Using social annotations for trend discovery in scientific publications

• Main Authors: HU, Meiqun, LIM, Ee Peng, JIANG, Jing
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2011
• Subjects: Social annotations; Temporal profiles; Emerging trends; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/1461; https://ink.library.smu.edu.sg/context/sis_research/article/2460/viewcontent/hcir11p.pdf
• Institution: Singapore Management University

Social tags and citing documents are two forms of social annotations to scientific publications. These social annotations provide useful contextual and temporal information for the annotated work, which encapsulates the attention and interest of the annotators. In this work, we explore the use of social annotations for discovering trends in scientific publications. We propose a trend discovery process that employs trend estimation and trend selection and ranking for analyzing the emerging trends shown in the social annotation profiles. The proposed sigmoid trend estimator allows us to characterize and compare how much, when and how fast the trends emerge. To perform topic-specific trend analysis, we further adopt topic modeling on the annotation content to decapsulate the multitude of impact created by the annotated work.