A robust technique of fake news detection using Ensemble Voting Classifier and comparison with other classifiers

These days online networking is generally utilized as the wellspring of data as a result of its ease, simple to get to nature. In any case, expending news from online life is a twofold edged sword as a result of the widespread of fake news, i.e., news with purposefully false data. Fake news is a major issue since it affects people just as society substantial. In the internet based life, the data is spread quick and subsequently discovery component ought to almost certainly foresee news quick enough to stop the dispersal of fake news. Consequently, identifying fake news via web-based networking media is a critical and furthermore an in fact testing issue. In this paper, Ensemble Voting Classifier based, an intelligent detection system is proposed to deal with news classification both real and fake tasks. Here, eleven mostly well-known machine-learning algorithms like Naïve Bayes, K-NN, SVM, Random Forest, Artificial Neural Network, Logistic Regression, Gradient Boosting, Ada Boosting, etc. are used for detection. After cross-validation, we used the best three machine-learning algorithms in Ensemble Voting Classifier. The experimental outcomes affirm that the proposed framework can accomplish to about 94.5% outcomes as far as accuracy. The other parameters like ROC score, precision, recall and F1 are also outstanding. The proposed recognition framework can effectively find the most important highlights of the news. These can also be implemented in other classification techniques to detect fake profiles, fake message, etc.

[1]  Robin M. Kowalski,et al.  Bullying in the digital age , 2017 .

[2]  Peter K. Smith,et al.  Cyberbullying: its nature and impact in secondary school pupils. , 2008, Journal of child psychology and psychiatry, and allied disciplines.

[3]  James J. Lindsay,et al.  Cues to deception. , 2003, Psychological bulletin.

[4]  B. Depaulo,et al.  Accuracy of Deception Judgments , 2006, Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc.

[5]  Zhenyu He,et al.  Particle filter re-detection for visual tracking via correlation filters , 2018, Multimedia Tools and Applications.

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

[7]  Di Yuan,et al.  Patch-based visual tracking with online representative sample selection , 2017, J. Electronic Imaging.

[8]  Issa Traoré,et al.  Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques , 2017, ISDDC.

[9]  Huan Liu,et al.  Exploiting Tri-Relationship for Fake News Detection , 2017, ArXiv.

[10]  Atik Mahabub,et al.  A robust voting approach for diabetes prediction using traditional machine learning techniques , 2019, SN Applied Sciences.

[11]  Shubhi Shrivastava,et al.  Spam mail detection through data mining techniques , 2017, 2017 International Conference on Intelligent Communication and Computational Techniques (ICCT).

[12]  Shlok Gilda,et al.  Evaluating machine learning algorithms for fake news detection , 2017, 2017 IEEE 15th Student Conference on Research and Development (SCOReD).

[13]  Mehdi Parviz,et al.  Multivariate Polynomials Estimation Based on GradientBoost in Multimodal Biometrics , 2008, ICIC.

[14]  Qiao Liu,et al.  Object tracking based on online representative sample selection via non-negative least square , 2018, Multimedia Tools and Applications.

[15]  Vinod Patidar,et al.  A Novel Technique of Email Classification for Spam Detection , 2013 .

[16]  Trevor Hastie,et al.  Multi-class AdaBoost ∗ , 2009 .

[17]  Konstantin Tretyakov,et al.  Machine Learning Techniques in Spam Filtering , 2004 .

[18]  Di Yuan,et al.  A multiple feature fused model for visual object tracking via correlation filters , 2019, Multimedia Tools and Applications.

[19]  Fenglong Ma,et al.  Discovering social spammers from multiple views , 2017, Neurocomputing.

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

[21]  William Yang Wang.  “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection , 2017, ACL.

[22]  Fenglong Ma,et al.  EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection , 2018, KDD.

[23]  Al-Zadid Sultan Bin Habib,et al.  A voting approach of modulation classification for wireless network , 2019, NSysS.

[24]  Mykhailo Granik,et al.  Fake news detection using naive Bayes classifier , 2017, 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON).

[25]  Di Yuan,et al.  Graph refining via iterative regularization framework , 2019 .

[26]  Huan Liu,et al.  Understanding User Profiles on Social Media for Fake News Detection , 2018, 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR).

[27]  H. Russell Bernard,et al.  Studying Fake News via Network Analysis: Detection and Mitigation , 2018, Lecture Notes in Social Networks.

[28]  Yongdong Zhang,et al.  News Credibility Evaluation on Microblog with a Hierarchical Propagation Model , 2014, 2014 IEEE International Conference on Data Mining.