## 1 Introduction
Finally, even though there are several investigations in this field, they do not compare their suggested approach with earlier studies, leading to a shortage of common datasets and benchmarks in the perspective of user interest modeling from social networking sites [12; 13]. Therefore, this paper aimed to propose a model for mining the explicit interests of social media users by modelling users and topics based on their similarities, according to essential factors in identifying similar users and topics. Furthermore, it aimed to examine the interaction patterns between users and topics through a hybrid graph based on clustering to model user interrelationships and topic interrelationships. Besides, the proposed model contributes to alleviating the cold start problem (has explanation in Appendix A). There are three types of experiments that have been carried out to achieve the research objectives. In the first experiment, we extracted each user's interests, in addition to clustering similar topics into clusters. In the second experiment, found similarities between users based on a combination of factors. The third experiment generated a heterogeneous graph based on users and topics. The quality and performance of the interest topics that have been inferred for each user was determined using hamming loss, precision, recall, f1-score, accuracy, and match score as essential aspects. Moreover, performance was evaluated by measuring the inferred interest against actual interests obtained from users' tweets and biographies. Based on ground truth, the results accuracy was 85%, as we reviewed the exiting methods in our study [16]. The contributions of this research can be viewed from four points:  
* Building a novel model that extracts the explicit and implicit user interests' topics: dependence on user modelling based on multi-faceted demographic data, big five personality (has explanation in Appendix A) traits and interests topics. Demographic data include user age and gender, while big five personality traits include openness, conscientiousness, extraversion, agreeableness, neuroticism. On the other hand, the big five model is the most popular and B extensively used in the literature to determine personality trait. * Alleviating the cold start problem on social media: finding similar users to new user; these users must be similar in age, gender and personality traits, as these factors greatly affect the interests of the users. * Building a heterogeneous hybrid graph model that examines the patterns of interaction between users and topics, as well as finding implicit user interests based on topics clustering and semantic similarities between topics. * Building introductory dataset including factors that have been studied in this research that influence and support explicit and implicit user interests mining. This research answers the following research questions: Q1: What effects do demographic data, big five personality traits and interests' topics hold on making inferences about users' interests? Q2: Does multi-faceted user modelling mitigate the problem of cold start? Q3: How effective is the hybrid model based on a heterogeneous graph between user and topic to enhance inferences about implicit user interests? The remaining sections of this paper are organized as follows. In Section 2, we presented related studies in user interests mining. Section 3 discussed proposed materials and methods. In Section 4, we presented our experiment and results. In Section 5, we discussed our results. The final section concludes the paper with a summary.