Research Article
Face News Detection Using Machine Learning Techniques
@INPROCEEDINGS{10.1007/978-3-031-66044-3_23, author={R. Sai Venkat and Ramu kuchipudi and K. Gangadhara rao and G. Srikanth and Palamakula Ramesh babu and T. Satyanarayana Murthy and G. Venakata Kishore}, title={Face News Detection Using Machine Learning Techniques}, proceedings={Pervasive Knowledge and Collective Intelligence on Web and Social Media. Second EAI International Conference, PerSOM 2023, Hyderabad, India, November 24--25, 2023, Proceedings}, proceedings_a={PERSOM}, year={2024}, month={8}, keywords={Machine Learning Fake News learning optimization deep learning}, doi={10.1007/978-3-031-66044-3_23} }- R. Sai Venkat
Ramu kuchipudi
K. Gangadhara rao
G. Srikanth
Palamakula Ramesh babu
T. Satyanarayana Murthy
G. Venakata Kishore
Year: 2024
Face News Detection Using Machine Learning Techniques
PERSOM
Springer
DOI: 10.1007/978-3-031-66044-3_23
Abstract
Nowadays, most people are shifting to online news reading rather than traditional methods because of its convenience and low cost. So, the number of online users is increasing day by day, which is also increasing fake news across the internet. This fake news must be detected and removed from the internet before causing damage to the nation’s peace. Social media platforms use fake news recognition algorithms to detect and remove misinformation from their platforms. However, these algorithms can fail when they misidentify satirical or humorous content as fake news. News organizations use fake news recognition technology to fact-check articles and ensure the accuracy of their reporting. However, these algorithms may fail to detect more sophisticated forms of misinformation, such as deep fakes or highly persuasive disinformation campaigns, leading to the spread of false information. Existing framework contains three main modules: information retrieval, natural language processing, and machine learning. Also has two phases: the data collection phase and the machine learning model-building phase. In the data collection phase, we obtained a data set and analyzed the data using natural language processing techniques to extract good features from web data. A detailed survey on an automatic online fake news detection using machine learning techniques are elaborated.
