An Approach for Detecting and Mitigating Fake...

An Approach for Detecting and Mitigating Fake News and Rumours on Social Media using Machine Learning Methods

Author Name : Venkata Ramana Kaneti

DOI: https://doi.org/10.56025/IJARESM.2023.06061888

ABSTRACT The extraction of valuable information from online sources is a rapidly evolving field within the realm of information technology. While traditional mass media outlets, such as news agencies, keep us informed about daily events, the contemporary landscape of social media platforms generates an enormous volume of usergenerated data. This data holds significant potential for containing news-related content of value. However, to harness the full potential of these resources, it becomes imperative to develop methods for sieving through the noise and extracting content that closely aligns with the quality and relevance of news media. Historically, two conventional techniques, namely Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA), were employed for topic discovery. LDA, a generative probabilistic model, found application in various tasks, including topic identification, while PLSA served as a statistical technique in the domain of topic modeling.