Paper Title
A Combined Machine Learning and Deep Learning Techniques based Approach for Fake News Spreaders Detection
Article Identifiers
: Keywords
Fake News, Fake News Spreader, BERT, Term Weight Measure, Feature Selection Algorithm
Abstract
The textual data is increasing exponentially in the internet through various social network platforms like discussion Forums, Twitter, Reviews sites, Facebook, Blogs and etc. These platforms changed the method of communication among people. Most of the people are interested to exchange the genuine information in these platforms. Some of them generate false or fake information and spread this information in these platforms. The fake or false information is spreading to defame the reputation of companies, people, services, products, and places. The detection of fake news in people’s communication becomes a popular research area in recent times. Most of the researchers proposed several approaches to detect the fake news or false information by analysing the written text. One way of restricting the spreading of information is when every user checks the truth of news content before spreading news into different social media groups. It is very difficult in this abundant information world to know the correctness of the information. In this context, identification of fake news spreaders is useful for the community of people to recognize whether the textual message came from fake source or genuine source. The PAN competition organizers introduced a task of Fake News Spreaders (FNS) detection in 2020. The task is detecting whether the Twitter author is fake news spreader or not. The organizers provided Twitter dataset for fake news spreaders detection. In this work, we developed a new approach by combining the feature representation methodologies of machine learning techniques and concepts of BERT for features extraction. The documents are represented with the combination of features selected by the feature selection algorithm and the documents representation by the BERT model. Support Vector Machine (SVM) classifier is used for evaluating the efficiency of the proposed approach. The SVM classifier with combined vector representation shows best accuracy for fake news spreaders detection.
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How To Cite (APA)
Pandarinath Potluri, Satyasriram Potluri, K.Ramalakshmi, & LVS Narayana (December-2022). A Combined Machine Learning and Deep Learning Techniques based Approach for Fake News Spreaders Detection . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 7(12), a245-a253. https://ijnrd.org/papers/IJNRD2212036.pdf
Issue
Volume 7 Issue 12, December-2022
Pages : a245-a253
Other Publication Details
Paper Reg. ID: IJNRD_184511
Published Paper Id: IJNRD2212036
Downloads: 000122000
Research Area: Engineering
Author Type: Indian Author
Country: Narasapur, Andhra Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2212036.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2212036
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