Citation count prediction: learning to estimate future citations for literature

In most of the cases, scientists depend on previous literature which is relevant to their research fields for developing new ideas. However, it is not wise, nor possible, to track all existed publications because the volume of literature collection grows extremely fast. Therefore, researchers generally follow, or cite merely a small proportion of publications which they are interested in. For such a large collection, it is rather interesting to forecast which kind of literature is more likely to attract scientists’ response. In this paper, we use the citations as a measurement for the popularity among researchers and study the interesting problem of Citation Count Prediction (CCP) to examine the characteristics for popularity. Estimation of possible popularity is of great significance and is quite challenging. We have utilized several features of fundamental characteristics for those papers that are highly cited and have predicted the popularity degree of each literature in the fu...

• Type: Journal
• Year: 2011
• Where: CIKM
• Authors: Rui Yan, Jie Tang, Xiaobing Liu, Dongdong Shan, Xiaoming Li
• Keywords: CIKM 2011, Citation Count, Information Storage And Retrieval, Information Technology, Natural Language Processing