Tools
Tools
Ravichandran, Navya (2024) Detecting misinformation Across Social Media, Healthcare, and Job Posting Websites using Machine Learning and Deep Learning Techniques. Masters thesis, Dublin, National College of Ireland.
Preview |
PDF (Master of Science)
Download (6MB) | Preview |
Preview |
PDF (Configuration Manual)
Download (3MB) | Preview |
Abstract
In recent days there is a rapid growth in technology across various streams like social media, Health care and Job Posting websites. This growth has led to the spread of false information called rumors among the public which have reduced their safety and trust in usage of technology and social websites. To address this, in our paper we will be developing a model that uses both Machine learning and Deep Learning algorithms in detection of fake news among multiple streams like healthcare, job posting websites and social media. Here datasets that have both images and texts are trained and tested against multiple advanced algorithms. A hybrid model is developed as a result to predict the false information with various data collection and Data Preprocessing techniques. Results show the efficiency of hybrid model by comparing its prediction accuracy across the techniques implemented. The implementation of this multi modal data helps to share only reliable online information across social media and other platforms. The model in final helps to promote social safety and trust among the end- users. The study includes combination of ten machine learning and deep learning algorithm across two datasets and CNN has better results comparatively. A detailed comparison is explained further in the implementation section. Here the machine learning data performs well on text data and deep learning algorithms performance is better on image data. Here the real time deployment of the model is a challenging and it will be addressed in future studies.
Actions (login required)
 |
View Item |