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.

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Current study portrays a novel method for counterfeit prevention and brand security in the FMCG business. The introduced approach consolidates Internet of Things, Cloud, and Mobile advancements with the utilization of specially crafted savvy labels (smart tags) applied to each product to give track and follow capacities. The smart labels join QR code with extra data printed with an imperceptible photochromatic ink. The labels are enacted by spotlight on cell phones during the checking. Prior to checking, clients are provoked to choose the setting of the products (available, sold, and consumed) to give extra data about each product container as it travels through the inventory network. Consumer family types reveals essential information on family types and roles in selecting the product for purchasing. The statistical analysis of family type is 84.7 and 15.2 percentage (out of 354 members) in nuclear and joint family respectively. Awareness percentage on smart tags is 49.15, 32.17 and 18.07 percentage in consumer awareness, unaware and may be respectively. Analysis of fake products identification on smart tags is 53.67, 27.96 and 18.36 percentage in consumer identification, unidentified, and may be respectively. Counterfeit information on products identification on smart tags is 97.74, 2.259 percentage in counterfeit information obtained by consumer is higher than not obtained consumers. Analysis of benefit percentage on products is 94.63, 5.37 percentage in benefit percentage is higher than non-obtained consumers. Likeliness of IoT -Smart Tags on products is 76.28, 23.72 percentage (out of 354 members) in interested percentage is higher than not interested consumers.

Twitter being a famous social media site not only helps people to share their thoughts in microblogs but also plays a pivotal role in situations of emergency for communication, announcement and so on. However, it results in anaversive effect when inappropriate tweet is reposted or shared to people thereby spreading rumors. This work describesthe methodologies in identifying the rumors using specific attributes like precision, fi-score, recall and support thereby solving the ranging rumor issues across the social media platform. A system detects candidate's rumor from twitter and then evaluates it applicably. The result of experiment shows the proposed algorithm in order to detect the rumors with acceptable accuracy.