Proposed Framework for Spam Recognition in Big Data for Social Media Networks in Smart Environment
Jitendra Pandey, Minimol Anil Job · 2019
Social Media Networks (SMNs) are becoming more and more popular across the globe in the past few years. Netizens share all their personal information regarding day to day activities, views, and opinions across various SMNs. Simultaneously, it can be observed that throughout the most popular SMNs face frequent social spam problems in various formats. Big Data theory is gaining much more attention and it is expected that SMNs will have more interactions with each other shortly. This would enable a spam link, content or profile attack to easily move from one social network like Twitter to other social networks like Facebook. Consequently, effective discovery of spam has turn out to be a noteworthy and prevalent issue. Proposed research highlights spam discovery across several SMNs by leveraging the data of sensing analogous spam inside an OSN (Open Source Network) and using it across various SMNs. Authors have selected Twitter and Facebook as the research marks, In this research paper, the authors proposed and presented a spam detection framework to find out spam on more than one social network those are most common features in terms of contents, behavior, posts and user involvement etc. Spam detection techniques can significantly facilitate in various social network to measure the vulnerability. Based on this fact the researchers have proposed and presented a spam detection framework to find out spam on more than one social network.