Developing a Model for Estimating the Extraversion Degree of Social Network Members Using the Information Extracted from the Graph Structure

Having knowledge about the personality of social network members can improve the social network services. This knowledge also can be applied to improve the interactions of social network members. The personality characteristics of social network members can be estimated via personality questionnaires. However, usually people are not interested in filling these questionnaires because it may violate their privacy. So, their personality characteristics should be estimated implicitly. In previous researches some methods have been presented to estimate the personality of social network members implicitly. However, these methods require the users’ profile and contextual information that is not accessible in most of the cases. In this paper, a model is presented which can estimate the extraversion degree of social network members implicitly using information extracted from the graph structure around each member. To develop this model, first, a dataset of social network members are collected. Then, by applying genetic programming and M5 regression on this dataset, some relations are extracted to estimate the extraversion degree of each member. The results of our model show high accuracy. In addition, the model extracted by genetic programming has higher accuracy and lower computational complexity compared to M5 regression.