| Popis: |
The growth of social networks is ever-increasing. Many available scientific publications evidence the interest of researchers in this area. Within a time span of eight years from 2011 to 2018, approximately 2600, 230, 150, and 110 scientific articles were published from the USA, Iran, Saudi Arabia, and Turkey, respectively around this area of research. To comprehensively survey all the sub-fields and interests within this research area, the present paper proposes a novel density-based method for finding topic descriptors from academic articles. By employing a robust to noise fuzzy clustering algorithm, the terms are clustered, and by utilizing a modified Parzen window, k topic descriptors from each cluster are extracted. Besides, an optimization problem has been designed to detect the similarity between word pairs. By conducting the experiments, the research priorities for four countries within this time span have been found. Moreover, the closeness of the research in developing countries to the developed country have been measured. The experimental results show that for four years, the research topics in Turkey were close to the research topics in the USA on average, and the research topics in Saudi Arabia were close to the USA topics during the past two years. Additionally, the experimental comparison of the proposed method with two clustering baselines indicates the superiority of the proposed method in terms of precision, recall, and accuracy. |
