## 3 Materials & Methods  
The research proposes a novel model for mining user interest from social media based on personality and demographic data. Moreover, it proposes an interactive social network graph based on clustering. The proposed system can increase the accuracy of the system and alleviate the cold start problem. The mining of user interest consists of two main phases: topic modelling and user modelling. First, the topic modelling phase aims to extract interest topics for each user and to find similar topics based on semantic similarity. Furthermore, it builds a cluster of similar topics. Second, the user modelling phase aims to find similar users and relationships between users based on substantial factors.  
The purpose of the mining process is to present and recommend topics of interest to a user or to find similar people with similar interests. Finding a closely related topic or similar user can alleviate the cold start problem. The problem with the cold start is that, if a new user joins the social network, there is no available or historical information that can contribute to finding people who are similar to this user or topics that may be of interest. The appropriate solution is to examine different factors to find patterns of similarity between the users. Therefore, the decision can be taken to find similar users to this new user from the current network users. Consequently, this new user can be offered the appropriate recommendation.  
Several methods have been proposed in the mining system of user interests, but most suffer from a major issue, which is a cold start. Several researchers have attempted to address the cold start problem; however, they were largely unsuccessful. Some studies suggest demographic data as a solution to this problem, while others suggest personality [39; 40; 41]. Each of them plays a critical role in determining the interests of users, as the users' personality represents a rich and explicit source of prediction of interests, as is the case with demographic data. Nevertheless, the fact that demographic and personal data are complementary to each other is often neglected; both are indispensable for predicting and recommending users' interests. Therefore, this research proposes a hybrid method combining personality traits, demographic features, and topics of interest with the traditional calculation of score-based similarities. Personal data was primarily classified into five personality characteristics: openness, consciousness, agreeableness, consent, and neuroticism. Figure 1 shows the general system architecture for the mining process and extracting topics of interest.  
The previous researchers have also neglected to study the relationships and the extent of closeness between topics into a cluster to predict implicit users' interests. For instance, if user A is interested in several topics and a considerable part of them are in the same cluster, it can, therefore, be expected that the rest of the cluster topics are important to this user.  
Figure 1: General System Architecture for Mining User Interest from Social Medial.  
In this research, two factors to extract the users' interests were considered: (1) the demographic/personality/interests topics similarities through users, and (2) the semantic similarities through interests' topics, as shown in Figure 2.