Computing the Relevance Degree of Social Media Network

Social Media Networks (SMNs) became very important in our life. SMNs made people communicating with almost everyone in the world. This technology facilitates the sharing of ideas and information through virtual communities. Accounts (pages) on SMNs are public profiles which are created for different reasons such as businesses, brands, and celebrities. These accounts can gain unlimited number of fans based on a profile orientation and its description that is published by owners. Accordingly, SMNs users face the content-related problem which is clear when they found unrelated content with profile orientation. A possible solution is computing the degree of relevance between the orientation and profile account content. This can be achieved by using the term frequency-inverse document frequency (TF-IDF) algorithm with some algebraic geometry such as intersection sets and mathematical framework such as probability. The promised output of this study is to obtain a matching percentage between the profile orientation and the content. Such outcomes can help users on social media networks make a suitable decision whether like or follow a certain account based on the content related.