International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
•
Impact Factor: 9.24
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
Home
Research Paper
Submit Research Paper
Publication Guidelines
Publication Charges
Upload Documents
Track Status / Pay Fees / Download Publication Certi.
Editors & Reviewers
View All
Join as a Reviewer
Reviewer Referral Program
Get Membership Certificate
Current Issue
Publication Archive
Conference
Publishing Conf. with IJFMR
Upcoming Conference(s) ↓
WSMCDD-2025
GSMCDD-2025
Conferences Published ↓
RBS:RH-COVID-19 (2023)
ICMRS'23
PIPRDA-2023
Contact Us
Plagiarism is checked by the leading plagiarism checker
Call for Paper
Volume 6 Issue 6
November-December 2024
Indexing Partners
Discerning Truth: Leveraging Naïve Bayes for Fake News Detection
Author(s) | Kavitha I, Arshad Ahamed M, Deral Akshan A, Gokul S, Kogul M |
---|---|
Country | India |
Abstract | These days individuals get to know all the news, temperate and political undertakings through social media. The most deliberate is to redirect the truthfulness and inventiveness of the news. This kind of news spreading poses a serious threat to social cohesiveness and well-being since it fosters polarization in politics and mistrust among people. False news producers use elaborate, colorful traps to further the success of their manifestations, one of which is to incite the providers' emotions. The information-savvy community has responded by adopting measures to address the issue. Hence by utilizing machine learning Algorithm, we are reaching to make a demonstrate that separate the genuine and fake news. This system works with the operations of NLP (Normal Dialect Handling) ways for recognizing the Genuine Time ‘phony news’ that's deluding stories that come from the untrustworthy source. By performing nostalgic examination, the show is prepared to characterize the suppositions, feelings and demeanor in a corpus on the off chance that news. In this framework we utilized TexrBlob, which is one of the effective python library to preform nostalgic examination. Our model grounded on a TFIDF vectorizer (Term recurrence Converse Report recurrence). We accumulated our datasets from facebook, instagram, wire, twitter conjointly from various other social medias. We evacuated a few datasets from Kaggle to test and preparing our framework In order to offer a show that classifies a composition as false or genuine based on its words and expressions, a proposed method involves gathering a dataset of both fake and genuine news and using a Naïve Bayes classifier. For visualization we utilized Scene, which is used to mix each kind of information to assist for creating appealing visualization |
Keywords | Naive Bayes, fake news, Sentiment analysis, visualization |
Field | Engineering |
Published In | Volume 6, Issue 2, March-April 2024 |
Published On | 2024-04-25 |
Cite This | Discerning Truth: Leveraging Naïve Bayes for Fake News Detection - Kavitha I, Arshad Ahamed M, Deral Akshan A, Gokul S, Kogul M - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.18312 |
DOI | https://doi.org/10.36948/ijfmr.2024.v06i02.18312 |
Short DOI | https://doi.org/gtsg6g |
Share this
E-ISSN 2582-2160
doi
CrossRef DOI is assigned to each research paper published in our journal.
IJFMR DOI prefix is
10.36948/ijfmr
Downloads
All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.