AI-Driven Strategies For Detecting Fake News

Abstract

The propagation of disinformation online has become a major factor in undermining the trustworthiness of modern data circulation processes. The main purpose of this fnal year project is to fll this gap by creating such a robust system for detecting fake news in real-time using AI techniques. The project harnesses Natural Language Processing (NLP), machine learning algorithms and deep learning models to parsing textual content and detecting indicators that the content may be misinformation. The adopted system program includes a multidimensional solution, starting with a data collection system that could get different sets of articles from various sources. Pre-processing operations are performed to clean and restructure the textual data for more effective analysis. In the core of the system, we employ the latest NLP methods, for example, Bag of Words, and TF-IDF (Term Frequency-Inverse Document Frequency), to distinguish the real sense behind the news content and sort out the underlying context and intent. Deep learning methods with feature extraction and model training algorithms use labeled datasets to improve the accuracy and capacity of the model. Moreover, the analysis is done using deep learning methods like neural networks (NNs) and transformers to understand complex relationships among news articles. In addition to the immediate goals, the project strives to make real-time predictions a reality by utilizing efficient algorithms. Through the application of streaming data processing techniques, the system can examine live news material in real-time and determine instantly the reliability of the fresh information. This real-time mechanism assures the system’s efficiency in tackling the spread of misinformation on the online platforms that seem to be unfolding very fast. Besides real-time predictions, this project also plans to involve the participators by adding extra interactive functions including a news quiz. Users can compete in contests specially designed to run their ability to detect fake news once the analyzing system has been implemented. In addition to the learning experience, users get the incentive to improve their media literacy skills while they are also helping the system train its data with their interactions. Such participation additionally encourages critical thinking and information literacy among users, while it enables the detection system to be additively improved through crowd-sourced feedback.