Challenges and Techniques in Multilingual Fake News Detection

Challenges and Techniques in Multilingual Fake News Detection

Prof. Arunadevi S Khaple1, Shraddha Vijay Bhilare2, Vaishnavi Parshuram Chikhale3 ,

Komal Appasaheb Bhosale 4, Mayuri Suresh Bais5

1,2,3,4,5 Department of Computer Engineering, Zeal College of Engineering and Research, Pune, India.

Abstract - The spread of false information across language and cultural barriers poses a significant threat to global societies. This study investigates the obstacles in detecting misinformation in various languages, including linguistic diversity, limited datasets, cultural nuances, and the ever-changing strategies of disinformation propagators. These complex issues require advanced methods to effectively identify and combat false information across multiple languages. The research evaluates several prominent machine learning approaches, such as Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), Logistic Regression, Decision Trees, and Natural Language Processing (NLP) techniques. By examining their effectiveness in multilingual contexts, this study sheds light on the advantages and drawbacks of each method. It underscores the need for continuous innovation and collaborative efforts in developing robust detection systems that can adapt to the ever-changing landscape of misinformation. These initiatives aim to promote informed public discourse and improve information integrity across diverse cultural contexts.

Key Words:  Multilingual Fake News Detection, Machine Learning, Machine Learning algorithms, Natural Language Processing, Misinformation.