Discovering influential content for improving knowledge management based on deep learning

The presence of social media has brought about fundamental changes in today's societies. These media are a good platform for users to share ideas. This has caused users to encounter a lot of information that is not suitable for them most of the time and has little influence on them. Providing a way to select effective posts for the user among a multitude of posts can be very important. The methods presented in recent research for selecting effective posts are based on statistical features related to various microblog data and less in terms of content content, each post has been measured on a specific user. Despite the variety of topics, content of tweets and different users, most of these methods are not accurate by providing a general model based on a large number of features and are not able to provide online predictions. In this study, by analyzing the dissemination of posts among users in a specific period of time, a method for measuring users' attention to shared content and their effects is examined. This method is named IKS, which is based on the characteristics of content published by The user is presented as a binary classroom problem based on in-depth learning. Evaluation of this method has been done using intuitive method and data set evaluation which is more accurate compared to other methods.