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Some of today’s most popular large language model (LLM)-powered chatbots occasionally reference Kremlin-linked disinformation websites, but it might not be for the reasons many fear. While some recent studies have claimed that Russian actors are “grooming” LLMs by flooding the web with disinformation, our small-scale analysis finds little evidence for this.

We examine potential bias in Facebook’s 10-trillion cell URLs dataset, consisting of URLs shared on its platform and their engagement metrics. Despite the unprecedented size of the dataset, it was altered to protect user privacy in two ways: 1) by adding differentially private noise to engagement counts, and 2) by censoring the data with a 100-public-share threshold for a URL’s inclusion.

Over the last decade, the rapid proliferation of social media platforms coupled with the advancement of computational methods for collecting, processing, and analyzing big datasets created new opportunities for social science. But alongside new insights about the behaviors of individuals and groups, these practices raise new questions regarding what constitutes ethical research.