Ranking users, papers and authors in online scientific communities
This addresses the challenge for researchers in managing scientific information overload in online communities, though it is incremental as it builds on existing ranking methods.
The paper tackled the problem of filtering scientific information in online communities by proposing a method to compute user reputation and artifact quality simultaneously, showing that including author credit improves performance, with top papers having higher citation counts and top authors having higher h-indexes compared to other algorithms.
The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, together with growing popularity of online scientific communities, calls for the development of effective information filtering tools. We propose here a method to simultaneously compute reputation of users and quality of scientific artifacts in an online scientific community. Evaluation on artificially-generated data and real data from the Econophysics Forum is used to determine the method's best-performing variants. We show that when the method is extended by considering author credit, its performance improves on multiple levels. In particular, top papers have higher citation count and top authors have higher $h$-index than top papers and top authors chosen by other algorithms.