Abstract
Owing to the rapid explosion of social networking portals in the past decade, we spread and consume information via the internet at an expeditious rate. It has caused an alarming proliferation of fake news on social networks. The global nature of social networks has facilitated international blowout of fake news. Fake news has proven to increase political polarization and partisan conflict. Fake news is also found to be more rampant on social media than mainstream media. The evil of fake news is garnering a lot of attention and research effort. In this work, we have tried to handle the spread of fake news via tweets. We have performed fake news classification by employing user characteristics as well as tweet text. Thus, trying to provide a holistic solution for fake news detection. For classifying user characteristics, we have used the XGBoost algorithm which is a collaboration of decision trees utilizing the boosting method. Further to correctly classify the tweet text we used various natural language processing techniques to preprocess the tweets and then applied a sequential neural network and state-of-the-art BERT transformer to classify the tweets. The models have then been evaluated and compared with various baseline models to show that our approach effectively tackles this problem.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gartner reveals Top Predictions for IT organizations and users in 2018 and and beyond, https://www.gartner.com/en/newsroom/press-releases/2017-10-03-gartner-reveals-top-predictions-for-it-organizations-and-users-in-2018-and-beyond, Accessed 20 Jan 2021
Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. J. Econ. Perspect. 31(2), 211–236 (2017)
News Use across Social Media Platforms 2016. https://www.journalism.org/2016/05/26/news-use-across-social-media-platforms-2016/
Castillo, C., Mendoza, M., Poblete, B.: Information credibility on twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 675–684, March 2011
Qazvinian, V., Rosengren, E., Radev, D., Mei, Q.: Rumor has it: identifying misinformation in microblogs. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 1589–1599, July 2011
Balmas, M.: When fake news becomes real: combined exposure to multiple news sources and political attitudes of inefficacy, alienation, and cynicism. Commun. Res. 41(3), 430–454 (2014)
Afroz, S., Brennan, M., Greenstadt, R.: Detecting hoaxes, frauds, and deception in writing style online. In: 2012 IEEE Symposium on Security and Privacy, pp. 461–475, IEEE May 2012
Chu, Z., Gianvecchio, S., Wang, H., Jajodia, S.: Detecting automation of twitter accounts: are you a human, bot, or cyborg? IEEE Trans. Dependable Secure Comput. 9(6), 811–824 (2012)
Takahashi, T., Igata, N.: Rumor detection on twitter. In: The 6th International Conference on Soft Computing and Intelligent Systems, and the 13th International Symposium on Advanced Intelligence Systems, pp. 452–457. IEEE November 2012
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, pp. 1103–1108. IEEE, December 2013
Chen, C., Wu, K., Srinivasan, V., Zhang, X.: Battling the internet water army: detection of hidden paid posters. In: 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013), pp. 116–120. IEEE, August 2013
Sakurada, M., Yairi, T.: Anomaly detection using autoencoders with nonlinear dimensionality reduction. In Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, pp. 4–11, December 2014
Zhao, Z., Resnick, P., Mei, Q.: Enquiring minds: early detection of rumors in social media from enquiry posts. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1395–1405, May 2015
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, June 2016
Hardalov, M., Koychev, I., Nakov, P.: In search of credible news. In: Dichev, C., Agre, G. (eds.) AIMSA 2016. LNCS (LNAI), vol. 9883, pp. 172–180. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44748-3_17
Vicario, M.D., Quattrociocchi, W., Scala, A., Zollo, F.: Polarization and fake news: early warning of potential misinformation targets. ACM Trans. Web (TWEB) 13(2), 1–22 (2019)
Zhang, X., Ghorbani, A.A.: An overview of online fake news: characterization, detection, and discussion. Inf. Process. Manag. 57(2), 102025 (2020)
Fernandez, A.C.T., Devaraj, M.: Computing the linguistic-based cues of fake news in the philippines towards its detection. In: Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics, pp. 1–9, June 2019
Gaurav, D., Tiwari, S.M., Goyal, A., Gandhi, N., Abraham, A.: Machine intelligence-based algorithms for spam filtering on document labeling. Soft. Comput. 24(13), 9625–9638 (2019). https://doi.org/10.1007/s00500-019-04473-7
Yang, S., Shu, K., Wang, S., Gu, R., Wu, F., Liu, H.: Unsupervised fake news detection on social media: a generative approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5644–5651, July 2019
Sharma, S., Kalra, V., Agrawal, R.: Depression discovery in cancer communities using deep learning. In: Handbook of Deep Learning in Biomedical Engineering, pp. 123–154. Academic Press, Boca Raton (2021)
Kanagaraj, N., Hicks, D., Goyal, A., et al.: Deep learning using computer vision in self driving cars for lane and traffic sign detection. Int. J. Syst. Assur. Eng. Manag. 12, 1011–1025 (2021). https://doi.org/10.1007/s13198-021-01127-6
Segaran, T., Hammerbacher, J.: Beautiful Data: The Stories Behind Elegant Data Solutions, O’Reilly Media Inc., Beijing (2009)
Norwig, P.: How to write a spelling corrector. norvig.com
Yang, F., Liu, Y., Yu, X., Yang, M.: Automatic detection of rumor on Sina Weibo. In: MDS 2012 (2012)
Ma, J., Gao, W., Wei, Z., Lu, Y., Wong, K.-F.: Detect rumors using time series of social context information on microblogging websites. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM 2015). Association for Computing Machinery, New York, NY, USA, pp. 1751–1754 (2015). https://doi.org/10.1145/2806416.2806607
Ma, J., et al.: Detecting rumors from microblogs with recurrent neural networks. In: IJCAI, pp. 3818–3824 (2016)
Kwon, S., Cha, M., Jung, K.: Rumor detection over varying time windows. Plos One 12, e0168344 (2017). https://doi.org/10.1371/journal.pone.0168344
Ma, J., Gao, W., Wong, K.-F.: Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning (2017). https://doi.org/10.18653/v1/P17-1066
Ajao, O., Bhowmik, D., Zargari, S.: Fake news identification on twitter with hybrid cnn and rnn models. In: Proceedings of the 9th International Conference on Social Media and Society, pp. 226–230, July 2018
Acknowledgments
The authors would like to thank Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, Huan Liu from FakeNewsNet for making their data available, enabling our research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Sharma, S., Saraswat, M., Dubey, A.K. (2021). Fake News Detection Using Deep Learning. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M. (eds) Knowledge Graphs and Semantic Web. KGSWC 2021. Communications in Computer and Information Science, vol 1459. Springer, Cham. https://doi.org/10.1007/978-3-030-91305-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-91305-2_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91304-5
Online ISBN: 978-3-030-91305-2
eBook Packages: Computer ScienceComputer Science (R0)