Smart Social Networking Platform In the last 5 years, the development of artificial intelligence technologies, data mining and big data analysis lead to more and more applications that form and group social activities. Especially in the field of social networks, more and more studies are constantly presented with the aim of analysing the data resulting from the activity of the users. In this study, there will be a presentation and research on the so-called "smart social networks", i.e. networks with self-learning mechanisms where they learn through the use of users and their profiles. Smart social networks offer the ability to evaluate user content, leading to recommendation systems regarding the material shared and offered by users, as well as what is best for users to see when there are many posts. At the same time, there is a possibility for each social network to become smarter, in terms of distinguishing the true message from the fake one. Moreover, we observe the emergence of social stratification based on the new model, i.e. the true messages initially published by a person are more similar by strengthening the self-learning mechanism. Meanwhile the use of other platforms e.g. education or games and the preferences of users in them, lead to crossover recommendation, i.e. interconnection between software making the connected people have higher social influences-contacts, resulting in learning possibilities at multiple levels. As the use of social media becomes increasingly prevalent in our daily lives, the need for a smarter and more responsible approach to networking has become apparent. One solution to this problem is the development of a social networking platform that uses the data generated by its users to learn and eventually deal with issues such as fake news. One of the key features of this platform would be the use of advanced machine learning algorithms to analyze the content shared by users. By analyzing patterns and trends in the data, the platform would be able to identify and flag potentially false or misleading information. This would include not only text-based posts, but also images and videos, as well as connections to other platforms. Another important aspect of this platform would be the ability for users to report and flag potentially false or misleading content. This would allow the platform to quickly identify and address any issues, while also providing a sense of community ownership and responsibility. In addition to identifying and addressing false information, the platform would also use the data generated by users to learn and adapt to the specific needs and concerns of its community. For example, it could use data on the types of content shared and the connections between users to identify and promote more diverse and inclusive content. Another important aspect of this platform would be the use of privacy-preserving techniques, such as differential privacy, to protect the data of its users while still allowing for valuable insights to be gleaned from it. Overall, the development of a smart social networking platform that uses data generated by its users to learn and address issues such as fake news would represent a significant step forward in the responsible and ethical use of social media. It would not only help to combat the spread of false information, but also promote a more diverse and inclusive online community.
Keywords Social Networks, Smart Social Networks, Recommendation Systems, Sentimental Analysis, Artificial Intelligence, Data Mining.