  Many academics use yearly publication numbers to quantify academic interest
for their research topic. While such visualisations are ubiquitous in grant
applications, manuscript introductions, and review articles, they fail to
account for the rapid growth in scientific publications. As a result, any
search term will likely show an increase in supposed "academic interest". One
proposed solution is to normalise yearly publication rates by field size, but
this is arduous and difficult. Here, we propose an simpler index that
normalises keywords of interest by a ubiquitous and innocuous keyword, such as
"banana". Alternatively, one could opt for field-specific keywords or
hierarchical structures (e.g. PubMed's Medical Subject Headings, MeSH) to
compute "interest market share". Using this approach, we uncovered plausible
trends in academic interest in examples from the medical literature. In
neuroimaging, we found that not the supplementary motor area (as was previously
claimed), but the prefrontal cortex is the most interesting part of the brain.
In cancer research, we found a contemporary preference for cancers with high
prevalence and clinical severity, and notable declines in interest for more
treatable or likely benign neoplasms. Finally, we found that interest in
respiratory viral infections spiked when strains showed potential for pandemic
involvement, with SARS-CoV-2 and the COVID-19 pandemic being the most extreme
example. In sum, the time is ripe for a quick and easy method to quantify
trends in academic interest for anecdotal purposes. We provide such a method,
along with software for researchers looking to implement it in their own
writing.
