Quantifying the structural and temporal characteristics of negative links in signed citation networks |
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| Institution: | 1. School of Management and Economics, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731 Chengdu, Sichuan, PR China;2. The Walker School of Business and Technology, Webster University, 470 E Lockwood Ave, Webster Groves, MO 63119, United States;3. The James F. Dicke College of Business Administration, Ohio Northern University, 525 S Main St, Ada, OH, United States;1. Department of Information Resources Management, Business School, Nankai University, Tianjin, China;2. Center for Network Society Governance, Nankai University, Tianjin, China;1. School of Journalism & Communication, South China University of Technology, China;2. School of Journalism & Communication, Jinan University, China;3. School of Computer Science and Engineering, South China University of Technology, China |
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| Abstract: | Although the citation relationships among papers can help in tracking and understanding the development of knowledge, few studies have noted that the content and sentiments of citations of a paper differ. Here, we use sentiment-labeled citation data to construct a directed signed citation network, in which an author may agree with or criticize the cited paper and these represent different ways of inheriting knowledge. The dataset we use consists of 9,038 papers in the field of Computational Linguistics, including 25,275 citations, with 20.8% positive citations, 8.6% negative citations and 70.6% neutral citations. We systematically quantify the structural patterns of negative citations, impact assortativity of involved papers, occurrence time distribution and consequences of receiving negative attention. Remarkably, we find that papers with different impacts have a similar probability of receiving negative citations, and highly cited papers tend to give negative citations to low-impact papers around but avoid giving negative citations to high-impact papers. Our research also reveals the random occurrence rules and colocation patterns of negative citation distribution. In addition, we show that, in the short term, around 60% of multiple negative citations is positively related to the impact of the cited paper while more than 80% are negatively related to the impact in the long run. Our findings explain the pattern by which negative citations occur and deepen the understanding of negative citations. |
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| Keywords: | Negative citations Citation behavior Signed network Natural-language processing |
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