# Measuring scientific buzz

## TL;DR

This paper analyzes the lifecycle and burstiness of keywords in AI literature, revealing how quickly new terms fade and identifying thematic trends over time using keyword data from a large corpus.

## Contribution

It provides the first large-scale analysis of keyword lifecycle and thematic bursts in AI, highlighting differences between journals and conferences.

## Key findings

- 80% of keywords fade within a year
- Keywords last longer in journals than conferences
- Identified two thematic bursts in AI research

## Abstract

Keywords are important for information retrieval. They are used to classify and sort papers. However, these terms can also be used to study trends within and across fields. We want to explore the lifecycle of new keywords. How often do new terms come into existence and how long till they fade out? In this paper, we present our preliminary analysis where we measure the burstiness of keywords within the field of AI. We examine 150k keywords in approximately 100k journal and conference papers. We find that nearly 80\% of the keywords die off before year one for both journals and conferences but that terms last longer in journals versus conferences. We also observe time periods of thematic bursts in AI -- one where the terms are more neuroscience inspired and one more oriented to computational optimization. This work shows promise of using author keywords to better understand dynamics of buzz within science.