@Article{iasc.2022.022720,
AUTHOR = {N. Kanagavalli, S. Baghavathi Priya},
TITLE = {Social Networks Fake Account and Fake News Identification with Reliable Deep Learning},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {33},
YEAR = {2022},
NUMBER = {1},
PAGES = {191--205},
URL = {http://www.techscience.com/iasc/v33n1/46146},
ISSN = {2326-005X},
ABSTRACT = {Recent developments of the World Wide Web (WWW) and social networking (Twitter, Instagram, etc.) paves way for data sharing which has never been observed in the human history before. A major security issue in this network is the creation of fake accounts. In addition, the automatic classification of the text article as true or fake is also a crucial process. The ineffectiveness of humans in distinguishing the true and false information exposes the fake news as a risk to credibility, democracy, logical truth, and journalism in government sectors. Besides, the automatic fake news or rumors from the social networking sites is a major research area in the field of social media analytics. With this motivation, this paper develops a new reliable deep learning (DL) based fake account and fake news detection (RDL-FAFND) model for the social networking sites. The goal of the RDL-FAFND model is to resolve the major problems involved in the social media platforms namely fake accounts, fake news/rumor identification. The presented RDL-FAFND model detects the fake account by the use of a parameter tuned deep stacked Auto encoder (DSAE) using the krill herd (KH) optimization algorithm for detecting the fake social networking accounts. Besides, the presented RDL-FAFND model involves an ensemble of the machine learning (ML) models with different linguistic features (EML-LF) for categorizing the text as true or fake. An extensive set of experiments have been carried out for highlighting the superior performance of the RDL-FAFND model. A detailed comparative results analysis has stated that the presented RDL-FAFND model is considerably better than the existing methods.},
DOI = {10.32604/iasc.2022.022720}
}