Lab Session: Digital Humanities

This Lab Activity was assigned by Dr. Dilip Barad.
You can read more about  here: Click here.

Human vs Machine Poetry Test: Reflection

Participating in the Human vs Machine Poetry Test was an intriguing and eye-opening experience. In this activity, I was asked to read a series of poems and determine whether each one was written by a human poet or generated by an AI program. Initially, I felt quite confident in my ability to distinguish between the two. I assumed that human poetry would naturally express deeper emotions, authentic feelings, and subtle nuances of experience qualities that I believed machines could not replicate.

However, as I engaged more deeply with the poems, my confidence began to fade. To my surprise, many machine-generated poems exhibited fluent rhythm, consistent imagery, and even emotional resonance. On the other hand, a few human-written poems appeared abstract, disjointed, and almost mechanical in tone. This blurred the distinction I had initially drawn between human creativity and artificial generation. My final score of 6 out of 10 reflected this difficulty it was far more challenging to judge the source of the poems than I had anticipated.

The most confusing moments came with the final set of poems. Some lines conveyed genuine emotion and lyrical depth but also contained repetitive patterns and slightly unnatural phrasing. This ambiguity made it nearly impossible to rely on intuition alone. I found myself questioning what truly defines authentic creativity is it emotional impact, originality, structure, or the intention behind the words?

Through this experiment, I realized that the boundary between human and machine creativity is becoming increasingly porous. AI has evolved to a level where it can imitate not only linguistic structures but also emotional tone and poetic sensibility. At the same time, this raises questions about the essence of art and authorship if machines can produce verses that move readers, then creativity might no longer be an exclusive human domain.

Ultimately, this activity reshaped my understanding of poetry and artistic production in the digital age. It revealed that creativity is not defined by the creator’s identity but by the reader’s experience. The test reminded me that both human poets and AI systems operate within patterns humans through cultural and emotional frameworks, and machines through data and algorithms. The thin line that separates them challenges us to rethink our assumptions about originality, emotion, and meaning in literature.

My Work on Voyant Tools: Reflection

After completing the Human vs Machine Poetry Test, I continued exploring the world of digital literature through Voyant Tools, a powerful online text analysis platform. I uploaded a small text containing the words Revolution 2020, Frankenstein, and 1984  three significant literary works that deal with social, scientific, and political anxieties in different ways.

Once I uploaded the text, Voyant Tools immediately generated several interactive visualizations, such as the Cirrus (word cloud), Trends graph, Summary panel, and Context tool. These visualizations transformed my understanding of the text from linear reading to a data-driven interpretation.

The Cirrus word cloud displayed the most frequent words in larger fonts, allowing me to instantly identify the dominant themes and ideas. For example, terms like power, control, and science appeared prominently, showing how each text revolves around human ambition and its consequences. The Trends graph illustrated how these words fluctuated across different parts of the text, reflecting the narrative emphasis on conflict and resistance.

Through Voyant, I realized that even a small textual dataset can reveal hidden patterns that might be overlooked during traditional reading. The Context tool helped me observe how key terms appeared alongside other words, deepening my understanding of thematic relationshipsfor instance, how Frankenstein often occurred with creation and responsibility, or how 1984 was frequently linked with surveillance and truth.

Overall, this experiment demonstrated how digital humanities tools like Voyant can complement literary interpretation. Instead of replacing close reading, they enhance it by offering new perspectives on frequency, association, and emphasis. Using Voyant Tools, I could “see” literature as a network of interconnected ideas, bridging traditional analysis with the dynamic possibilities of hypertextual learning.

It showed how often each word appeared and their distribution. Words like revolution and 2020 stood out in the graph.

 A colorful cloud highlighted keywords like Frankenstein and 1984. This gave me a quick way to identify the main themes.

This activity showed fascinating connections between words, such as revolution linking with 2020 and Frankenstein, revealing how ideas of transformation, creation, and power intersect across different narratives. It felt like uncovering hidden links within a story  patterns that might not appear through simple reading.

These visual representations demonstrated how digital tools can transform plain text into interactive maps of meaning, where language becomes data and interpretation becomes exploration. By turning literary analysis into something visual, dynamic, and engaging, Voyant Tools illustrated the true potential of digital humanities bridging creativity, technology, and critical thinking in the study of literature.

CLiC Activity Book – Study Material

When I first began working with the CLiC Dickens Project, I found the digital interface slightly confusing. I was unsure how to search for specific words or characters, and the options like quotes, sentences, and concordance lines seemed complicated. However, as I explored the tool step by step, I gradually understood its structure and purpose. CLiC allows readers to study literature not just through close reading but through data-driven insights showing patterns in word use, dialogue, and narrative voice.

For my group task, I focused on the character of Mrs. Sparsit in Hard Times. Using CLiC, I searched her mentions across the novel to analyze how Dickens constructs her image through recurring words and tone. The tool revealed that her name often appears alongside terms of observation and judgment, reflecting her controlling and critical personality. This digital method helped me see Mrs. Sparsit not just as a character but as a linguistic pattern shaped by Dickens’s narrative choices. Overall, the CLiC activity deepened my understanding of digital textual analysis, showing how technology can enrich traditional literary interpretation.

I used the dialogue chart feature, which displayed all the lines spoken by Mrs. Sparsit throughout Hard Times. By examining this chart, I noticed that her speech is often sharp, formal, and subtly sarcastic, revealing her pride and moral superiority. This helped me understand how Dickens gives her a distinct and consistent voice that reflects her social pretensions and judgmental nature.

Next, I explored another function by clicking on her name in the search results. This not only showed her direct speech but also the narrative descriptions and references made by Dickens and other characters. It provided multiple perspectives how she speaks, how she is perceived, and how the narrator frames her. The tool’s options like sentences and quotes made it easy to shift between contexts, allowing a deeper analysis of her role in the story. Overall, CLiC helped me study Mrs. Sparsit through a multi-layered digital lens, connecting textual detail with character interpretation in a way that traditional reading alone might overlook.

After this, I also explored the character of Oliver in Oliver Twist using CLiC. The dialogue chart showed his speech as simple, innocent, and sincere, reflecting his purity. By checking the keywords and context, I found he’s often described as poor, little, and innocent. This helped me see how Dickens builds sympathy for him. CLiC made it easier to notice such patterns of language and characterization in a visual, interactive way.

When I searched for “Oliver”, I could see both his dialogues and the frequency of his name in the narration. By clicking on quotes, I examined what Oliver actually says, which revealed his innocence and politeness. The suspension option helped me observe the verbs associated with his speech (such as cried Oliver or pleaded Oliver), reflecting his emotional tone in many scenes. Although I struggled at first, once I understood the method, I found CLiC extremely helpful because it provides concrete textual evidence instead of mere impressions. It allowed me to see how Dickens constructs character through both dialogue and narrative description.