Fake News Detection Stay Informed: How to Spot Fake News Effectively

Divya, Daf and Tanu, Wasekar and Punam, Mendhe and Shreya, Kawale and Rina, Shirpurkar (2024) Fake News Detection Stay Informed: How to Spot Fake News Effectively. International Journal of Trend in Scientific Research and Development, 8 (5). pp. 753-759. ISSN 2456-6470

Text ijtsrd69447.pdf Download (949kB)

Abstract

In a time where information travels quickly because to digital channels, the proliferation of fake news presents serious obstacles to reasoned public discourse and decision-making. This essay examines the approaches and resources for identifying false news, highlighting the significance of media literacy and critical thinking. We look at a variety of tactics, such as fact-checking websites, teaching digital literacy, and using algorithms to find false content. We demonstrate the significance of social media in the spread of false information by examining case studies and contemporary trends. We also offer concrete recommendations for how people and organizations can improve their capacity to identify fake news. The ultimate goal of this research is to enable users to successfully negotiate the complicated information landscape and promote a better educated public that can tell reality from fabrication. The proliferation of misinformation in digital media has led to significant challenges in information credibility, prompting the need for effective fake news detection systems. This paper presents a comprehensive analysis of current methodologies for identifying fake news, focusing on both automated and human-centric approaches. We explore machine learning techniques, natural language processing, and network analysis, highlighting their strengths and limitations. Additionally, we examine the role of social media platforms in spreading misinformation and the ethical implications of detection algorithms. Our findings suggest that a hybrid approach, combining algorithmic solutions with user education and awareness, can enhance the reliability of information. By providing insights into emerging trends and future directions, this study aims to contribute to the ongoing discourse on combating fake news and fostering a more informed society. Fake news detection involves identifying misinformation online using various techniques. Key methods include machine learning algorithms that analyze text features, natural language processing to understand context, and social network analysis to track information spread. Hybrid approaches, combining automated systems with user feedback and education, are increasingly considered the ultimate solution for enhancing detection effectiveness. Current challenges include addressing bias in algorithms and ensuring transparency in detection processes. Would you like to delve deeper into any particular technique or case study.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	23 Oct 2024 09:39
Last Modified:	23 Oct 2024 09:39
URI:	http://eprints.umsida.ac.id/id/eprint/14304

Actions (login required)

View Item