Don't Trust Generative Agents to Mimic Communication on Social Networks Unless You Benchmarked their Empirical Realism (2026 · 4 citations)
LLM-driven agents are increasingly used as stand-ins for people in computational social science. However, studies disagree on whether they reproduce human behaviour faithfully. The authors examine this for social networks, where simulation could reveal how platform design choices such as ranking or filtering affect…
Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction (2026 · 1 citation)
The paper asks whether LLMs used as stand-ins for real people in social science can reproduce how specific individuals behave online, rather than just producing comments that look plausible.
Next Reply Prediction X (NRP-X) Dataset: Linguistic Discrepancies in Naively Generated Content (2026)
Social scientists increasingly use LLMs as stand-ins for human participants. When models are simply prompted without behavioral constraints, their writing may differ from real users' in ways that undermine research validity. The authors build NRP-X, a resource for testing this on authentic X (formerly Twitter) data…

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Updated 1 November 2026