute for the Study of Journalism (2018)

Newman, R.; Fletcher, N.; Kalogeropoulos, R.; Nielsen A.: Digital News Report 2019. Technical report, Reuters Institute for the Study of Journalism (2019)

Twitter. Twitter Inc., 2006.

Zhao, W. X.; Jiang, J.; Weng, J.; He, J.; Lim, Ee, P.; Yan, H. and Li, X.: Comparing Twitter and Traditional Media Using Topic Models. In ECIR 2011: Advances in Information Retrieval, pp. 338–349 (2011)

Pratiwi, I. Y. R.; Asmara, R. A.; Rahutomo, F.: Study of hoax news detection using naïve bayes classifier in Indonesian language. In 2017 11th International Conference on Information & Communication Technology and System (ICTS), pp 73–78. IEEE(2017)

10.

Chen, Y.-R.; Chen, H.-H.: Opinion Spam Detection in Web Forum: A Real Case Study. In Proceedings of the 24th International Conference on World Wide Web - WWW ’15, pages 173–183, New York, New York, USA, 2015. ACM Press.

11.

Mertoğlu, U.; Sever, H.; Genc, B.: Savunmada Yenilikci Bir Dijital Donusum Alani. In: Savtek 2018, 9, pp. 771–778. Savunma Teknolojileri Kongresi. METU, Ankara (2018)

12.

Zhao, B.; Rubinstein, B.I.; Gemmell, J.; Han, J.: A Bayesian approach to discovering truth from conflicting sources for data integration. Proceed. VLDB Endowment 5(6), 550–561 (2012)CrossRef

13.

Li, Q.; Li, Y.; Gao, J.; Zhao, B.; Fan, W.; Han, J..: Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation. In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pages 1187–1198, New York, NY, USA, 2014. ACM.

14.

Yang, S.; Shu, K.; Wang, S.; Renjie, G.; Fan, W.; Liu, H.: Unsupervised Fake News Detection on Social Media: A Generative Approach. Proceed. AAAI Conf. Artif. Intell. 33, 5644–5651 (2019)

15.

Anderson, J.G.: Evaluation in health informatics: social network analysis. Comput. Biol. Med. 32(3), 179–193 (2002)CrossRef

16.

Otte, E.; Rousseau, R.: Social network analysis: a powerful strategy, also for the information sciences. J. Inf. Sci. 28(6), 441–453 (2002)CrossRef

17.

Borgatti, S.P.; Mehra, A.; Brass, D.J.; Labianca, G.: Network analysis in the social sciences. Science 323(5916), 892–895 (2009)CrossRef

18.

Brin, S.; Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. 30, 107–117 (1998)

19.

Haveliwala, T.H.: Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search. IEEE Trans. Knowl. Data Eng. 15(4), 784–796 (2003)CrossRef

20.

Xing, W.; Ghorbani, A.: Weighted PageRank algorithm. In Proceedings. Second Annual Conference on Communication Networks and Services Research, 2004. pages 305–314. IEEE, 2004.

21.

Cailan, Z.; Kai, C.; Shasha, L.: Improved PageRank algorithm based on feedback of user clicks. In 2011 International Conference on Computer Science and Service System (CSSS), pages 3949–3952. IEEE, 2011.

22.

Kwak, H.; Lee, C.; Park, H.; Moon, S.: What is Twitter, a social network or a news media? In Proceedings of the 19th international conference on World wide web - WWW ’10, page 591, New York, New York, USA, 2010. ACM Press.

23.

Weng, J.; Lim, E.-P.; Jiang, J.; He, Q.: Twitterrank: Finding Topic-Sensitive Influential Twitterers. In Proceedings of the third ACM international conference on Web search and data mining - WSDM ’10, page 261, New York, New York, USA, 2010. ACM Press.

24.

Gupta, P.; Goel, A.; Lin, J.; Sharma, A.; Wang, D.; Zadeh, R.: WTF: the who to follow service at Twitter. In Proceedings of the 22nd international conference on World Wide Web - WWW ’13, pages 505–514, New York, New York, USA, 2013. ACM Press.

25.

Ngaffo, A. N.; El Ayeb, W.; Choukair, Z.: Mining User Opinion Influences on Twitter Social Network: Find that Friend who Leads your Opinion Using Bayesian Method and a New Emotional PageRank Algorithm. In 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pages 680–685. IEEE, 2019.

26.

Moreno, F.; González, A.; Valencia, A.: NewFriends: an algorithm for computing the minimum number of friends required by a user to get the highest PageRank in a social network. Int. J. Comput. Math. 91(2), 278–290 (2014)MathSciNetCrossRef

27.

Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM (JACM) 46(5), 604–632 (1999)MathSciNetCrossRef

28.

Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on web documents. In Proceedings of the eleventh international conference on World Wide Web - WWW ’02, page 527, New York, New York, USA, 2002. ACM Press.

29.

Yang, M.-C.; Lee, J.-T.; Lee, S.-W.; Rim, H.-C.: Finding Interesting Posts in Twitter Based on Retweet Graph Analysis. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval - SIGIR ’12, page 1073, New York, New York, USA, 2012. ACM Press.

30.

Abilhoa, W.D.; de Castro, L.N.: A keyword extraction method from twitter messages represented as graphs. Appl. Math. Comput. 240, 308–325 (2014)

31.

Yang, C.; Harkreader, R.; Zhang, J.; Shin, S.; Gu, G.: Analyzing spammers’ social networks for fun and profit. In Proceedings of the 21st international conference on World Wide Web - WWW ’12, pages 71–80, New York, New York, USA, 2012. ACM Press.

32.

Yang, M.-C.; Rim, H.-C.: Identifying interesting Twitter contents using topical analysis. Expert Syst. Appl. 41(9), 4330–4336 (2014)CrossRef

33.

Mocanu, D.; Rossi, L.; Zhang, Q.; Karsai, M.; Quattrociocchi, W.: Collective attention in the age of (mis)information. Comput. Hum. Behav. 51, 1198–1204 (2015)CrossRef

34.

Kwon, S.; Cha, M.; Jung, K.; Chen, W.; Wang, Y.: Prominent Features of Rumor Propagation in Online Social Media. In 2013 IEEE 13th International Conference on Data Mining, pages 1103–1108. IEEE, 2013.

35.

Nguyen, N. P.; Yan, G.; Thai, M. T.; Eidenbenz, S.: Containment of Misinformation Spread in Online Social Networks. In Proceedings of the 3rd Annual ACM Web Science Conference on - WebSci ’12, pages 213–222, New York, New York, USA, 2012. ACM Press.

36.

Bird, S.; Klein, E.; Loper, E.: Natural language processing with Python: analyzing text with the natural language toolkit. O’Reilly Media, Inc., 2009.

37.

Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Müller, A.; Nothman, J.; Louppe, G.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Vanderplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Perrot, M.; Duchesnay, É.: Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2012)MathSciNetMATH

38.

Language Technology Group at the University of Oslo. NLPL word embeddings repository, 2018.

39.

Yang, Z.; Yang, D.; Dyer, C.; He, X.; Smola, A.; Hovy, E.: Hierarchical Attention Networks for Document Classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1480–1489, Stroudsburg, PA, USA, 2016. Association for Computational Linguistics.

40.

Sahin, G.: Turkish document classification based on Word2Vec and SVM classifier. In 2017 25th Signal Processing and Communications Applications Conference (SIU), pages 1–4. IEEE (2017)

41.

Elsaadawy, A.; Torki, M.; Ei-Makky, N.: A Text Classifier Using Weighted Average Word Embedding. In 2018 International Japan-Africa Conference on Electronics, Communications and Computations (JAC-ECC), pages 151–154. IEEE, 2018.

42.

Bilgin, M.: Kelime Vektörü Yöntemlerinin Model Oluşturma Sürelerinin Karşılaştırılması. Bilişim Teknolojileri Dergisi, pages 141–146, 2019.

43.

Karcioglu, A. A.; Aydin, T.: Sentiment Analysis of Turkish and English Twitter Feeds Using Word2Vec Model. In 2019 27th Signal Processing and Communications Applications Conference (SIU), pages 1–4. IEEE (2019)

44.

Lilleberg, J.; Zhu, Y.; Zhang, Y.: Support vector machines and Word2vec for text classification with semantic features. In 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), pages 136–140. IEEE, 2015.

45.

Goodfellow, I.; Bengio, Y.; Courville, A.: Deep learning. The MIT Press, Cambridge, MA (2017)MATH

46.

Abualigah, L.M.; Khader, A.T.; Hanandeh, E.S.: A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis. Eng. Appl. Artif. Intell. 73, 111–125 (2018)CrossRef

47.

Abualigah, L.M.; Khader, A.T.; Hanandeh, E.S.: A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J. Comput. Sci. 25, 456–466 (2018)CrossRef

48.

Lee, D.D.; Sebastian Seung, H.: Learning the parts of objects by non-negative matrix factorization. Nature 410(6755), 788–791 (1999)CrossRef

49.

Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7(2), 179–188 (1936)CrossRef

50.

Alpaydin, E.: Machine Learning: The New AI. The MIT Press, Cambridge, MA (2016)

51.

Faizollahzadeh Ardabili, S.; Najafi, B.; Shamshirband, S.; Minaei Bidgoli, B.; Deo, R.C.; Chau, K.W.: Computational intelligence approach for modeling hydrogen production: a review. Eng. Appl. Comput. Fluid Mech. 12(1), 438–458 (2018)

52.

Taylor, V. F.; Spolaor, R.; Conti, M.; Martinovic, I.: AppScanner: Automatic Fingerprinting of Smartphone Apps from Encrypted Network Traffic. In 2016 IEEE European Symposium on Security and Privacy (EuroSP), pages 439–454. IEEE (2016)

53.

Vapnik, V.: The Nature of Statistical Learning Theory. Springer (1995)

54.

Basakin, E.E.; Ekmekcioglu, O.; Ozger, M.: Drought Analysis with Machine Learning Methods. Pamukkale Univ. J. Eng. Sci. 25(8), 985–991 (2019)CrossRef

55.

Leo B.: Random Forests. In Machine Learning, chapter 45, pages 5–32. Springer, 2001.

56.

Christopher O.: Understanding LSTM Networks, 2015.

57.

Cho, K.; van Merrienboer, B.; Gulcehre, C.; Bahdanau, D.; Bougares, F.; Schwenk, H.; Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1724–1734, Stroudsburg, PA, USA, 2014. Association for Computational Linguistics.

58.

Shamshirband, S.; Rabczuk, T.; Chau, K.-W.: A Survey of Deep Learning Techniques: Application in Wind and Solar Energy Resources. IEEE Access 7, 164650–164666 (2019)CrossRef

59.

Aceto, G.; Ciuonzo, D.; Montieri, A.; Pescape, A.: Mobile encrypted traffic classification using deep learning: Experimental evaluation, lessons learned, and challenges. IEEE Trans. Netw. Serv. Manage. 16(2), 445–458 (2019)CrossRef

60.

Fan, Y.; Kangkang, X.; Hui, W.; Zheng, Y.; Tao, B.: Spatiotemporal modeling for nonlinear distributed thermal processes based on KL decomposition MLP and LSTM network. IEEE Access 8, 25111–25121 (2020)CrossRef

61.

Schuster, M.; Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)CrossRef

62.

TensorFlow. The Sequential model- Keras, 2019.

63.

Montieri, A.; Ciuonzo, D.; Bovenzi, G.; Persico, V.; Pescape, A.: A dive into the dark web: Hierarchical traffic classification of anonymity tools. IEEE Trans. Netw. Sci. Eng. 7(3), 1043–1054 (2020)CrossRef

64.

Banan, A.; Nasiri, A.; Taheri-Garavand, A.: Deep learning-based appearance features extraction for automated carp species identification. Aquacult. Eng. 89, 102053 (2020)CrossRef

65.

Wu, C.L.; Chau, K.W.: Prediction of rainfall time series using modular soft computingmethods. Eng. Appl. Artif. Intell. 26(3), 997–1007 (2013)CrossRef

66.

Taormina, R.; Chau, K.-W.: ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS. Eng. Appl. Artif. Intell. 45, 429–440 (2015)CrossRef

67.

Ali, S. and Karacan, H.: Akan Veri Kümeleme Teknikleri Üzerine Bir Derleme. European Journal of Science and Technology, pages 17–30, 2018.

68.

Galan-Garcia, P.; Puerta, J.G.D.L.; Gomez, C.L.; Santos, I.; Bringas, P.G.: Supervised machine learning for the detection of troll profiles in twitter social network: Application to a real case of cyberbullying. Logic J. IGPL 24(1), 42–53 (2016)MathSciNet

69.

Kadry, S. and Al-Taie, M. Z.: Başlık: Social Network Analysis : An Introduction with an Extensive Implementation to a Large-scale Online Network Using Pajek. eBook Collection (EBSCOhost), 2014.

70.

Gephi. Gephi-open source graph visualization software, 2020.

71.

Peters, M.; Neumann, M.; Iyyer, M.; Gardner, M.; Clark, C.; Lee, K.; Zettlemoyer, L.: Deep Contextualized Word Representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 2227–2237, Stroudsburg, PA, USA, 2018. Association for Computational Linguistics.

72.

Devlin, J.; Chang, M.-W.; Lee, K.; Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North, pages 4171–4186, Stroudsburg, PA, USA, 2019. Association for Computational Linguistics.

73.

Brown, T. B.; Benjamin, M.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Ariel H.-V.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D. M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I. and Amodei, D.: Language Models are Few-Shot Learners, 2020.

74.

Hadeer, A.: Detecting Opinion Spam and Fake News Using N-gram Analysis and Semantic Similarity. Msc. thesis, University of Ahram Canadian, 2017.

75.

Granik, M.; Mesyura, V.: Fake news detection using naive Bayes classifier. In 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), pages 900–903. IEEE, (2017)

76.

Eugenio, T.; Gabriele, B.; Della Vedova, M. L.; Moret, S. and de Alfaro, L.: Some Like it Hoax: Automated Fake News Detection in Social Networks. In Proceedings of the Second Workshop on Data Science for Social Good, Skopje, Macedonia, 2017.

77.

Rubin, V.; Conroy, N.; Chen, Y.; Cornwell, S.: Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News. In Proceedings of the Second Workshop on Computational Approaches to Deception Detection, pages 7–17, Stroudsburg, PA, USA, 2016. Association for Computational Linguistics.

78.

Pérez-Rosas, V.; Kleinberg, B.; Lefevre, A.; Mihalcea, R.: Automatic Detection of Fake News. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3391–3401 (2017)

79.

Samir, B.: “The Pope Has a New Baby!” Fake News Detection Using Deep Learning, 2017.

80.

Miraj, P.: Detection of Maliciously Authored News Articles. Msc. thesis, The Cooper Union For The Advancement of Science and Art, 2017.

81.

Ågren, A. and Ågren, C.: Combating Fake News with Stance Detection using Recurrent Neural Networks. Msc. thesis, University of Gothenburg, 2018.

82.

Rajendran, G.; Chitturi, B.; Poornachandran, P.: Stance-In-Depth Deep Neural Approach to Stance Classification. Procedia Comput. Sci. 132, 1646–1653 (2018)CrossRef

83.

Bhatt, G.; Sharma, A.; Sharma, S.; Nagpal, A.; Raman, B.; Mittal, A.: On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification (2017)

84.

Ruchansky, N.; Seo, S.; Liu, Y.: CSI: A Hybrid Deep Model for Fake News Detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 797–806, New York, NY, USA, 2017. ACM.

85.

Singhania, S.; Fernandez, N.; Rao, S.: 3HAN: A Deep Neural Network for Fake News Detection. In 24th International Conference on Neural Information Processing (ICONIP 2017), pages 572–581, 2017.

86.

Volkova, S.; Shaffer, K.; Jang, J. Y.; Hodas, N.: Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 647–653, Stroudsburg, PA, USA, 2017. Association for Computational Linguistics.

87.

Wang, W. Y.: “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 422–426, Stroudsburg, PA, USA, 2017. Association for Computational Linguistics.

88.

Girgis, S.; Amer, E.; Gadallah, M.: Deep Learning Algorithms for Detecting Fake News in Online Text. In 2018 13th International Conference on Computer Engineering and Systems (ICCES), pages 93–97. IEE (2018)

89.

Fang, Y.; Gao, J.; Huang, C.; Peng, H.; Runpu, W.: Self Multi-Head Attention-based Convolutional Neural Networks for fake news detection. PLoS ONE 14(9), e0222713 (2019)CrossRef

Title: Detection of Turkish Fake News in Twitter with Machine Learning Algorithms
Authors: Suleyman Gokhan Taskin
Ecir Ugur Kucuksille
Kamil Topal
Publication date: 01-10-2021
Publisher: Springer Berlin Heidelberg
Published in: Arabian Journal for Science and Engineering / Issue 2/2022
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-021-06223-0

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 2/2022

Deep Transfer Learning Based Unified Framework for COVID19 Classification and Infection Detection from Chest X-Ray Images

Comparative Analysis of AlexNet, ResNet18 and SqueezeNet with Diverse Modification and Arduous Implementation

A Two-stage Method of Synchronization Prediction Framework in TDD

Multi-focus Image Fusion Using Hybrid De-focused Region Segmentation Approach

Fuzzy Logic-Based SBR Acceleration Approach for Radio Propagation Prediction in Indoor Environments

Multi Objective Task Scheduling in Cloud Computing Using Cat Swarm Optimization Algorithm

Premium Partners