Digital Humanities in Practice: Exploring Literature Through AI and Technology

This blog is written as a task assigned by the head of the Department of English (MKBU), Prof. and Dr. Dilip Barad Sir. Here is the link to the professor's blog for background reading: Click here.

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Source: DALL·E 3 - Representational

1. Understand how once we used to debate on if machines can write poems.

Can a computer write poetry? | Oscar Schwartz

2. Take a test - Was this poem written by a human or a computer?

 

Human vs Machine Poetry Test (Your Experience)

In this activity, I was asked to read a set of poems and decide whether they were written by a human poet or generated by a computer program. At first, I felt confident, because I thought human poems would always feel more emotional or natural.

However, as I progressed, I realized the task was not easy. Some machine-written poems had a smooth rhythm, while a few human-written ones were abstract and mechanical. I scored 6 out of 10, which shows how tricky it can be to judge purely by reading.

In fact, the last questions confused me the most. The lines sounded emotional, yet had an artificial repetition that made it hard to decide. This taught me that the line between machine creativity and human creativity is thinner than I expected.

CLiC Activity Book – Study Material

When I first started working with the CLiC Dickens Project, I felt a bit confused about the method. At the beginning, I wasn’t sure how to search or what the options like quotes or sentences really meant. But after exploring step by step, I understood how to use it properly.

For my group task, I worked on the character of Mrs. Sparsit in Hard Times.

I used the dialogue chart, which showed all the lines spoken by Mrs. Sparsit in the novel. By looking at this chart, I noticed how her speech is often sharp, formal, and sometimes sarcastic. This helped me see how Dickens gives her a very distinct voice.

Then, I tried the other function, where I clicked on her name in the search results. This brought up not only her direct speech but also how Dickens and other characters describe her. The tool also gave me options like sentences and quotes, which made it easier to study her role from different angles.

After this, I also looked at Oliver in Oliver Twist.

When I searched for “Oliver,” I could see both his dialogue and how often his name appears in the narration.

By clicking on quotes, I could study what Oliver actually says, which showed me his innocence and politeness.

The suspension option helped me see what verbs are used with his speech (like cried Oliver, pleaded Oliver), which reflects his emotional state in many scenes.

At first I struggled, but once I understood the method, I found CLiC very helpful because it gives me real evidence from the text instead of just impressions. It helped me understand how Dickens shapes his characters through both their own words and the way they are described.

Voyant - the activity will be explained in the lab

1. Cirrus

 

2.Links   

3. Dreamspace 

4. Phrases 

Learning Outcomes

• Gained practical experience using digital humanities tools like Voyant for literary analysis.

• Learned to visualize and track word frequency and thematic patterns across the text.

• Understood character prominence and the flow of recurring motifs using visualizations like StreamGraph and Cirrus.

• Explored relationships between words and ideas through tools like TermsBerry, Knotes, and DreamScape.

• Observed how Hardy emphasizes themes of social constraint, personal aspiration, and relationships throughout the novel.

• Learned to combine quantitative data (word counts, clusters) with qualitative interpretation for deeper textual insights.

• Appreciated the ability of digital tools to reveal patterns and structures not easily visible in traditional close reading.

References:

Barad, Dilip. “What If Machines Write Poems.” What If Machines Write Poems, 1 Jan. 1970, blog.dilipbarad.com/2017/03/what-if-machines-write-poems.html.