Survey on Fake News Detection Techniques

In recent years widespread rumors and fake news has given rise to many social and political problems. Most of the information today is acquired from digital sources. In Digital media it is difficult to assign accountability to the opinion due to which the data received cannot be authenticated. Lack of constant supervision has motivated the miscreants to spread fake information. Fake news articles that are planted over digital media shares important linguistic features such as immoderate usage of unconfirmed hyperbole and non-verified quotes. It is necessary to invent an automated mechanism to identify fake news and also to minimize its impact by restricting its spread. This survey comprehensively and systematically studies different methodologies in the detection of fake news in digital media. The survey identifies and specifies fundamental theories in Machine Learning, to facilitate and enhance the research of fake news detection. By understanding the different methodologies in fake news studies, we highlight some potential research gaps at the end of this survey.

[1]  Kalina Bontcheva,et al.  Can Rumour Stance Alone Predict Veracity? , 2018, COLING.

[2]  Jignesh M. Patel,et al.  Estimating the selectivity of tf-idf based cosine similarity predicates , 2007, SGMD.

[3]  Kyeong-Hwan Kim,et al.  Fake News Detection System using Article Abstraction , 2019, 2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE).

[4]  Palagati Bhanu Prakash Reddy,et al.  Fake Data Analysis and Detection Using Ensembled Hybrid Algorithm , 2019, 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC).

[5]  Hernán A. Makse,et al.  CUNY Academic Works , 2022 .

[6]  Atik Mahabub.  A robust technique of fake news detection using Ensemble Voting Classifier and comparison with other classifiers , 2020 .

[7]  Bhavika Bhutani,et al.  Fake News Detection Using Sentiment Analysis , 2019, 2019 Twelfth International Conference on Contemporary Computing (IC3).

[8]  Oluwaseun Ajao,et al.  Sentiment Aware Fake News Detection on Online Social Networks , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  Yufeng Wang,et al.  Stacking-Based Ensemble Learning on Low Dimensional Features for Fake News Detection , 2019, 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS).

[10]  Chang-Sung Jeong,et al.  Natural Language Contents Evaluation System for Detecting Fake News using Deep Learning , 2019, 2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE).

[11]  Suin Seo,et al.  Offensive Sentence Classification Using Character-Level CNN and Transfer Learning with Fake Sentences , 2017, ICONIP.

[12]  Eslam Amer,et al.  Deep Learning Algorithms for Detecting Fake News in Online Text , 2018, 2018 13th International Conference on Computer Engineering and Systems (ICCES).

[13]  Anders Edelbo Lillie,et al.  Fake News Detection using Stance Classification: A Survey , 2019, ArXiv.