My First Steps into Digital Humanities: A Personal Lab Reflection
Welcome back to my academic blog! Today, I want to take you through my very first lab session in our Digital Humanities module. As an MA English student here at MKBU, I am very used to holding physical books, annotating margins, and doing deep, qualitative close readings. So, when Dr. Dilip Barad introduced us to this lab session, I knew I was stepping out of my comfort zone and into a completely new, computational way of interacting with literature. Here is the Mind Map : Click Here Here is the detailed infograph of this blog:
Here is exactly what I did during the lab, the tools I experimented with, and my honest reflections on the whole process.
1. Taking the "Bot or Not" Poetry Challenge
To put this to the test, I took an interactive poetry quiz by NPR. The goal was to see if I could tell the difference between verses written by human poets and those generated by an algorithm.
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| Trying to decipher if these lines were penned by a human or generated by code! |
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| My final results from the NPR Turing test for poetry. |
2. My Experience with CLiC: Distant Reading
Once I finished the poetry debate, I opened up the CLiC (Corpus Linguistics in Context) web app. Instead of reading a novel page by page, I used this tool to perform a "distant reading" scanning the entire text at once to find hidden patterns. I decided to run my searches on Arthur Conan Doyle’s The Sign of the Four.
- Searching for Gendered Context: I wanted to see how female characters are framed in the novel, so I searched for the terms "woman" and "women" across the whole book. Seeing the concordance lines stacked up on my screen instantly highlighted the specific adjectives and contexts Doyle used.
- Filtering for Dialogue: Next, I explored the theme of "justice." What I loved about CLiC is that I could restrict my search strictly to the "Quotes" subset. I typed in "justice" and applied the filter, which allowed me to separate what the characters were actually saying about justice from the narrator's voice.
- Getting the Hang of the Tool: I will be completely honest my initial understanding of the interface was a bit shaky! Staring at all those raw data lines and the KWICGrouper columns felt overwhelming at first. But as I clicked around and practiced applying tags to specific character lines, it clicked for me. I realized just how powerful this tool is for objective, data-driven character analysis.
3. Visualizing David Copperfield with Voyant Tools
For the final part of my lab activity, I transitioned to Voyant Tools. If CLiC is great for finding specific phrases, Voyant is amazing for turning massive blocks of text into visual art. I chose Charles Dickens's David Copperfield for this experiment.
The Initial Dashboard: When I uploaded the text, the system processed an astonishing 348,481 words in seconds! Looking at the Summary pane, I could immediately see the most frequent words: "said" (2893), "mr" (2280), "little" (872), "know" (734), and "aunt" (729). The Cirrus word cloud visualization gave me a striking, at-a-glance view of these dominant terms.
Exploring the Bubbles: I then clicked over to the Bubbles visualization tool. I highlighted the word "mother" in yellow to see its relationships. It was fascinating to see how it clustered closely with "aunt," "miss," and "peggotty." Looking at it visually proved just how central these female and maternal figures are to David's narrative.
Tracking Trends: The Trends line graph was probably my favorite part. By tracking the frequencies of words like "mr," "little," and "aunt" across different segments of the document, I could literally trace the narrative arc. For instance, watching the spikes for the word "aunt" perfectly matched up with Miss Betsey Trotwood's major appearances in the story.
Looking at Term Distribution: Finally, I generated a broader distribution graph. While it initially looked like a chaotic tangle of colored lines to me, taking a closer look helped me appreciate the sheer density of Dickens's vocabulary. It visually proved how much structural data is hiding just beneath the surface of the prose.
4. What My Group and I Learned
After I finished experimenting with the tools, I sat down with my group members to discuss our learning outcomes. We came to a few major collective realizations:
A Complementary Approach: We all agreed that tools like Voyant and CLiC don't replace our traditional literary criticism skills. I still need my understanding of narrative techniques! But now, I have quantitative data to actually back up my qualitative arguments.
Bridging the Tech Gap: Many of us admitted to feeling a bit lost during the initial technical setup. We realized that as literature students, we need to push past our hesitation with digital interfaces if we want to stay relevant in modern academic research.
Asking New Questions: Ultimately, I walked away from this session learning how to ask different kinds of questions. Instead of just asking what a text means, I now know how to ask how often and in what exact contexts those meanings are built.
This first lab session challenged me, but it was incredibly rewarding. I am so excited to see how I can apply these digital tools to my future coursework!
- Kruti Vyas
Here is the Slide Deck of this blog:
Here is the Video Overview of this blog:
References:
- Barad, Dilip. "What if Machines Write Poems." Dilip Barad | Teacher Blog, 21 Mar. 2017,
blog.dilipbarad.com/2017/03/what-if-machines-write-poems.html - Mahlberg, Michaela, et al. CLiC: Corpus Linguistics in Context. University of Birmingham, clic.bham.ac.uk. Accessed 3 Aug. 2026.
- Schwartz, Oscar. "Can a computer write poetry? | Oscar Schwartz." YouTube, uploaded by TED, 10 Feb. 2016, youtu.be/UpkAqPEcMyE?si=YUhKv1pXK9xl9sbh.
- Sinclair, Stéfan, and Geoffrey Rockwell. Voyant Tools. 2016, beta.voyant-tools.org. Accessed 3 Aug. 2026.
Distant Reading & Digital Tools
A personal journey of an MA English student stepping out of the margins of physical books and into the computational landscapes of Digital Humanities.
1. The Poetry Turing Test
The lab began with a challenge: Can a machine replicate the emotional weight of poetry? The experiment involved an NPR quiz designed to see if human readers could distinguish between algorithmically generated verses and those penned by human masters.
The "Robot" Reveal
"I went in thinking I would easily spot the 'robot' poems, but some machine-generated verses were hauntingly beautiful... my experience of 'literariness' is heavily dependent on the meaning I project onto the words."
The visualization represents the difficulty gap encountered during the test. As a qualitative reader, the realization that algorithms can trigger "human" emotional responses was the first major cognitive shift of the lab.
2. CLiC: Distant Reading Patterns
Shifting to Arthur Conan Doyle’s The Sign of the Four, we moved from page-by-page reading to "distant reading"—scanning the entire text for systemic patterns in character framing and thematic language.
Gender Term Frequencies
Searching for "woman" vs "women" revealed how female characters are framed. The concordance lines highlighted specific adjectives that define Doyle's gendered context.
Thematic Context: Justice
By filtering the "Quotes" subset in CLiC, we separated dialogue from narration to see how characters talk about "Justice" compared to the narrator's voice.
3. Voyant Tools: Dickens by the Numbers
If CLiC is a magnifying glass, Voyant is a satellite view. Analyzing Charles Dickens's David Copperfield, the tool processed 348,481 words in seconds to reveal structural data hidden beneath the prose.
Term Distribution Trends Across Narrative Arc
The Trends line graph traces the narrative. Spikes in the word "aunt" directly correlate with Miss Betsey Trotwood's appearances, allowing us to see the plot structure through word frequency.
By highlighting clusters like "mother," "aunt," and "miss," we visually confirmed the central role of maternal figures in David's life.
Group Learning Outcomes
The Hybrid Approach
Digital tools do not replace literary criticism; they provide quantitative backing for qualitative arguments.
Bridging the Gap
Literature students must push past technical hesitation to stay relevant in modern academic research paradigms.
New Inquiries
We learned to ask not just "what" a text means, but "how" that meaning is built through frequency and context.
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