Evaluation of User Activity Parameters in Social Networks Using Machine Learning Tools

Social networks provide a huge amount of data that can be used to understand user behavior because they cover a wide range of human activities such as posts, comments, likes and follower interactions. Analyzing these data helps to identify patterns and trends that can shape patterns of behavior and assess the parameters of their activity. Key parameters such as the frequency of users' actions, their interactions, the content they share, and the emotional tone of their communications offer valuable insights into user behavior. By formalizing these parameters, large data sets can be turned into meaningful indicators for analyzing behavior, predicting or improving existing models by using machine learning tools, namely: regression methods or by applying their classification. To improve the accuracy of the analysis, it is extremely important to choose appropriate activity indicators and take into account changes over time and different types of interactions. This research suggests analyzing key metrics, including post frequency and user engagement, and visualizing these metrics to gain a deeper understanding of user behavior trends. According to the obtained results, it is possible to calculate the audience engagement factor, the popularity factor of the post in social networks, and the audience retention rate. In addition, an important result when evaluating user parameters for analyzing their behavior is the content influence factor, which shows the contribution of a user's post to other users