Citation Amnesia: On The Recency Bias of NLP and Other Academic Fields
It highlights a recency bias in academic citations, particularly in NLP, which could lead to neglecting relevant older research, though it is incremental as it quantifies an existing concern.
This study analyzed citation patterns across 20 academic fields over 43 years, finding that many fields, especially NLP and ML, show a decline in citing older works, termed 'citation age recession', with NLP experiencing a -12.8% drop from previous peaks.
This study examines the tendency to cite older work across 20 fields of study over 43 years (1980--2023). We put NLP's propensity to cite older work in the context of these 20 other fields to analyze whether NLP shows similar temporal citation patterns to these other fields over time or whether differences can be observed. Our analysis, based on a dataset of approximately 240 million papers, reveals a broader scientific trend: many fields have markedly declined in citing older works (e.g., psychology, computer science). We term this decline a 'citation age recession', analogous to how economists define periods of reduced economic activity. The trend is strongest in NLP and ML research (-12.8% and -5.5% in citation age from previous peaks). Our results suggest that citing more recent works is not directly driven by the growth in publication rates (-3.4% across fields; -5.2% in humanities; -5.5% in formal sciences) -- even when controlling for an increase in the volume of papers. Our findings raise questions about the scientific community's engagement with past literature, particularly for NLP, and the potential consequences of neglecting older but relevant research. The data and a demo showcasing our results are publicly available.