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[2602.19177] Next Reply Prediction X Dataset: Linguistic Discrepancies in Naively Generated Content
Summary
The paper introduces the Next Reply Prediction X Dataset, addressing linguistic discrepancies in content generated by Large Language Models (LLMs) compared to human responses, emphasizing the need for improved methodologies in computational social science research.
Why It Matters
As LLMs become increasingly used in social science research, understanding their limitations is crucial. This study provides a framework to evaluate LLM-generated content against human-generated data, enhancing the validity of research findings and promoting better practices in the field.
Key Takeaways
- LLMs can produce significant linguistic discrepancies when naively applied in research.
- The study introduces a dataset for evaluating LLM outputs against authentic human responses.
- Improved prompting techniques are necessary for more accurate LLM-generated content.
- Quantitative metrics are provided for assessing the quality of synthetic data.
- The findings highlight the importance of specialized datasets in computational social science.
Computer Science > Computation and Language arXiv:2602.19177 (cs) [Submitted on 22 Feb 2026] Title:Next Reply Prediction X Dataset: Linguistic Discrepancies in Naively Generated Content Authors:Simon Münker, Nils Schwager, Kai Kugler, Michael Heseltine, Achim Rettinger View a PDF of the paper titled Next Reply Prediction X Dataset: Linguistic Discrepancies in Naively Generated Content, by Simon M\"unker and 4 other authors View PDF HTML (experimental) Abstract: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 ...