Peter Broadwell is a Digital Scholarship Research Developer at the Center for Interdisciplinary Digital Research. His work applies machine learning, web-based visualization, and other methods of digital analysis to complex cultural data. Recent studies in which he has participated have involved automatic translation and indexing of folklore collections in multiple languages, deep learning-based analysis of dance choreography from video sources, and multimedia annotation of Japanese Noh theater performances.

Current Role at Stanford

Digital Scholarship Research Developer, Center for Interdisciplinary Digital Research, Stanford University Libraries

Honors & Awards

  • Jöran Sahlgren Prize, Kungl. Gustav Adolfs Akademien för Svensk Folkkultur (2022)
  • Best Conference Paper, Joint Council on Digital Libraries (2016)
  • Mellon Dissertation Fellowship in the Humanities in Original Sources, Council on Library and Information Resources (2007-2008)

Education & Certifications

  • Ph.D., University of California, Los Angeles, Musicology (2010)
  • M.A., University of California, Los Angeles, Musicology (2006)
  • M.S., University of California, Berkeley, Computer Science (2004)
  • B.A., Grinnell College, Computer Science (2001)

All Publications

  • Disentangling the Folklore Hairball A Network Approach to the Characterization of a Large Folktale Corpus FABULA Abello, J., Broadwell, P. M., Tangherlini, T. R., Zhang, H. 2023; 64 (1-2): 64-91
  • Comparing Themes Extracted via Topic Modeling and Manual Content Analysis: Korean-Language Discussions of Dementia on Twitter. Studies in health technology and informatics Lee, H., Jang, S. J., Sun, F. F., Broadwell, P., Yoon, S. 2022; 295: 230-233


    We randomly examined Korean-language Tweets mentioning dementia/Alzheimer's disease (n= 12,413) posted from November 28 to December 9, 2020, without limiting geographical locations. We independently applied Latent Dirichlet Allocation (LDA) topic modeling and qualitative content analysis to the texts of the Tweets. We compared the themes extracted by LDA topic modeling to those identified via manual coding methods. A total of 16 themes were detected from manual coding, with inter-rater reliability (Cohen's kappa) of 0.842. The proportions of the most prominent themes were: burdens of family caregiving (48.50%), reports of wandering/missing family members with dementia (18.12%), stigma (13.64%), prevention strategies (5.07%), risk factors (4.91%), healthcare policy (3.26%), and elder abuse/safety issues (1.75%). Seven themes whose contents were similar to themes derived from manual coding were extracted from the LDA topic modeling results (perplexity: -6.39, coherence score: 0.45). Our findings suggest that applying LDA topic modeling can be fairly effective at extracting themes from Korean Twitter discussions, in a manner analogous to qualitative coding, to gain insights regarding caregiving for family members with dementia, and our approach can be applied to other languages.

    View details for DOI 10.3233/SHTI220704

    View details for PubMedID 35773850

  • Comparative K-Pop Choreography Analysis through Deep-Learning Pose Estimation across a Large Video Corpus DIGITAL HUMANITIES QUARTERLY Broadwell, P., Tangherlini, T. R. 2021; 15 (1)
  • Bridges, Sex Slaves, Tweets, and Guns A Multi-Domain Model of Conspiracy Theory FOLKLORE AND SOCIAL MEDIA Tangherlini, T. R., Roychowdhury, V., Broadwell, P. M., Peck, A., Blank, T. J. 2020: 39-66
  • Comparing published scientific journal articles to their pre-print versions INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES Klein, M., Broadwell, P., Farb, S. E., Grappone, T. 2019; 20 (4): 335-350
  • Reading the Quan Tang shi: Literary History, Topic Modeling, Divergence Measures DIGITAL HUMANITIES QUARTERLY Broadwell, P., Chen, J. W., Shepard, D. 2019; 13 (4)
  • SoLoGlo - A Service to Archive, Analyze, and Link Social, Local, and Global News Klein, M., Broadwell, P., Grappone, T., Farb, S., Soc Imaging Sci & Technol SOC IMAGING SCIENCE & TECHNOLOGY. 2015: 27-29
  • Semi-Supervised Morphosyntactic Classification of Old Icelandic PLOS ONE Urban, K., Tangherlini, T. R., Vijunas, A., Broadwell, P. M. 2014; 9 (7): e102366


    We present IceMorph, a semi-supervised morphosyntactic analyzer of Old Icelandic. In addition to machine-read corpora and dictionaries, it applies a small set of declension prototypes to map corpus words to dictionary entries. A web-based GUI allows expert users to modify and augment data through an online process. A machine learning module incorporates prototype data, edit-distance metrics, and expert feedback to continuously update part-of-speech and morphosyntactic classification. An advantage of the analyzer is its ability to achieve competitive classification accuracy with minimum training data.

    View details for DOI 10.1371/journal.pone.0102366

    View details for Web of Science ID 000341306600070

    View details for PubMedID 25029462

    View details for PubMedCentralID PMC4100772

  • A searchable meta-graph can connect even troublesome house elves and other supernatural beings to scholarly folk categories. COMMUNICATIONS OF THE ACM Abello, J., Broadwell, P., Tangherlini, T. R. 2012; 55 (7): 60-70