## 6 Result Discussion  
This section provides a clear discussion of this research and explanations for the results obtained. Social media content is rich in user interests and current trends. Predicting users' interests is an essential part of many applications, such as custom searches and recommendations. This research addresses identifying users' interests in social networking sites through fundamentally new factors that may alleviate the cold start problem and, thus, can propose recommendations for current users. Moreover, it studies the relationships between users and those between the subjects of interest and determines user patterns in social networks. Social media networks consist of several users, which are often represented as nodes, with links between nodes signifying the relationships between them. This research used a social network graph as a hybrid model to study inter-user relationships and inter-topic relationships and to visualise the social network graph. This is an appropriate way to present users and their interests and to study the relationships between them, as it facilitates reading information and gives a comprehensive overview of users' social network types, patterns, and interests. Due to the importance of the topics in finding similarities between users, the enhanced equation proposed takes the highest weight between personality factor and demographic data. As a result, the inferred topics' accuracy was calculated and represented 85%, which indicates the optimal results in topics inferring and, thus, the validity of the equation results. The results of this research indicate that there is validity in the factors that were relied upon to find similarities between users. Based on the enhanced equation results, users' interests can be determined precisely and can, thus, suggest topics to users based on those of interest to similar user profiles. This research proved that similar users often have the same interests, which can mitigate the cold start problem according to observations based on all cases on dataset. In the case of a new user joining a social network, similar users can be identified in the demographic data and big five personality traits. According to this, these users will have interests that will likely be of interest to the new users. It is also possible to determine the interests of current users in the network by searching for topics similar to their interest topics. The case studies presented above allow the following summary to be provided:  
* Depending on the first case, if the personality and demographic data are similar, such as age and gender, then the likelihood of having the same interests and similarity rate is high and confident. * Based on the second and third cases, if the personalities of users are similar and the demographic (age/gender) data differ, either one or both, then the state of confidence here is that their interests will often be different. It has also been observed that similarity in the demographic data mathematically gives a very high result, which is due to the encoding of gender to zero and one, and this makes it mathematically close to the first case; however, theoretically, and in reality, they are different. Hence, the interests were diverse for this reason; thus, a higher weight for interest topics was placed in the improved equation. Consequently, the similarity percentage decreased dramatically between the users, something which is in stark contrast to the initial example presented. * The fourth case showed a difference between users' personality traits, even though their demographic data were identical, and from which it could be seen that their interests differed. Thus, both demographic data and personality characteristics are intrinsic factors in determining users' interests and neither can be overlooked.