摘要
生成式人工智能正深刻变革着计算社会科学领域,为数据收集与分析的多模态处理开辟了新的可能。 这一技术突破对缺乏深厚编程背景的研究者而言影响重大。 其一,生成式人工智能可通过自动化代码生成、注释和调试,大幅提高社会科学研究者的工作效率;其二,借助创新的提示工程,研究人员能够深入开展复杂的数据分析;其三,计算社会科学的教学领域也能受益于生成式人工智能工具,尤其是在代码注释和复杂代码解释方面,从而简化了学习过程,使计算社会科学技术更易理解和接触。
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参考文献
- Bail, C. 2024, “Can Generative AI Improve Social Science?” Proceedings of the National Academy of Sciences 121(21): e2314021121.
- Burke, M. et al. 2021, “Using Satellite Imagery to Understand and Promote Sustainable Development. ” Science 371(6535): eabe8628.
- Davidson, T. 2024, “ Start Generating: Harnessing Generative Artificial Intelligence for Sociological Research. ” Socius 10: 23780231241259651.
- Edelmann, A. et al. 2020, “Computational Social Science and Sociology. ” Annual Review of Sociology 46 (1): 61-81.
- Fan, X. & Y. Zhang 2022, “‘Just a Virus’ or Politicized Virus? Global Media Reporting of China on COVID-19. ” Chinese Sociological Review 1: 1-28.
- Gilardi, F. et al. 2023, “ChatGPT Outperforms Crowd Workers for Text-Annotation Tasks. ” Proceedings of the National Academy of Sciences 120(30): e2305016120.
- Jean, N. et al. 2016, “Combining Satellite Imagery. ” Science 353(6301): 790-794.
- Lazer, D. et al. 2009, “Computational Social Science. ” Science 323(5915): 721-723.
- Lazer, D. et al. 2020, “Computational Social Science: Obstacles and Opportunities. ” Science 369(6507): 1060-1662.
- Ledford, H. 2020, “How Facebook, Twitter and Other Data Troves Are Revolutionizing Social Science. ” Nature 582(7812): 328-330.
- Liu, Z. et al. 2021, “Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. ” arXiv: 2103. 14030 [Cs].
- Maher, T. et al. 2024, “The Racial Limits of Disruption: How Race and Tactics Influence Social Movement Organization Testimony before Congress, 1960-1995. ” Social Forces 073.
- Ollion, É. et al. 2024, “The Dangers of Using Proprietary LLMs for Research. ” Nature Machine Intelligence 6 (1): 4-5.
- Seguin, C. et al. 2023, “ A Seat at the Table: A New Data Set of Social Movement Organization Representation Before Congress During the Twentieth Century. ” Socius 9: 23780231221144598.
- Wang, B. et al. 2023, “Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters. ”
- Wankmüller, S. 2022, “Introduction to Neural Transfer Learning With Transformers for Social Science Text Analysis. ” Sociological Methods & Research 00491241221134527.
- Won, D. et al. 2017, “Protest Activity Detection and Perceived Violence Estimation from Social Media Images. ” doi:10. 1145/3123266. 3123282.
- Wu, X. et al. 2024, Computational Social Science: Application in China Studies. [S. l. ]: Taylor & Francis.
- Yao, S. et al. 2023, “Tree of Thoughts: Deliberate Problem Solving with Large Language Models. ”
- Zhang, Y. et al. 2023, “Sinophobia Was Popular in Chinese Language Communities on Twitter During the Early COVID-19 Pandemic. ” Humanities and Social Sciences Communications 10(1): 1-12.