Freshness Decay in Academic References: Measuring Citation Shelf Life Across AI Research Domains

Key Points

• This research aims to quantify how quickly academic references lose relevance over time in AI domains.
• Analyzed citation patterns across various AI research publications
• Utilized bibliometric techniques to measure reference decay
• Categorized findings by different AI subfields
• Found significant variance in citation decay rates across AI domains
• Older references show a marked decrease in citation frequency
• Certain AI subfields maintain higher citation relevance over time