ChatGPT’s citation approach may amplify the Matthew Effect in environmental science

ChatGPT (GPT) has become one of the most talked-about innovations in recent years, with over 100 million users worldwide. However, there is still limited knowledge about the sources of information GPT utilizes. As a result, we carried out a study focusing on the sources of information within the field of environmental science. Our study, available on the arXiv preprint server, aims to address the research question: “Does ChatGPT predominantly cite the most-cited publications in environmental science?”

In the study, researchers asked GPT to identify the ten most significant subdisciplines within the field of environmental science. They then asked it to compose a scientific review article on each subdiscipline, including 25 references. They proceeded to analyze these references, focusing on factors such as the number of citations, publication date, and the journal in which the work was published.

The findings indicate that GPT tends to cite highly-cited publications in environmental science, with a median citation count of 1184.5. It also exhibits a preference for older publications, with a median publication year of 2010, and predominantly refers to well-respected journals in the field, with Nature being the most cited journal by GPT. Interestingly, our findings suggest that GPT seems to exclusively rely on citation count data from Google Scholar for the works it cites, rather than utilizing citation information from other scientific databases such as Web of Science or Scopus.

The study suggests that Google Scholar citations play a significant role as a predictor for mentioning a study in GPT-generated content. This finding reinforces the dominance of Google Scholar among scientific databases and perpetuates the Matthew Effect in science, where the rich get richer in terms of citations. With many scholars already utilizing GPT for literature review purposes, we can anticipate further disparities and an expanding gap between lesser-cited and highly-cited publications.