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Adaptive Goal Function of Ant Colony Optimization in Fake News Detection

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Computational Collective Intelligence (ICCCI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12876))

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Abstract

Currently, there is a very rapid growth of information published on the Internet, both on social media and news sites. However, a serious problem is a disinformation in the form of fake news. Due to the rapid spread of information on the Internet, it is very important to be able to quickly identify true and fake news. The solution to this problem can be an initial analysis of news by its title and quick selection of true or fake news. Additionally, the possibility of balancing precision and recall as the quality of classification measures could allow for better news selection. In this paper, we propose the use of the adaptive goal function of ant colony optimization algorithms in fake news detection. The goal of this solution is to increase recall or precision of the selected class – in this case fake or true news. We use natural language processing (NLP) to describe the title of the news. In addition, a constrained term matrix is used. The choice of titles alone and the restriction of the words analyzed are related to speeding up the initial classification. Eventually, we present an analysis of a real dataset and classification results (detailing recall and precision) of news using the adaptive goal function of the ACDT algorithm.

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References

  1. https://www.uvic.ca/engineering/ece/isot/datasets/index.php

  2. Ahmed, H., Traore, I., Saad, S.: Detecting opinion spams and fake news using text classification. Secur. Priv. 1(1), e9 (2018)

    Article  Google Scholar 

  3. Amirhosseini, M.H., Kazemian, H.: Automating the process of identifying the preferred representational system in neuro linguistic programming using natural language processing. Cogn. Process. 20(2), 175–193 (2019)

    Article  Google Scholar 

  4. Bedekar, P.P., Bhide, S.R.: Optimum coordination of directional overcurrent relays using the hybrid ga-nlp approach. IEEE Trans. Power Delivery 26(1), 109–119 (2010)

    Article  Google Scholar 

  5. Boryczka, U., Kozak, J.: Ant colony decision trees – a new method for constructing decision trees based on ant colony optimization. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ICCCI 2010. LNCS (LNAI), vol. 6421, pp. 373–382. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16693-8_39

    Chapter  Google Scholar 

  6. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)

    MATH  Google Scholar 

  7. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Chapman & Hall, New York (1984)

    MATH  Google Scholar 

  8. Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)

    Article  Google Scholar 

  9. Chowdhary, K.: Fundamentals of Artificial Intelligence. Springer, New Delhi (2020). https://doi.org/10.1007/978-81-322-3972-7

  10. Colorni, A., Dorigo, M., Maniezzo, V., et al.: Distributed optimization by ant colonies. In: Proceedings of the First European Conference on Artificial life, vol. 142, pp. 134–142. Paris, France (1991)

    Google Scholar 

  11. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995). https://doi.org/10.1007/BF00994018

  12. Gelfert, A.: Fake news: a definition. Informal Logic 38(1), 84–117 (2018)

    Article  MathSciNet  Google Scholar 

  13. Golbeck, J., et al.: Fake news vs satire: a dataset and analysis. In: Proceedings of the 10th ACM Conference on Web Science, pp. 17–21 (2018)

    Google Scholar 

  14. Howard, P.N., Bolsover, G., Kollanyi, B., Bradshaw, S., Neudert, L.M.: Junk news and bots during the us election: What were michigan voters sharing over twitter. CompProp, OII, Data Memo (2017)

    Google Scholar 

  15. Kannan, S., Gurusamy, V., Vijayarani, S., Ilamathi, J., Nithya, M.: Preprocessing techniques for text mining. Int. J. Comput. Sci. Commun. Netw. 5(1), 7–16 (2014)

    Google Scholar 

  16. Kao, A., Poteet, S.R.: Natural Language Processing and Text Mining. Springer, London (2007). https://doi.org/10.1007/978-1-84628-754-1

  17. Kozak, J., Boryczka, U.: Dynamic version of the acdt/acdf algorithm for h-bond data set analysis. In: ICCCI, pp. 701–710 (2013)

    Google Scholar 

  18. Kozak, J.: Decision Tree and Ensemble Learning Based on Ant Colony Optimization. SCI, vol. 781. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-93752-6

    Book  Google Scholar 

  19. Kozak, J., Boryczka, U.: Goal-oriented requirements for acdt algorithms. In: Hwang, D., Jung, J.J., Nguyen, N.-T. (eds.) ICCCI 2014. LNCS (LNAI), vol. 8733, pp. 593–602. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11289-3_60

    Chapter  Google Scholar 

  20. Lazer, D.M., et al.: The science of fake news. Science 359(6380), 1094–1096 (2018)

    Article  Google Scholar 

  21. Luhn, H.P.: A statistical approach to mechanized encoding and searching of literary information. IBM J. Res. Dev. 1(4), 309–317 (1957)

    Article  MathSciNet  Google Scholar 

  22. Martens, D., De Backer, M., Haesen, R., Vanthienen, J., Snoeck, M., Baesens, B.: Classification with ant colony optimization. IEEE Trans. Evol. Comput. 11(5), 651–665 (2007). https://doi.org/10.1109/TEVC.2006.890229

    Article  Google Scholar 

  23. Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: An ant colony algorithm for classification rule discovery. In: Data Mining: a Heuristic Approach, pp. 191–208. Idea Group Publishing, London (2002)

    Google Scholar 

  24. Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: Data mining with an ant colony optimization algorithm. IEEE Trans. Evol. Comput. Spec. Issue Ant Colony Algorithms 6, 321–332 (2004)

    Google Scholar 

  25. Pérez-Rosas, V., Kleinberg, B., Lefevre, A., Mihalcea, R.: Automatic detection of fake news. arXiv preprint arXiv:1708.07104 (2017)

  26. Potthast, M., Köpsel, S., Stein, B., Hagen, M.: Clickbait detection. In: Ferro, N., et al. (eds.) ECIR 2016. LNCS, vol. 9626, pp. 810–817. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30671-1_72

    Chapter  Google Scholar 

  27. Rashkin, H., Choi, E., Jang, J.Y., Volkova, S., Choi, Y.: Truth of varying shades: Analyzing language in fake news and political fact-checking. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2931–2937 (2017)

    Google Scholar 

  28. Rokach, L., Maimon, O.: Data Mining With Decision Trees: Theory And Applications. World Scientific Publishing, Singapore (2008)

    Google Scholar 

  29. Rubin, V.L., Chen, Y., Conroy, N.K.: Deception detection for news: three types of fakes. In: Proceedings of the Association for Information Science and Technology, vol. 52, no. 1, pp. 1–4 (2015)

    Google Scholar 

  30. Rubin, V.L., 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, pp. 7–17 (2016)

    Google Scholar 

  31. Schapire, R.E.: The strength of weak learnability. Mach. Learn. 5, 197–227 (1990)

    Google Scholar 

  32. Sowmiya, C., Sumitra, P.: A hybrid approach for mortality prediction for heart patients using ACO-HKNN. J. Ambient Intell. Humanized Comput. 12(5), 5405–5412 (2020). https://doi.org/10.1007/s12652-020-02027-6

    Article  Google Scholar 

  33. Straková, J., Straka, M., Hajic, J.: Open-source tools for morphology, lemmatization, pos tagging and named entity recognition. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 13–18 (2014)

    Google Scholar 

  34. Wang, K., Thrasher, C., Viegas, E., Li, X., Hsu, B.J.P.: An overview of microsoft web n-gram corpus and applications. In: Proceedings of the NAACL HLT 2010 Demonstration Session, pp. 45–48 (2010)

    Google Scholar 

  35. Webster, J.J., Kit, C.: Tokenization as the initial phase in nlp. In: COLING 1992 Volume 4: The 15th International Conference on Computational Linguistics (1992)

    Google Scholar 

  36. Yazdi, K.M., Yazdi, A.M., Khodayi, S., Hou, J., Zhou, W., Saedy, S.: Improving fake news detection using k-means and support vector machine approaches. Int. J. Electron. Commun. Eng. 14(2), 38–42 (2020)

    Google Scholar 

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Authors and Affiliations

  1. Department of Machine Learning, University of Economics in Katowice, 1 Maja, 40-287, Katowice, Poland

    Barbara Probierz, Jan Kozak, Piotr Stefański & Przemysław Juszczuk

Authors
  1. Barbara Probierz
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  2. Jan Kozak
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  3. Piotr Stefański
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  4. Przemysław Juszczuk
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Corresponding author

Correspondence to Barbara Probierz .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Democritus University of Thrace, Kimmeria, Xanthi, Greece

    Lazaros Iliadis

  3. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Probierz, B., Kozak, J., Stefański, P., Juszczuk, P. (2021). Adaptive Goal Function of Ant Colony Optimization in Fake News Detection. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_29

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  • DOI: https://doi.org/10.1007/978-3-030-88081-1_29

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