DeepNet: An Efficient Neural Network for Fake News Detection using News-User Engagements

The rise of social media allows every user to share and immediately publish their views. Today, the problem of fake news has obtained significant consideration among researchers due to its harmful nature to deceive the people of the society. It has created an alarming situation in the world. News ecosystem evolved from a small set of trusted and regulated sources to numerous online news sources. Fake news has an adverse impact on society as it may manipulate public opinions. Thus, it is essential to investigate the credibility of news articles shared on social media outlets. In this paper, we have designed an effective deep neural network that is capable of handling not only the content of the news article but also the user-relationships in the social network. We have designed our proposed approach using tensor factorization method. A tensor expresses the social context of news articles formed by a combination of the news, user, and user-group information. Our proposed method (DeepNet) has validated on a real-world fake news dataset: BuzzFeed and Fakeddit. DeepNet outperforms from existing fake news detection methods by employing deep architecture with different kernelsizes convolutional layers combining news content and social context-based features.

[1]  He Jiang,et al.  Combating Fake News , 2019, ACM Trans. Intell. Syst. Technol..

[2]  Yanjie Fu,et al.  Fake News Detection with Deep Diffusive Network Model , 2018, ArXiv.

[3]  Tamara G. Kolda,et al.  All-at-once Optimization for Coupled Matrix and Tensor Factorizations , 2011, ArXiv.

[4]  Sandya Mannarswamy,et al.  CIMTDetect: A Community Infused Matrix-Tensor Coupled Factorization Based Method for Fake News Detection , 2018, 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[5]  Wei Gao,et al.  Detecting Rumors from Microblogs with Recurrent Neural Networks , 2016, IJCAI.

[6]  Richard A. Harshman,et al.  Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-model factor analysis , 1970 .

[7]  Claire Cardie,et al.  Finding Deceptive Opinion Spam by Any Stretch of the Imagination , 2011, ACL.

[8]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[9]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Yejin Choi,et al.  Syntactic Stylometry for Deception Detection , 2012, ACL.

[11]  Yongdong Zhang,et al.  News Verification by Exploiting Conflicting Social Viewpoints in Microblogs , 2016, AAAI.

[12]  Suhang Wang,et al.  Fake News Detection on Social Media: A Data Mining Perspective , 2017, SKDD.

[13]  Zhoujun Li,et al.  TI-CNN: Convolutional Neural Networks for Fake News Detection , 2018, ArXiv.

[14]  Sungyong Seo,et al.  CSI: A Hybrid Deep Model for Fake News Detection , 2017, CIKM.

[15]  Marcelo Mendoza,et al.  Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011 , 2011 .

[16]  Kai Shu.  Beyond News Contents: The Role of Social Context for Fake News Detection , 2018 .

[17]  Reza Zafarani,et al.  Fake News: A Survey of Research, Detection Methods, and Opportunities , 2018, ArXiv.

[18]  C. R. Rao,et al.  SOLUTIONS TO SOME FUNCTIONAL EQUATIONS AND THEIR APPLICATIONS TO CHARACTERIZATION OF PROBABILITY DISTRIBUTIONS , 2016 .

[19]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[20]  Eugenio Tacchini,et al.  Some Like it Hoax: Automated Fake News Detection in Social Networks , 2017, ArXiv.

[21]  Stephan Günnemann,et al.  Introduction to Tensor Decompositions and their Applications in Machine Learning , 2017, ArXiv.

[22]  Jiawei Han,et al.  Evaluating Event Credibility on Twitter , 2012, SDM.

[23]  Yimin Chen,et al.  Misleading Online Content: Recognizing Clickbait as "False News" , 2015, WMDD@ICMI.