Computational Text Analysis

Overview: These papers combine principles from statistics, computer science, computational linguistics, and network science, focusing on the integration of large-scale open-source datasets into language-based research tasks, on approaches to addressing complexities in text-based causal inference, and on the refinement of automated content analysis techniques.

Brian Libgober, Connor T. Jerzak. Linking Datasets on Organizations Using Half-a-Billion Open-Collaborated Records. ArXiv Preprint, 2023.
@article{libgober2023linking,
  title={Linking Datasets on Organizations Using Half-a-Billion Open-Collaborated Records},
  author={Libgober, Brian and Connor T. Jerzak},
  journal={ArXiv Preprint},
  year={2023},
  pages={},
  publisher={}
}
[Overview][Data][Code]

Adel Daoud, Connor T. Jerzak, Richard Johansson. Conceptualizing Treatment Leakage in Text-based Causal Inference. Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL): 5638-5645, 2022.
@article{daoud2022conceptualizing,
  title={Conceptualizing Treatment Leakage in Text-based Causal Inference},
  author={Daoud, Adel and Connor T. Jerzak and Richard Johansson},
  journal={Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)},
  year={2022},
  volume={},
  pages={5638--5645},
  publisher={Association for Computational Linguistics},
  doi={10.18653/v1/2022.naacl-main.413}
}
[Overview]

Connor T. Jerzak, Gary King, Anton Strezhnev. An Improved Method of Automated Nonparametric Content Analysis for Social Science. Political Analysis, 31(1): 42-58, 2023.
@article{jerzak2023improved,
  title={An Improved Method of Automated Nonparametric Content Analysis for Social Science},
  author={Jerzak, Connor T. and Gary King and Anton Strezhnev},
  journal={Political Analysis},
  year={2023},
  volume={31},
  number={1},
  pages={42-58},
  publisher={}
}
[Overview][Data][Code]

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