Skip to main content
SpringerLink
Log in

Certain Investigation of Fake News Detection from Facebook and Twitter Using Artificial Intelligence Approach

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The news platform has moved from traditional newspapers to online communities in the technologically advanced area of Artificial Intelligence. Because Twitter and Facebook allow us to consume news much faster and with less restricted editing, false information continues to spread at an impressive rate and volume. Online Fake News Detection is a promising field in research and captivates the attention of researchers. The sprawl of huge chunks of misinformation in social network platforms is vulnerable to global risk. This article recommends using a Machine Learning optimization technique for automated news article classification on Facebook and Twitter. The emergence of the research is facilitated by the strategic implementation of Natural Language Processing for social forum fake news findings in order to distort news reports from non-recurrent outlets. The relent from the study is outstanding with text document frequency words, which act as extraction technique’s attribute, and the classifier is acted upon by Hybrid Support Vector Machine by achieving 91.23% accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Avinesh, P. V. S., Schiller, B., Caspelherr, F., Chaudhuri, D., Meyer, C. M., & Gurevych, I. (2018). A Retrospective Analysis of the Fake News Challenge Stance Detection Task. In Proceedings of the 27th International Conference on Computational Linguistics, pp 1859–1874, Santa Fe, New Mexico, USA.

  2. Ahmed H., Traore I., Saad S. (2017) Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In: Traore I., Woungang I., Awad A. (eds) Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. ISDDC 2017. Lecture Notes in Computer Science, vol 10618. Springer, Cham. https://doi.org/10.1007/978-3-319-69155-8_9

  3. Akshay Jain & Amey Kasbe (2018) “Fake News Detection,” IEEE International Students' Conference on Electrical, Electronics and Computer Sciences.

  4. Allamanis, M., & Sutton, C. (2016) A “Convolutional Attention Network for Extreme Summarization of Source Code. In Int. Conf. on Machine Learning vol 48 (New York, NY) 2091–2100

  5. Chen, Y.-C., Liu, Z.-Y., and Kao, H.-Y. (2017). IKM at SemEval-2017 Task 8: Convolutional Neural Networks for Stance Detection and Rumor Verification. In Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval2017), pp 465–469, Vancouver, Canada.

  6. Cody Buntain & Jennifer Golbeck (2017) Automatically Identifying Fake News in Popular Twitter Threads. IEEE International Conference on Smart Cloud (SmartCloud).

  7. Conroy, N., Rubin, V., & Chen, Y. (2015). Automatic deception detection: Methods for finding fake news. Proceedings of the Association for Information Science and Technology, 52(1), 1–4.

    Article  Google Scholar 

  8. Dai, J. J., Wang, Y., Qiu, X., Ding, D., Zhang, Y., Wang, Y., Jia, X., Zhang, L. C., Wan, Y., Li, Z., Wang, J., Huang, S., Wu, Z., Wang, Y., Yang, Y., She, B., Shi, D., Lu, Q., Huang, K., Song, G. (2019) Big DL: A distributed deep learning framework for big data. In Proceedings of the ACM symposium on cloud computing, Association for Computing Machinery. pp 50–60. https://arxiv.org/pdf/1804.05839.pdf, https://doi.org/10.1145/3357223.3362707

  9. Fake news detection using naive Bayes classifier. (2017) IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), pp 900–903.

  10. Felix Hamborg, Norman Meuschke, Corinna Breitinger, & Bela Gipp (2017) News-please: A generic news crawler and extractor. In Maria Gaede, Violeta Trkulja, and Vivien Petra, (eds.) Proceedings of the 15th International Symposium of Information Science, March 2017, pp. 218–223.

  11. Gim´enez, M., Baviera, T., Llorca, G., G´amir, J., Calvo, D., Rosso, P., & Rangel, F. (2017). Overview of the 1st Classification of Spanish Election Tweets Task at IberEval 2017. In Second Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval 2017).

  12. Helmstetter S, Paulheim H (2018) Weakly supervised learning for fake news detection on Twitter. In: 2018 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), IEEE, pp 274–277

  13. Hnin Ei Wynne & Zar Zar Wint. 2019. Content-Based Fake News Detection Using N-Gram Models. In Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services (iiWAS2019). Association for Computing Machinery, New York, NY, USA, 669–673. DOI:https://doi.org/10.1145/3366030.3366116

  14. Tagami, T., Ouchi, H., Asano, H., Hanawa, K., Uchiyama, K., Suzuki, K., Inui, K., Komiya, A., Fujimura, A., Yanai, H. et al. (2018) Suspicious News Detection Using Micro Blog Text. arXiv 2018, arXiv:1810.11663

  15. Jiang, T., Jian Ping Li, Amin Ul Haq, Abdus Saboor & Amjad Ali (2021) A Novel Stacking Approach for Accurate Detection of Fake News.” IEEE Access, 9, 22626–22639.

  16. Rubin, V. L., Chen, Y., & Conroy, N. J. (2015). Deception detection for news: Three types of fakes. Proceedings of the Association for Information Science and Technology, 52(1), 1–4.

    Article  Google Scholar 

  17. Xie, S., Wang, G., Lin, S., & Yu, P. S. (2012, April). Review spam detection via time-series pattern discovery. In Proceedings of the 21st International Conference on World Wide Web (pp. 635–636). ACM.

  18. Yaqing Wang, Fenglong Ma, Zhiwei Jin, Ye Yuan, Guangxu Xun, Kishlay Jha, Lu Su, & Jing Gao. Eann: Event adversarial neural networks for multi-modal fake news detection. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 849–857. ACM, 2018.

  19. Zubiaga, A. Aker, K. Bontcheva, M. Liakata, R. Procter (2018) Detection and resolution of rumors in social media: a survey. ACM Computer Survey, 51(2), 32:1–32:36.

  20. Horne, B.D., Adali, S.: This just in: fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. In: the 2nd International Workshop on News and Public Opinion at ICWSM (2017)

  21. Ahmad, I., Yousaf, M., Suhail Yousaf & Muhammad Ovais Ahmad (2020) Fake News Detection Using Machine Learning Ensemble Methods.” Hindawi-Complexity, 2020, 1–11.

  22. Islam, M. R., Liu, S., Wang, X., et al. (2020). Deep learning for misinformation detection on online social networks: A survey and new perspectives. Social Network Analysis and Mining, 10, 82. https://doi.org/10.1007/s13278-020-00696-x

    Article  Google Scholar 

  23. Ma, J., Gao, W., Mitra, P., Kwon, S., Jansen, B. J., Wong, K.-F., & Cha, M. (2016). Detecting rumors from microblogs with recurrent neural networks. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI, address, pp. 3818,3824

  24. Jan, B., Farman, H., Khan, M., Imran, M., Islam, I. U., Ahmad, A., Ali, S., & Jeon, G. (2017). Deep learning in big data Analytics: A comparative study. Computers & Electrical Engineering, 1, 1–13.

    Google Scholar 

  25. Jennifer Golbeck, Jennine B. Everett, Waleed Falak, Carl Gieringer, Jack Graney, Kelly M. Hoffman, Lindsay Huth, Zhenya Ma, Mayanka Jha, Misbah Khan, Varsha Kori, Matthew Mauriello, Elo Lewis, George Mirano, William T. Mohn IV, Sean Mussenden, Tammie M. Nelson, Sean Mcwillie, Akshat Pant, & Paul Cheakalos, Fake news vs satire: A dataset and analysis, 05 2018, pp. 17–21.

  26. Kamran Kowsari, Mojtaba Heidarysafa, Donald E. Brown, Kiana Jafari Meimandi, & Laura E. Barnes. RMDL: Random Multimodel Deep Learning for Classification. Proceedings of the 2nd International Conference on Information System and Data Mining—ISDM ’18, pages 19–28, 2018. arXiv:1805.01890.

  27. Kim, J., Tabibian, B., Oh, A., Schölkopf, B., & Gomez-Rodriguez, M. (2018) Leveraging the crowd to detect and reduce the spread of fake news and misinformation. In Proceedings of the eleventh ACM international conference on web search and data mining, pp 324–332

  28. Kuai, Xu., Wang, F., Wang, H., & Yang, Bo. (2019). Detecting fake news over online social media via domain reputations and content understanding. Tsinghua Science and Technology, 25(1), 20–27.

    Google Scholar 

  29. Lau, R. Y., Liao, S. Y., Kwok, R. C. W., Xu, K., Xia, Y., & Li, Y. (2011). Text mining and probabilistic language modeling for online review spam detecting. ACM Transactions on Management Information Systems, 2(4), 1–30.

    Article  Google Scholar 

  30. Li, L., Cai, G., & Chen, N. (2018a) A rumor events detection method based on deep bidirectional GRU neural network. In 2018 IEEE 3rd international conference on image. Vision and computing (ICIVC), IEEE, pp 755–759

  31. Markines, B., Cattuto, C., & Menczer, F. (2009). Social spam detection. In Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web (pp. 41–48)

  32. Umer, M., Imtiaz, Z., Ullah, S., & Mehmood, A. (2020). Gyu Sang Choi and Byung-Won On, “Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM).” IEEE Access, 8, 2169–3536.

  33. Natali Ruchansky, Sungyong Seo, & Yan Liu. CSI: A hybrid deep model for fake news detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp 797–806. ACM, 2017.

  34. Tolmie, P., Procter, R., Randall, D. W., Rouncefield, M., Burger, C., Wong Sak Hoi, G., Zubiaga, A., Liakata, M. (2017) Supporting the use of user-generated content in journalistic practice. In Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM, pp. 3632–3644

  35. Qing Liao, Heyan Chai, Hao Han, Xiang Zhang, Xuan Wang, Wen Xia & Ye Ding (2021) An Integrated Multi-Task Model for Fake News Detection. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2021.3054993.

  36. Rada Mihalcea, & Carlo Strapparava, The lie detector: explorations in the automatic recognition of deceptive language, Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, August 04–04, 2009, Suntec, Singapore

  37. Rubin, V. L., Conroy, N. J., Chen, Y., & Cornwell, S. (2016). Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News. In Proceedings of NAACL-HLT (pp. 7–17).

  38. Shivam B. Parikh & Pradeep K. Atrey (2018) Media-Rich Fake News Detection: A Survey. In IEEE Conference on Multimedia Information Processing and Retrieval.

  39. Ahmad, T., Akhtar, H., Chopra, A., & Waris Akhtar, M. (2014) Satire detection from web documents using machine learning methods, pp. 102–105.

  40. Bouazizi, M., & Ohtsuki, T. O. (2016). A pattern-based approach for Sarcasm detection on twitter. IEEE Access, 4, 5477–5488.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department Management, Universitas Airlangga, Jawa Timur, Indonesia

    Roy Setiawan

  2. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, 522502, Andhra Pradesh, India

    Vidya Sagar Ponnam

  3. Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, 627152, Tamil Nadu, India

    Sudhakar Sengan

  4. Department of Computer Sciences and Software Engineering, Faculty of Basic and Applied Sciences, International Islamic University, Islamabad, Pakistan

    Mamoona Anam

  5. Department of Information Technology, National Engineering College, Kovilpatti, 628503, Tamil Nadu, India

    Chidambaram Subbiah

  6. School of Agricultural and Food Engineering, Faculty of Food and Agricultural Technology, Pibulsongkram Rajabhat University, Phitsanulok, Thailand

    Khongdet Phasinam

  7. Department of Mechanical Engineering, PSN College of Engineering and Technology, Tirunelveli, 627152, Tamil Nadu, India

    Manikandan Vairaven & Selvakumar Ponnusamy

Authors
  1. Roy Setiawan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vidya Sagar Ponnam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sudhakar Sengan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mamoona Anam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chidambaram Subbiah
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Khongdet Phasinam
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Manikandan Vairaven
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Selvakumar Ponnusamy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sudhakar Sengan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Setiawan, R., Ponnam, V.S., Sengan, S. et al. Certain Investigation of Fake News Detection from Facebook and Twitter Using Artificial Intelligence Approach. Wireless Pers Commun 127, 1737–1762 (2022). https://doi.org/10.1007/s11277-021-08720-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08720-9

Keywords

Search

Navigation