The Core Issues and Latest Progress of Current Digital Humanities Research: An Interview with Ted Underwood

Lihui Feng, Ted Underwood

Research output: Contribution to journalArticlepeer-review


Ted Underwood, a professor both at the Department of English and the School of Information Sciences, University of Illinois, Urbana-Champaign, has always played a leading role in tackling the intensified conversation between the digital and the humanities over the past decade. Writing in large quantities on the issues of machine learning, digital library, text mining, and digital humanities, Underwood has long been committed to the interdisciplinary studies of literature. In his monograph Distant Horizons: Digital Evidence and Literary Change (2019), Underwood makes an exploration of how digital methods can help us describe and comprehend the larger arcs of literary change across longer time spans. During her visit to the University of Washington (2019-2020), Feng Lihui carried out an interview with Underwood on a wide spectrum of cutting-edge topics, including digital humanities, machine learning, and statistical models. When commenting on the significance of digital analysis, Underwood points out that digital analysis, with a bird's-eye view, can bring to light a broader landscape of literary history, thus drastically revolutionizing how we perceive literary history. Not only does he lay particular emphasis on the fact that data is a construction, but also he elaborates on the idea that quantitative analysis can also be critical in light of the latest progress of current digital humanities research. With regard to the future trends of digital humanities research, he declares that the problem that we need to reflect on and wrestle with at present is how to change the existing practice of data science to make it work for critique.

Original languageEnglish (US)
Pages (from-to)1-13
Number of pages13
JournalForeign Literature Studies
Issue number6
StatePublished - Dec 25 2021


  • Digital analysis
  • Digital humanities
  • Machine learning
  • Statistical model

ASJC Scopus subject areas

  • Literature and Literary Theory


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