Document Type : Original Article
Data Analysis & Processing Research Group, IT Research Faculty, ICT Research Institute, Iran; email@example.com
Net&Sys Security Assessment Research Group, ICT Security Faculty, ICT Research Institute, Iran;
Data Analysis & Processing Research Group, IT Research Faculty, ICT Research Institute, Iran;
E-Content & E-Services Research Group, IT Research Faculty, ICT Research Institute, Iran;
Content interpretation is a cognitive ability which is mostly concerned with understanding the intention of the person who has created or narrated the content. Narrator is also important since his/ her specific intention, which is inevitable in media like a social network, may change the reality of a created content. Here, the focal point is a sort of transformation with the aim of yielding the class of a message behind the content. In this paper, we propose a rule-based framework for interpreting contents in a social network that has the ability to perform such a transformation through using some generic rules with propositions at high abstraction level. The reason for selecting abstract propositions is their ability in covering wide range of facts occurring in the real world situation. Our suggested framework is in reality able to determine the class of a message indicating the possible intention of either a content’s creator or its narrator, such as whether a narrator is seeking honesty/justice toward the others, is after respect for the people, cares for compassion/ mercy, emphasizes a significance of knowing/ thinking in life, or is after self-upgradation to conduct a healthy life. These classes of message are determined according to both philosophical and psychological aspects which do exist behind the cognitive, emotional and ethical faculties in human being. Results of some experiments show that the generic rules proposed in this paper, which are structured on the ground of abstract propositions, have enough ability to respond successfully to the issue of interpretation in a social network with the characteristics already mentioned. Also, these results approve the fact that such an abstraction is able enough to handle the possible facts hidden in the contents showing up in social network.