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Foundations & Applications of Humanities Analytics

Complexity Explorer · Santa Fe Institute · Spring 2023

Foundations & Applications of Humanities Analytics was a self-paced, nine-chapter course from Complexity Explorer at the Santa Fe Institute, led by David Kinney and Simon DeDeo. Its lectures, quizzes, assignments, and guest lectures introduced ways computational text analysis can help us explore human culture and experience.

What interested me

I was especially interested in the possibility of discovering knowledge that is difficult to see by reading one document at a time. The course showed how textual signals, correlations, co-occurrence, similarity, novelty, resonance, and change over time can make patterns across a large collection visible. These techniques do not replace interpretation; they create new evidence to interpret and new questions to ask.

Case studies and process from Lesson 1

Lesson 1 made the approach concrete through three case studies. Analysis of Old Bailey trial transcripts tracked how the language of violent and nonviolent cases became more distinguishable over time. A study of Russian diary-writing grouped the language surrounding references to diaries to examine how writers understood the form. Research into French Revolution debates measured how novel patterns of speech entered the Assembly, persisted, or disappeared.

The lesson also introduced an iterative humanities analytics process: begin with a meaningful question, define the cultural or social concept carefully, choose and inspect the available data, translate the concept into measurable signals, analyse the resulting patterns, and interpret them in context. Results can expose weaknesses in the data, definition, or measurement, sending the inquiry back to an earlier step.

Learning through the assignments

The assignments gave me opportunities to move between a humanities question and a form that data analysis could test. I examined how language around Russian diary-writing could be classified and compared over time, then considered how similar methods might be applied to Malaysian parliamentary records to investigate changing positions, political influence, and continuity among elected representatives.

In another exercise, I explored how creation myths might be operationalised for comparison across digitised stories. I also tested “corruption” and “reform” as signals in Malaysian newspaper archives, asking whether political reform appeared as a response to corruption. That exercise made the limits of the method tangible: an archive or search index is never the same as the whole record, a chosen word is only a proxy for an idea, and the same word can carry different meanings in different contexts.

From analysis to this website

I am not treating this website as a formal humanities research dataset, but the course influences how I am constructing it. Each card is a small record; each authored connection states why two parts of my practice belong in relation. The site’s connectome lets a visitor move across those relationships and notice larger patterns that a conventional, linear résumé can hide.

The same learning informs my interest in AI-augmented knowledge environments. Retrieval and analysis can surface connections across documents, experiences, and ideas, but people still need to question the source material, understand what the available data leaves out, and remain responsible for interpretation. That balance—using computational methods to discover possibilities without surrendering judgement—is what continues to interest me.

Related page

Related page

  • AI-augmented knowledge environments — This inquiry extends the course’s interest in computational analysis into tools for retrieving, connecting, and interpreting knowledge with human judgement.
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