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.

Nicolas Audinet de Pieuchon, Adel Daoud, Connor T. Jerzak, Moa Johansson, Richard Johansson. Can Large Language Models (or Humans) Disentangle Text?. Proceedings of the Sixth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS 2024): 57-67, 2024.
@article{pieuchon2024can,
  title={Can Large Language Models (or Humans) Disentangle Text?},
  author={Pieuchon, Nicolas Audinet de and Adel Daoud and Connor T. Jerzak and Moa Johansson and Richard Johansson},
  journal={Proceedings of the Sixth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS 2024)},
  year={2024},
  pages={57--67},
  publisher={}
}
[Overview][Data][Code]

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]

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]

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]

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