Fake News Detection and Classify the Category

Title

Fake News Detection and Classify the Category

Creator

Mitra P.; Jacob L.

Description

A new type of disinformation has emerged: fake news, or untrue stories that have been presented as actual occurrences. We can no longer tell whether the information is true from fraudulent since so much information is published on social media these days. Artificial intelligence algorithms are helpful in resolving the fake news identification issue. In the field of natural language processing, fake news identification is a crucial yet difficult issue (NLP). In this article, we discuss similar duties as well as the difficulties associated with finding bogus news. Based on these findings, we suggest intriguing avenues for future study, such as developing more accurate, thorough, fair, and useful detection models. The average public's life is impacted by mass media since it happens regularly. Because of this, news stories are written that are somewhat true or even entirely untrue. Using online social networking sites, people deliberately promote these fake goods. It is crucial to decide whether the news is false owing to its potential to have detrimental social and national effects. The false news identification process made use of many criteria, including the headline and body content of the news piece. The suggested method works effectively in terms of producing results with excellent accuracy, precision, and memory. Comparing all the models employed in this study, it was discovered that Distillbert and multinomial nae bayes models perform better than Logistic and others ml models. The credibility of the story may be evaluated using a larger dataset for better results and additional variables like the author and publisher of the news. Grenze Scientific Society, 2023.

Source

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023, Vol-2023-June, pp. 2053-2065.

Date

2023-01-01

Publisher

Grenze Scientific Society

Subject

Artificial Intelligence Algorithm; Distillbert; Fake news; Identification; Multinomial Nae Bayes; Natural Language Processing

Coverage

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

Rights

Restricted Access

Relation

0

Format

Online

Language

English

Type

Conference paper

Identifier

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174225258&partnerID=40&md5=b7a6298ff981f1c0b761da29a55e9bc7