Fake News Detection and Classify the Category

Title

Fake News Detection and Classify the Category

Creator

Mitra P.; Jacob L.

Description

Everyone depends on numerous sources of E-news in today's world when the internet is ubiquitous. Online content abounds, especially social media feeds, many of which are unreliable and may not always be factual. For people to utilise social media platforms like Facebook, Twitter, and others, fake news is a topic that may be studied through Natural Language Processing techniques. Using ideas from natural language processing and machine learning applied to social media, our goal in this work is to conduct categorization of different news items that are available online. Our intention is to empower the user to utilise NLP (Natural Language Processing) methods to identify 'fake news,' which refers to misinformed material that may be categorised as genuine or false using software like Python. The model focuses on identifying false news sources based on several articles from a website, categorising the news as false or true, and determining its veracity using unreliable sources like scikit-learn and NLP for textual analysis of the website distributing the news. When a source is identified as a publisher of false news, which can be predicted with high vectorization and also suggested using the Python scikit-learn module to do tokenization and feature development, biased viewpoints may be identified and categorised in any subsequent articles from that source. 2022 IEEE.

Source

2022 International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2022

Date

2022-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Artificial Intelligence; Logistic Regression; Machine Learning; Multi-layer Perceptron; Natural Language Processing; Random Forest; Support Vector Machine

Coverage

Mitra P., Christ University Pune, Lavasa Campus, Department of Data Science, Pune, India; Jacob L., Christ University Pune, Lavasa Campus, Department of Data Science, Pune, India

Rights

Restricted Access

Relation

ISBN: 978-166545361-5

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/TQCEBT54229.2022.10041596

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149182145&doi=10.1109%2fTQCEBT54229.2022.10041596&partnerID=40&md5=00e69431597f7985c16388c24548f01e