Teleoscope: Exploring Themes in Large Document Sets By Example
<|reference_start|>Teleoscope: Exploring Themes in Large Document Sets By Example: Qualitative thematic exploration of data by hand does not scale and researchers create and update a personalized point of view as they explore data. As a result, machine learning (ML) approaches that might help with exploration are challenging to apply. We developed Teleoscope, a web-based system that supports interactive exploration of large corpora (100K-1M) of short documents (1-3 paragraphs). Teleoscope provides visual programming workflows that have semantic and computational meaning; helping researchers to retrace, share, and recompute their sense-making process. Attempting to create qualitative "themes" rather than "topics," our NLP approach tunes an ML model to "think like you" without significant retraining. Here, we present our two-year design process and validation of Teleoscope, including a multi-week study with qualitative researchers (N = 5), a six-month field deployment with a qualitative research group, and an on-going public release.<|reference_end|>
@article{bucci2024crystallizing,
    title={Crystallizing Schemas with Teleoscope: Thematic Curation of Large Text
  Corpora},
    author={Paul Bucci, Leo Foord-Kelcey, Patrick Yung Kang Lee, Alamjeet Singh,
  Ivan Beschastnikh},
    journal={arXiv preprint arXiv:2402.06124},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06124},
    primaryClass={cs.HC}
}