Fake News Detection in Healthcare Using Machine Learning

Title

Fake News Detection in Healthcare Using Machine Learning

Creator

Hormese, Tessa; Rajesh, R.

Description

The internet has revolutionary power in todays society, acting as an unmatched catalyst for technical innovation, worldwide connectedness, and information dissemination. It has transformed communication and made knowledge more accessible to all, and given people, companies, and society the tools they need to prosper in the connected digital world. However, this power is responsible for navigating issues, such as the proliferation of fake news and safeguarding information integrity. As peoples health comes first, false information about it might have disastrous consequences. Even for the most knowledgeable professionals in the field, identifying false information about health can be difficult because of the variety of factors that must be considered. New advances in machine learning have enabled automatic classification of bogus news. For the detection of fake news correctly, we must train the automation in such a way that it captures the bogus correctly, and for that the data we input is of at most importance and, in fact, the most important as well. To enhance the models capacity to discern between authentic and fake news, this study investigates the extraction of structural and semantic information from text using a combination of named entity recognition and syntactic parsing. Utilizing these characteristics, we trained a variety of machine learning algorithms, assessed their effectiveness, and found that the Random Forest classifier outperformed the others in classification. 2025 Scrivener Publishing LLC.

Source

Mathematics and Computer Science for Real-World Applications;pp.235-250

Date

01-01-2025

Publisher

wiley

Subject

Fake news; Healthcare; Natural language processing; Vectorization

Coverage

Hormese T., Department of Statistics and Data Science, Christ (Deemed to be University), Bangalore, India; Rajesh R., Department of Statistics and Data Science, Christ (Deemed to be University), Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 978-139427535-9; 978-139427532-8;

Format

online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1002/9781394275359.ch15

https://www.scopus.com/pages/publications/105013712230?origin=resultslist