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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2505.19402 |
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| _version_ | 1866915305025961984 |
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| author | Peng, Tai-Quan Yang, Xuzhen |
| author_facet | Peng, Tai-Quan Yang, Xuzhen |
| contents | This paper examines how large language models (LLMs) are transforming core quantitative methods in communication research in particular, and in the social sciences more broadly-namely, content analysis, survey research, and experimental studies. Rather than replacing classical approaches, LLMs introduce new possibilities for coding and interpreting text, simulating dynamic respondents, and generating personalized and interactive stimuli. Drawing on recent interdisciplinary work, the paper highlights both the potential and limitations of LLMs as research tools, including issues of validity, bias, and interpretability. To situate these developments theoretically, the paper revisits Lasswell's foundational framework -- "Who says what, in which channel, to whom, with what effect?" -- and demonstrates how LLMs reconfigure message studies, audience analysis, and effects research by enabling interpretive variation, audience trajectory modeling, and counterfactual experimentation. Revisiting the metaphor of the methodological compass, the paper argues that classical research logics remain essential as the field integrates LLMs and generative AI. By treating LLMs not only as technical instruments but also as epistemic and cultural tools, the paper calls for thoughtful, rigorous, and imaginative use of LLMs in future communication and social science research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19402 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Recalibrating the Compass: Integrating Large Language Models into Classical Research Methods Peng, Tai-Quan Yang, Xuzhen Artificial Intelligence Computers and Society This paper examines how large language models (LLMs) are transforming core quantitative methods in communication research in particular, and in the social sciences more broadly-namely, content analysis, survey research, and experimental studies. Rather than replacing classical approaches, LLMs introduce new possibilities for coding and interpreting text, simulating dynamic respondents, and generating personalized and interactive stimuli. Drawing on recent interdisciplinary work, the paper highlights both the potential and limitations of LLMs as research tools, including issues of validity, bias, and interpretability. To situate these developments theoretically, the paper revisits Lasswell's foundational framework -- "Who says what, in which channel, to whom, with what effect?" -- and demonstrates how LLMs reconfigure message studies, audience analysis, and effects research by enabling interpretive variation, audience trajectory modeling, and counterfactual experimentation. Revisiting the metaphor of the methodological compass, the paper argues that classical research logics remain essential as the field integrates LLMs and generative AI. By treating LLMs not only as technical instruments but also as epistemic and cultural tools, the paper calls for thoughtful, rigorous, and imaginative use of LLMs in future communication and social science research. |
| title | Recalibrating the Compass: Integrating Large Language Models into Classical Research Methods |
| topic | Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2505.19402 |