Fake Information Detection Techniques
Henil Chopada1, Maitri Patel2, Rushikesh Desai3, Divya Ebenezer Nathaniel4
1Henil Chopada, Student, Department of Computer Science and Engineering, Babaria Institute of Technology, Vadodara (Gujarat), India.
2Maitri Patel, Student, Department of Computer Science and Engineering, Babaria Institute of Technology, Vadodara (Gujarat), India.
3Rushikesh Desai, Student, Department of Computer Science and Engineering, Babaria Institute of Technology, Vadodara (Gujarat), India.
4Mrs. Divya Ebenezer Nathaniel, Assistant Professor, Department of Computer Science and Engineering, Babaria Institute of Technology, Vadodara (Gujarat), India.
Manuscript received on 26 April 2020 | Revised Manuscript received on 08 May 2020 | Manuscript Published on 22 May 2020 | PP: 79-86 | Volume-9 Issue-7S July 2020 | Retrieval Number: 100.1/ijitee.G10040597S20 | DOI: 10.35940/ijitee.G1004.0597S20
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Fake or unverified information spreads same as actual facts on the internet, hence maybe going viral and influencing the general public opinion and their choices. fake news represents the maximum present-day forms of fake and unverified facts, respectively, and need to be detected as soon as possible for averting their results. The interest in efficient detection techniques has been therefore growing very rapid in the remaining years. In this paper we present survey on the specific techniques to computerized detection of fake news proposed in the latest literature. Particularly, this paper focus on five main aspects. First, we record and discuss the various definitions of fake news that have been considered in various literatures. Second, we highlight how the collection of applicable data for simulation of fake information detection is tough and we present the numerous approaches, which have been adopted to accumulate this information, additionally the publicly available datasets. Third, we describe the features that have been used in various fake news detection techniques. Fourth, we provide an evaluation of various techniques used for detection. In the end, we discuss future directions that could be considered for this problem.
Keywords: Data Mining, Text Mining, Machine Learning, Deep Learning, Natural Language Processing, Classification, Fake News.
Scope of the Article: Community Information Systems