# Ranking users, papers and authors in online scientific communities

arXiv: https://arxiv.org/abs/1311.3064 · HTML: https://mlsanity.com/paper/1311.3064/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2013.

## Built on by (most cited first, 2 of 2)

- [Application of Machine Learning for Online Reputation Systems](https://mlsanity.com/paper/2209.04650/) (2022)
- [Mitigating the Participation Bias by Balancing Extreme Ratings](https://mlsanity.com/paper/2502.03737/) (2025)

## Abstract (arXiv)

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.