Next Reply Prediction X Dataset: Linguistic Discrepancies in Naively Generated Content
This addresses methodological risks for computational social science researchers using LLMs as human proxies, but it is incremental as it builds on existing concerns about synthetic data validity.
The paper tackled the problem of linguistic discrepancies in LLM-generated content used in social science research by creating a history-conditioned reply prediction dataset from X data to evaluate LLMs against human content, finding that naive prompting leads to significant discrepancies and highlighting the need for better techniques and datasets.
The increasing use of Large Language Models (LLMs) as proxies for human participants in social science research presents a promising, yet methodologically risky, paradigm shift. While LLMs offer scalability and cost-efficiency, their "naive" application, where they are prompted to generate content without explicit behavioral constraints, introduces significant linguistic discrepancies that challenge the validity of research findings. This paper addresses these limitations by introducing a novel, history-conditioned reply prediction task on authentic X (formerly Twitter) data, to create a dataset designed to evaluate the linguistic output of LLMs against human-generated content. We analyze these discrepancies using stylistic and content-based metrics, providing a quantitative framework for researchers to assess the quality and authenticity of synthetic data. Our findings highlight the need for more sophisticated prompting techniques and specialized datasets to ensure that LLM-generated content accurately reflects the complex linguistic patterns of human communication, thereby improving the validity of computational social science studies.