# Venue Analytics: A Simple Alternative to Citation-Based Metrics

## TL;DR

This paper introduces a venue scoring method based solely on publication data, enabling large-scale, transparent evaluation of venues, authors, and institutions over time without relying on citation metrics.

## Contribution

It presents a novel venue evaluation approach using regression on publication data, applicable across decades and scalable to large datasets, enhancing transparency and repeatability.

## Key findings

- Venue scores correlate with traditional impact measures
- Method enables temporal analysis of conference quality
- Applicable to large, open datasets for broad evaluation

## Abstract

We present a method for automatically organizing and evaluating the quality of different publishing venues in Computer Science. Since this method only requires paper publication data as its input, we can demonstrate our method on a large portion of the DBLP dataset, spanning 50 years, with millions of authors and thousands of publishing venues. By formulating venue authorship as a regression problem and targeting metrics of interest, we obtain venue scores for every conference and journal in our dataset. The obtained scores can also provide a per-year model of conference quality, showing how fields develop and change over time. Additionally, these venue scores can be used to evaluate individual academic authors and academic institutions. We show that using venue scores to evaluate both authors and institutions produces quantitative measures that are comparable to approaches using citations or peer assessment. In contrast to many other existing evaluation metrics, our use of large-scale, openly available data enables this approach to be repeatable and transparent. To help others build upon this work, all of our code and data is available at https://github.com/leonidk/venue_scores