Prediction of Rumour Source Identification Using DRNN with LSTM in Online Social Networks
Keywords:
Fake News, Rumour, Prediction, Deep Learning, LSTM, DRNNAbstract
Recently, the utilization of informal communities, for example, Facebook, Twitter, and Sina Weibo has become an indistinguishable piece of our day by day lives. It is considered as a helpful stage for clients to share individual messages, pictures, and recordings. Notwithstanding, while individuals appreciate informal organizations, numerous beguiling exercises, for example, counterfeit news or reports can delude clients into accepting deception. In addition, spreading the monstrous measure of falsehood in interpersonal organizations has become a worldwide danger. Subsequently, falsehood identification (MID) in interpersonal organizations has acquired a lot of consideration and is viewed as an arising space of exploration interest. We track down that few investigations identified with MID have been concentrated to new research issues and strategies. While significant, in any case, the mechanized recognition of deception is hard to achieve as it requires the high-level model to see how related or disconnected the detailed data is when contrasted with genuine data. The current examinations have principally centered around three general classes of deception: bogus data, counterfeit news, and talk recognition. Consequently, identified with the past issues, we present a far-reaching overview of robotized deception identification on (i) bogus data, (ii) bits of hearsay, (iii) spam, (iv) counterfeit news, and (v) disinformation. The proposed work utilizing this deep learning approach like DNN, and LSTM accomplishes 82% precision. Our methodology instinctively recognizes pertinent highlights related with counterfeit reports without past information on the area.
Downloads
References
J Keller. 2013. A fake AP tweet sinks the DOWfor an instant.Bloomberg Businessweek (2013).
Jure Leskovec and Julian J Mcauley. 2012. Learning to discover social circles in ego networks. In Advances in neural information processing systems. 539–547.
Steve Schifferes, Nic Newman, Neil Thurman, David Corney, AyseGöker, and Carlos Martin. 2014. Identifying and verifying news through social media: Developing a user-centred tool for professional journalists. Digital Journalism 2, 3 (2014), 406–418.
Andrea Ceron, Luigi Curini, Stefano M Iacus, and Giuseppe Porro. 2014. Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media & Society 16, 2 (2014),340–358.
Emilio Ferrara. 2015. Manipulation and abuse on social media by emilioferrara with ching-man au yeung as coordinator. ACM SIGWEB Newsletter Spring (2015), 4.
Jean Burgess, Farida Vis, and Axel Bruns. 2012. Hurricane Sandy: The Most Tweeted Pictures. The Guardian Data Blog, November 6 (2012).
BBC. 2017. Facebook to tackle fake news in Germany 2017. (2017).
Olivia Solon. 2016. Facebook’s failure: Did fake news and polarized politics get Trump elected. The Guardian 10 (2016).
Craig Silverman. 2016. Here are 50 of the biggest fake news hits on Facebook from 2016. BuzzFeed, https://www.buzzfeed.com/craigsilverman/top-fake-news-of-2016 (2016).
Marcella Tambuscio, Giancarlo Ruffo, Alessandro Flammini, and Filippo Menczer. 2015. Fact-checking effect on viral hoaxes: A model of misinformation spread in social networks. In Proceedings of the 24th International Conference on World Wide Web. ACM, 977– 982.
Andreas M Kaplan and Michael Haenlein. 2010. Users of the world, unite! The challenges and opportunities of social media. Businessmhorizons 53, 1 (2010), 59–68.
Aditi Gupta, Hemank Lamba, PonnurangamKumaraguru, and Anupam Joshi. 2013. Faking sandy: characterizing and identifying fake images on twitter during hurricane sandy. In Proceedings of the 22nd international conference on World Wide Web. ACM, 729–736.
Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J Jansen, Kam-Fai Wong, and Meeyoung Cha. 2016. Detecting Rumors from Microblogs with Recurrent Neural Networks. In IJCAI. 3818–3824.
Andrej Karpathy and Li Fei-Fei. 2015. Deep visual-semantic alignments for generating image descriptions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3128–3137.
JiangWang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, and Wei Xu. 2016. Cnn-rnn: A unified framework for multi-label image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2285–2294.
Sejeong Kwon, Meeyoung Cha, Kyomin Jung, Wei Chen, and YajunWang. 2013. Prominent features of rumor propagation in online social media. In Data Mining (ICDM), 2013 IEEE 13th International Conference on. IEEE, 1103–1108.
Anupama Aggarwal, Ashwin Rajadesingan, and PonnurangamKumaraguru.2012. Phishari: automaticrealtime phishing detection on twitter. In eCrime Researchers Summit (eCrime), 2012. IEEE, 1–12.'
Sarita Yardi, Daniel Romero, Grant Schoenebeck, et al. 2009. Detecting spam in a twitter network. First Monday 15, 1 (2009).
John O’Donovan, Byungkyu Kang, Greg Meyer, Tobias Hollerer, and Sibel Adalii. 2012. Credibility in context: An analysis of featuredistributions in twitter. In Privacy, Security, Risk and Trust (PASSAT), 2012 international conference on and 2012 international conference on social computing (SocialCom). IEEE, 293–301.
Aditi Gupta, PonnurangamKumaraguru, Carlos Castillo, and Patrick Meier. 2014. Tweetcred: Real-time credibility assessment of content on twitter. In International Conference on Social Informatics. Springer, 228–243.
Klaus Greff, Rupesh K Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber. 2017. LSTM: A search space odyssey. IEEE transactions on neural networks and learning systems 28, 10 (2017), 2222–2232.
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short- termmemory. Neural computation 9, 8 (1997), 1735–1780.
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15, 1 (2014), 1929–1958.
ArkaitzZubiaga, Maria Liakata, and Rob Procter. 2016. Learning Reporting Dynamics during Breaking News for Rumour Detection in social media. arXiv preprint arXiv:1610.07363 (2016).
Jing Ma, Wei Gao, Zhongyu Wei, Yueming Lu, and Kam-Fai Wong. 2015. Detect rumors using time series of social context information on microblogging websites. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management. ACM, 1751–1754
Sejeong Kwon, Meeyoung Cha, and Kyomin Jung. 2017. Rumor detection over varying time windows. PloS one 12, 1 (2017), e0168344.
Shiou Tian Hsu, Changsung Moon, Paul Jones, and NagizaSamatova. 2017. A Hybrid CNN-RNN Alignment Model for Phrase-Aware Sentence Classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics:Volume 2, Short Papers, Vol. 2. 443–449.
Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning. 448–456.
Oluwaseun Ajao, Jun Hong, and Weiru Liu. 2015. A survey of location inference techniques on Twitter. Journal of Information Science 41, 6(2015), 855–864.
Gahirwal, M., Moghe, S., Kulkarni, T., Khakhar, D. and Bhatia, J., 2018. Fake News Detection. International Journal of Advance Research, Ideas, and Innovations in Technology, 4(1), pp.817-819.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Raja Kumari Mukiri, Vijaya Babu Burra
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
