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Comparative Micro Blogging News Analysis on the COVID-19 Pandemic Scenario

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Proceedings of International Conference on Data Science and Applications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 287))

Abstract

The coronavirus outbreak (COVID-19) was followed by a significant number of false and unreliable content, particularly on a social media forum like Twitter, Facebook, or news portal. The Covid-19 pandemic has triggered havoc all over the planet, but the propagation of false news, termed rumor, correlates with this global fatal pandemic. The dissemination of rumors on social networking sites is quicker than the spreading of Corona Virus among people and may have heavy harmful health implications in a tragedy like COVID-19. This is compounded even during a pandemic. Therefore, such rumors may be described as a major concern for our social life. Fake news can be classified and not published on social media in order to shield users from these rumors. We tried to build a model in this paper to filter false news of Twitter. Toward this purpose, experimental evaluation on eight different machine learning models like Support Vector Machine, and different deep learning models like glove embedding or lstm on the Twitter dataset of 8560 tweets to distinguish false news regarding Covid-19 is conducted. We also employed context learning and summarization of the dataset. This research (fake news identification) allows people to get solid information and recognize those spread rumors.

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References

  1. Clement, J.: Number of global social network users 2010–2023. Retrieved 30 April 2020 (2020)

    Google Scholar 

  2. Allcott, Hunt, Gentzkow, Matthew: Social media and fake news in the 2016 election. Journal of economic perspectives 31(2), 211–36 (2017)

    Article  Google Scholar 

  3. Aghababaeian, Hamidreza, Hamdanieh, Lara, Ostadtaghizadeh, Abbas: Alcohol intake in an attempt to fight COVID-19: A medical myth in Iran. Alcohol 88, 29–32 (2020)

    Article  Google Scholar 

  4. Patel, N.A., Patel, R.: A survey on fake review detection using machine learning techniques. In: 2018 4th International Conference on Computing Communication and Automation (ICCCA). IEEE (2018)

    Google Scholar 

  5. Patwa, P., et al.: Fighting an Infodemic: COVID-19 Fake News Dataset (2020). arXiv preprint arXiv:2011.03327

  6. Granik, M., Mesyura, V.: Fake news detection using Naive Bayes classifier. In: 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON). IEEE (2017)

    Google Scholar 

  7. Reis, J.C.S., et al.: Supervised learning for fake news detection. IEEE Intell. Syst. 34(2), 76–81 (2019)

    Google Scholar 

  8. Shu, K., et al.: Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor. Newsl. 19(1), 22–36 (2017)

    Google Scholar 

  9. Shu, K., Wang, S., Liu, H.: Beyond news contents: the role of social context for fake news detection. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (2019)

    Google Scholar 

  10. Gilda, S.: Evaluating machine learning algorithms for fake news detection. In: 2017 IEEE 15th Student Conference on Research and Development (SCOReD). IEEE (2017)

    Google Scholar 

  11. Shu, Kai, Mahudeswaran, Deepak, Liu, Huan: FakeNewsTracker: a tool for fake news collection, detection, and visualization. Computational and Mathematical Organization Theory 25(1), 60–71 (2019)

    Article  Google Scholar 

  12. Vijjali, R., et al. : Two stage transformer model for covid-19 fake news detection and fact checking (2020). arXiv preprint arXiv:2011.13253

  13. Saif, H., Fernández, M., He, Y., Alani, H.: On stopwords, filtering and data sparsity for sentiment analysis of twitter. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), pp. 810–817. Reykjavik, Iceland: European Language Resources Association (ELRA) (2014)

    Google Scholar 

  14. Willett, P.: The Porter stemming algorithm: then and now. Program (2006)

    Google Scholar 

  15. Richardson, L.: Beautiful soup documentation. Dosegljivo: https://www.crummy.com/software/BeautifulSoup/bs4/doc/. [Dostopano: 7. 7. 2018] (2007)

  16. Thompson, Ken: Programming techniques: Regular expression search algorithm. Communications of the ACM 11(6), 419–422 (1968)

    Article  Google Scholar 

  17. Heimerl, F., et al.: Word cloud explorer: Text analytics based on word clouds. In: 2014 47th Hawaii International Conference on System Sciences. IEEE (2014)

    Google Scholar 

  18. Nargesian, F., et al.: Learning feature engineering for classification. IJCAI (2017)

    Google Scholar 

  19. Shi, Congying, Chaojun, Xu., Yang, Xiaojiang: Study of TFIDF algorithm. Journal of Computer Applications 29(6), 167–170 (2009)

    Google Scholar 

  20. Grefenstette, G.: Tokenization. Syntactic Wordclass Tagging, pp. 117–133. Springer, Dordrecht (1999)

    Google Scholar 

  21. Rodríguez, P., et al.: Beyond one-hot encoding: Lower dimensional target embedding. Image Vis. Comput. 75, 21–31 (2018)

    Google Scholar 

  22. Drori, I., et al.: AlphaD3M: machine learning pipeline synthesis. AutoML Workshop at ICML (2018)

    Google Scholar 

  23. Noble, W.S.: What is a support vector machine? Nat. Biotechnol.D 24(12), 1565–1567 (2006)

    Article  Google Scholar 

  24. Wright, R.E.: Logistic regression. In: Grimm, L.G., Yarnold, P.R. (eds.) Reading and Understanding Multivariate Statistics, pp. 217–244. American Psychological Association (1995)

    Google Scholar 

  25. Raschka, S.: Naive bayes and text classification i-introduction and theory (2014). arXiv preprint arXiv:1410.5329

  26. Liaw, A., Wiener, M.: Classification and regression by randomForest. R news 2(3), 18–22 (2002)

    Google Scholar 

  27. Peterson, L.E.: K-nearest neighbor. Scholarpedia 4(2), 1883 (2009)

    Google Scholar 

  28. Freund, Yoav, Schapire, Robert, Abe, Naoki: A short introduction to boosting. Journal-Japanese Society For Artificial Intelligence 14(771–780), 1612 (1999)

    Google Scholar 

  29. Chen, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4–2, 1–4 (2015)

    Google Scholar 

  30. Levy, Omer, Goldberg, Yoav: Neural word embedding as implicit matrix factorization. Adv. Neural Inf. Process. Syst. 27, 2177–2185 (2014)

    Google Scholar 

  31. Sundermeyer, M., Schlüter, R., Ney, H.: LSTM neural networks for language modeling. In: Thirteenth Annual Conference of the International Speech Communication Association (2012)

    Google Scholar 

  32. Weerts, H.J.P, Mueller, A.C., Vanschoren, J.: Importance of tuning hyperparameters of machine learning algorithms (2020). arXiv preprint arXiv:2007.07588

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Author information

Authors and Affiliations

  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

    Chetna Kaushal

  2. Information and Communication Technology, Islamic University, Kushtia, 7003, Bangladesh

    Md Abu Rumman Refat & Md Khairul Islam

  3. Prime University, Dhaka, 1216, Bangladesh

    Md Al Amin

Authors
  1. Chetna Kaushal
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  2. Md Abu Rumman Refat
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  3. Md Al Amin
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  4. Md Khairul Islam
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Editor information

Editors and Affiliations

  1. Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Mukesh Saraswat

  2. Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India

    Sarbani Roy

  3. Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India

    Chandreyee Chowdhury

  4. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia

    Amir H. Gandomi

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Kaushal, C., Refat, M.A.R., Amin, M.A., Islam, M.K. (2022). Comparative Micro Blogging News Analysis on the COVID-19 Pandemic Scenario. In: Saraswat, M., Roy, S., Chowdhury, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 287. Springer, Singapore. https://doi.org/10.1007/978-981-16-5348-3_30

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