Ritika Sharma, Gauri Kumari, Ashish Kumar Rauniyar, Gourab Das,
Rajnish Kr Mishra, Pawan K. Chaurasia
Department of Information Technology, Babasaheb Bhimrao Ambedkar University Central University, Lucknow – 226025, India
Cite this article: Sharma, R., Kumari, G., Rauniyar, A.K., Das, G., Mishra, R.K., Chaurasia, P.K., 2024. Unmasking Social Fake News: Machine Learning Approach. J. Appl. Sci. Innov. Technol. 3 (1), 20-32.
Highlights
- Machine Learning can analyze social media text to identify fake news.
- Linguistic patterns in the text itself can reveal clues about fakery.
- Social network factors like who shares the information can also be considered.
Abstract:
The increasing use of social media, has led to inconsistencies in online news, causing confusion and uncertainty for consumers. The spread of the ‘fake or false’ news on social media platform is a matter of serious concern due to its destructive impact on social and national sector. There are a lot of on-going research works dedicated to fake or false news detection. Fake or false news and disinformation spread on social media platforms negatively affects stability and social harmony. This paper showcases ‘fake news’ detection models using machine learning algorithms. The paper categorizes and describes the best approaches in several landscape of ‘fake news’ (text) detection across different domains that include ‘health, religion, crime, forged documents, jobs, and politics’. It explores into the problem’s dimensions, existing methodologies, their comparative analysis, and proposes an innovative solution for the on-going battle against misinformation. In addition to creating a model with supervised ML algorithm that can classify the news as ‘true or false’ by using different tools. The model will undergo the feature selection methods, to experiment and chosen the best-fit features to obtain the accurate and best performance.
Keywords: Fake News; Internet; Social Media; Articles; Machine Learning Models
Scope: Information Technology
