Bibliometrics are important measures for venue quality in digital libraries. Impacts of venues are usually the major consideration for subscription decision-making, and for ranking and recommending high-quality venues and documents. For digital libraries in the Computer Science literature domain, conferences play a major role as an important publication and dissemination outlet. However, with a recent profusion of conferences and rapidly expanding fields, it is increasingly challenging for researchers and librarians to assess the quality of conferences. We propose a set of novel heuristics to automatically discover prestigious (and lowquality) conferences by mining the characteristics of Program Committee members. We examine the proposed cues both in isolation and combination under a classification scheme. Evaluation on a collection of 2,979 conferences and 16,147 PC members shows that our heuristics, when combined, correctly classify about 92% of the conferences, with a low false positive rate of 0.035 and a recall of more than 73% for identifying reputable conferences. Furthermore, we demonstrate empirically that our heuristics can also effectively detect a set of low-quality conferences, with a false positive rate of merely 0.002. We also report our experience of detecting two previously unknown low-quality conferences. Finally, we apply the proposed techniques to the entire quality spectrum by ranking conferences in the collection.
Zhuang et al. REF have identified a set of heuristics to automatically determine the quality of the conferences based on characteristics of the PC.
Measuring conference quality by mining program committee characteristics
JCDL '07
Computer Science