Tag Archives: praxis

Exploring Global Music Trends with Tableau: 2nd Praxis Blogpost.

As a graduate student taking a digital humanities course, I explored the dataset titled Spotify – Top Songs by Country Charts from Kaggle, containing the top 50 songs for each country on Spotify as of May 2020 with columns such as Country, Continent, Rank, Title, Artists, Album, Explicit, and Duration, this data offers a snapshot of global music preferences, highlighting how tastes vary across regions and continents. This dataset, spanning 29 European countries, 11 Asian countries, 1 African country, 10 North American countries, 9 South American countries, and 1 country in Australia, provided a rich ground for examining global music trends through Tableau Public. My analysis focused on three key visualizations: Top Songs by Country and Rank, Top Artist by Country, and Top Artist/Song by Map.

Top Songs by Country and Rank

In the first worksheet, I created a heat map to visualize the distribution of song ranks by country. Each cell represented a rank (1–50) for a specific country, with the color intensity corresponding to the duration of the song. This visualization revealed intriguing regional preferences: European countries, for instance, displayed a broader range of top-ranked songs compared to the Americas, where songs like Safaera by Bad Bunny, Jowell & Randy, and Nengo Flow dominated other North & South American charts in May. At the same time, Rockstar by Dababy & Roddy Ricch topped the chart in the US and Canada. The heat map provided an intuitive way to compare how different countries favored certain ranks and song durations.

Top Artist by Country

The second worksheet highlighted the most prominent artists in each country using a Crosstab (text table). By filtering the dataset by Rank and Country, I discovered that local artists heavily influenced charts in Europe and South America, while North America and Asia leaned toward global superstars. This visualization emphasized the intersection of cultural identity and music consumption, illustrating how countries’ diversity shapes global trends.

Top Artist/Song by Map

The third worksheet utilized a Mapbox to represent the average rank of songs by country. This visualization underscored the geographic spread of Spotify’s influence, with darker colors indicating higher-ranked songs. Filtering by Continent, Country, and Rank provided further insights—for example, South Africa showed a strong love for TheWeeknd in artist popularity. While Europe’s charts reflected diverse tastes across its countries. I tried adding explicit content as a filter revealing how regions like South America and Europe were more likely to feature explicit songs in their top charts.

Reflections

Having used Tableau before but not consistently with the tool, I can say it was both rewarding and instructive. Its interactive features allowed me to craft dynamic visualizations that conveyed the global reach and regional nuances of Spotify’s top charts. However, challenges like cleaning data (e.g., converting song durations into total seconds) and balancing interactivity with clarity highlighted the importance of thoughtful design in this digital humanities project.

This project aligns with our readings on the storytelling power of data visualization, showing how tools like Tableau can illuminate cultural and social patterns. By mapping music’s global influence, I gained a deeper appreciation for the intersection of technology, data, and human expression. This praxis not only improved my technical skills but also underscored the potential of digital tools to explore and share complex cultural narratives.

Kelechi Iwuagwu (Data Analytics & Viz, CUNY Grad Center)

Text Mining Praxis: Gender and As You Like It. . . Or Maybe Not Quite

I entered this Praxis assignment with a goal: measure pronoun usage in at least one Shakespearean play. Why pronouns? I was curious to see how often characters reflected on each other using gendered pronouns. Furthermore, in my research, I found that pronouns were often used as stop words (filler words that are removed before mining – think “the”, “an”, “a”, etc), which made me curious about how much these overlooked words could tell. Why Shakespeare? His plays are easy to find as public domain text files, the plots are well known, several have interesting gender dynamics, and. . . I was in a Shakespeare company in my undergrad years. I figured for new tools I may as well retread familiar territory.

I settled on As You Like It as this play particularly plays with gender – an exiled daughter of a duke disguises herself as a man who then offers to play the role of a woman (herself, no less) to help her love interest cope with his inability to see her. It makes more sense in context. With a woman as the main character, and one whose gender presentation changes throughout the play, I felt that As You Like It would serve as a good test case.

Next, came which software to use. I wound up trying a few, each of which will receive its own section:

Voyant

I started with Voyant, which was definitely the easiest to use. I copied and pasted the link from MIT’s webpage of the script (https://shakespeare.mit.edu/asyoulikeit/full.html) and clicked Reveal.

Immediately, I saw this Cirrus map of the most common words:

The script has 22,817 total words and 3,267 unique word forms according to Voyant with the top five words being Rosalind (the main character), Orlando (her love interest), Celia (Rosalind’s cousin / best friend), love, and good. I scrolled through the most popular words, trying to select both pronouns and gendered terms, but realized that this would be far too manual to be efficient. I then tried searching for pronouns before realizing that this was essentially using the Find function on the original webpage, which also didn’t seem like the best use of the tool. Interestingly, the context tab does show what precedes and follows the term. Unfortunately, the terms only show up when you use the regex term ending in an asterisk, so you do have to filter through words that start with the pronoun (i.e. she -> shepherd). It’s fascinating and provides some additional information, but doesn’t always refer to whom the pronoun is referring. Overall it was a fascinating starting point, but didn’t really answer my question

Google Ngram (Aside)

I pulled up Google Ngram to see if it would help. Considering that it shows how often words appear in a large selection of text, it did not help with my original question. I did want to share however, that out of curiosity, I searched for the term “themself” (single reflexive form of they) as proof of it being a legitimate pronoun / using singular “they” has historical validation. The result? From 1800 – 2022, we see a spike for “themself” usage in the most recent years, which seems to affirm the narrative that singular “they” is a more recent trend.

However, if you were to expand the search to texts from 1500 – 2022, the modern usage pales in comparison to its usage throughout the 16th century. Did this answer my question either? No, but I did find this to be an informative aside.

{L}exos

The last software that I tried was {L}exos, a data cleaning and analysis software from Wheaton College. My original MIT link resulted in unwanted HTML that I couldn’t scrub, so I downloaded a text file of the script from Project Gutenberg and trimmed the front matter (title, Dramatis Personae, and scene list) and end matter (terms of use). Then, I went to the scrub function and tried to: make all text lowercase to avoid case sensitivity, remove digits, and remove punctuation but keep apostrophes. I had to replace the roman numerals for Act and Scene numbers with digits so that I wouldn’t get an improper count of “I”‘s in the text. I attempted to remove all words but pronouns and character names, but the result wasn’t as revealing as I’d hoped. Fascinating, but not revealing. Then, I took out a number of the stop words (i.e. the, an, a, that, enter, exit) to attempt to get a more comprehensive look at word frequency, resulting in this word cloud:

From this cloud, we can see that the first and second person are the most popular, but I still wanted more information. So, I went into the Content Analysis tab and entered text file dictionaries for each pronoun type I was looking for: first person, second person, third person masculine, third person feminine, they pronouns, and themself (out of curiosity). For second person, I included both forms of you and thou. I then selected all of the dictionaries and hit analyze. {L}exos generated a table of how many times each pronoun type occurred – first was first person, then second, then third person masculine, then third person feminine, then they pronouns (sadly, no record of themself).

Interestingly, in As You Like It, the most popular feminine pronoun is “her”, which was used 81 times, as opposed to the most popular pronouns for first person – I: 503 times, second person – you: 422 times, third person masculine – he: 180 times, and they – they: 48 times. “She” occurred in the text 46 times.

Conclusion

Did I (eventually) answer my original question? Yes, thanks to {L}exos, I was eventually able to find out the exact number of times pronouns appeared in As You Like It. Am I surprised that the amount of feminine pronouns was significantly less than first person, second person, or third person masculine? Not entirely – since a play involves characters either monologuing their inner thoughts or speaking with each other, I’m not surprised that first and second person pronouns had such high representation. The fact that the masculine pronouns occurred at roughly three times the amount of feminine pronouns was surprising – though the characters are mostly men, the lead is a woman, and there is quite a lot of pining at the center of this plot. The fact that the most popular feminine pronoun was the objective her rather than the nominative she was in fact surprising.

However, while I was able to find some information, my next step is to dig deeper into whom the pronouns were referring. Is it neat to know how often different pronouns occur and compare them? Sure. Is there a lot of context missing that would make this study even richer? Absolutely. In the future, I’d like to do a web scrape of the script to find who is speaking when each pronoun occurs and at what time so I can go back into the text and analyze who is being referred to. Is Rosalind (/ Ganymede, her male alter ego) referred to more with feminine pronouns or masculine pronouns? How often are each character referred to by pronouns? Do any characters have any interesting balance of pronoun types (i.e. does Jaques have more objective pronouns used toward him than nominative)? I had originally selected As You Like It for its gender subversion. While I answered my literal original question, I feel somewhat unsatisfied in the lack of answer toward the spirit behind my original question.

Praxis: Text Mining

Wow! Voyant is so powerful and accessible. Personally, I am really curious about what goes into building a tool like this.

The thing I enjoyed the most about exploring the text mining resources was actually looking through the Library of Congress’ Chronicling America database. How rich! I think it would be really interesting to try to look through this specifically for Black newspapers throughout the country over time.

I had some “fun” looking at mentions of Palestine pre-1948 in Nebraska newspapers:

Omaha daily bee. (Omaha [Neb.]), 08 Jan. 1911. Chronicling America: Historic American Newspapers. Lib. of Congress.
Omaha daily bee. (Omaha [Neb.]), 08 Jan. 1911. Chronicling America: Historic American Newspapers. Lib. of Congress.

I played with entering some of these texts in Voyant as well as personal writing and cover letters. I found this tool easier to experiment with and reason about than the more at-large data vis tools, which makes sense because they are designed for a smaller subset of use cases.

As with our other praxis assignments, it’s hard for me to really get in the weeds and reason about the use of these tools without a defined problem or question to work through. I think so much of what is interesting about Digital Humanities are the lines of inquiry that open up to us when using these powerful tools, and I’m interested in better learning how to ask meaningful questions.

Text Analysis Praxis Assignment

I chose to experiment with Voyant and word tree for the text analysis praxis. I wanted to find a text that I was familiar with so that I could at least kind of understand what I was looking at. I went to Project Gutenberg to see if I could find a text that was available that I was familiar with. I was excited to find The Well of Loneliness by Radclyffe Hall. Project Gutenberg is an awesome resource that I enjoyed exploring and hope to continue to use in the future.

The first tool I used was Voyant. I liked the various charts that this tool produced and the ease of viewing/playing with the different visualizations.

Voyant – The Well of Loneliness

I didn’t think it was all that illuminating to see that “like”, “little”, and “said” were among the most used words. I was also unsure of how to read or manipulate the data in any meaningful way based on the initial output. But, once I played around a little bit things got more interesting. I decided that I would look at the main characters love interests throughout the novel and compare that to instances of of the word “longing”. I wanted to use the words “longing”, “longed”, “lonely”, but I could not figure out how to make them all into one category. I think if I had played around with the tool more I would have been able to figure that out. Based on the data “longing” was the word in this cluster used the most throughout the book, so I chose to use that word for my analysis.

Briefly, the novel follows Stephen, a lesbian in early 20th century England. Collins is her tutor (childhood), Angela a friend who she has a relationship with (adolescent/early 20s), and then Mary who is arguably the love of her life (adulthood). I think it’s interesting that Stephen’s feelings of longing are heightened when she is in a relationship. In the novel Stephen is obviously queer, she wears “mens” clothes, doesn’t marry, does traditionally masculine activites, etc. Whether it’s a symptom of the time or genuine attraction, Stephen dates feminine women who are often betrothed to men (Angela) or they face discrimination/a harder life, which prompts Stephen to push them away and into the arms of a man (Mary). I think you could extrapolate that these factors influence her feelings of longing.

The other tool I explored for this praxis was word tree. I really liked the interface and how the user interacted with the text. It was useful to have the full quote highlighted on the side of the page, which I think would be really useful for performing close readings of texts. This tool also seemed to capture the overall themes of the novel better than the Voyant analysis. As a fun little treat the results also read like poetry to me.

word tree – The Well of Loneliness

Blogpost: (PRAXIS) Text Analysis of the US Constitution using Voyant for the first time.

My Experience with Voyant.

Before resolving to use Voyant, I initially explored Google N-gram but found it “kinda” difficult to navigate for deeper insights. Voyant, on the other hand, felt much more user-friendly, especially with its collection of very helpful features. The Cirrus tool, which creates a word cloud, stood out immediately. It highlights the most frequent words in a corpus, offering a quick, visual snapshot of key terms. Another useful feature was Terms, which displays the frequency of terms across the document, making it easy to track word usage patterns.

Links, a network diagram tool, was particularly helpful for exploring how words co-occur, offering insight into relationships between key concepts. The Reader view displayed the full text, allowing me to highlight and analyze terms within the document directly. Additionally, TermsBerry, a playful bubble chart, allowed me to visualize word frequency and connections in an engaging manner.

Other features, such as Trends, as well as Context and Bubblelines, added even more depth to the analysis. Voyant also provides statistics such as word counts, vocabulary density, and readability scores, making it not only visually engaging but also a quantitative tool for text analysis. Its ability to generate instant visual feedback and downloadable outputs made it ideal for my praxis.

Analyzing the U.S. Constitution

First, as part of the mining, I searched on Google for a txt. file of The US Constitution, and was able to find THE
CONSTITUTION OF THE UNITED STATES OF AMERICA As Amended on www.govinfo.gov which I highlighted all, copied, and pasted into the Voyant reader for analysis.

Using Voyant to analyze the U.S. Constitution was an interesting experience. The corpus was a single document, containing 39,243 words and 1,896 unique word forms. Voyant’s summary statistics revealed key insights, such as a vocabulary density of 0.048, indicating high repetition in language, and a readability index of 10.001, suggesting that the text is accessible to a broad audience.

From the Cirrus tool, it was revealed that the most frequent words in the text were terms like “shall” (1,268 occurrences), “states” (592), “congress” (396), “state” (387), and “president” (370). These terms reflect the U.S. Constitution’s focus on governance, authority, and the distribution of power.

Voyant Cirrus for analysis of The US Constitution

The Links tool allowed me to explore how these terms are connected. For example, it was interesting to see how frequently “states” and “congress” appeared together, highlighting their relationship in the text.

The Reader view allowed me to read the full document while tracking specific words, and TermsBerry provided an interactive visualization of word frequency, which made it easy to explore patterns and relationships between terms.

The Trends (which combines line and bar charts for term frequency over time)

In summary, Voyant offered a visually engaging, data-oriented approach to analyzing the U.S. Constitution, making the analysis both colorful, accessible, and insightful. As a prospective Digital Humanist, I will very likely be using it much more in the future.

Kelechi Iwuagwu – (A Data Analysis & Viz Candidate, CUNY Grad Center)

Text Analysis Praxis

I focused on Voyant Tools. Initially, I uploaded two different young adult books separately as the texts to analyze and I noticed most of the frequent words used for both were common words like “said”, “just”, “like” or a character’s name. I also noticed that some terms were either highlighted green or red but when I hovered over them it didn’t tell me what the highlighted colors meant, I assumed they were either positive or negative. For example, “dead” or similar iterations of the word, “monster”, “darkness” and “stupid” were highlighted in red. Other terms like “nice”, “hero”, “love” and “magic” were highlighted green.

When doing this assignment, I was curious about how often certain phrases or words were used because of running jokes between each book’s fandom. For Percy Jackson and the Lightning Thief, if I select the top phrases for Annabeth Chase, one of the main characters, I get “Annabeth yelled” which could be because her life and the lives of her friends are usually in danger.

Secondly, I used an article titled Race and Gender by Gebru. The most frequently used words were more varied than in the books, which made sense since the books are narrated in the first person. Gender was used 53 times, and AI was used 50 times. Some of the terms highlighted in red were “issues,” “inequality,” “harassment,” “errors,” and “racist.” Terms like “powerful,” “ethical,” “success,” and “fair” were highlighted green.

It was also interesting to see the results of comparing two documents. Distinctive words between the two get added to the summary section and the trends graph compares how frequently specific terms appear in each document. For example, “archives” and “archival” appear more frequently in Toward slow archives but have fewer appearances in Johnson Markup bodies. On the other hand, in Johnson Markup bodies terms like “black” and “digital” had a higher frequency rate than Toward slow archives.

In general, I think the text analysis provides more informative analyses for academic articles/texts. I did like the overall interface of the tool. There were a lot of features to explore; there was a summary section that included vocab density, readability index, average words per sentence, and most frequent words in the document. Another section called “Reader” featured the document that could be read in real-time and yet another section included a graph that displayed word frequency trends at document segments.

Text Analysis Praxis

For the text analysis praxis assignment, I mostly explored Voyant and the JSTOR text analyzer, Constellate. Voyant was a very self-explanatory and easy to use platform. You can either upload your text or paste it directly in the text box. Then, the tool creates multiple visualizations based on that text. My first step was to choose which text to dive into. I knew I wanted it to be a simple text. Additionally, I wanted to explore something personal to me, that might reveal something pertinent to my life. To this end, I copy and pasted my two personal statements from my grad school applications into Voyant. The statistics revealed that the words used most frequently are data, children, education, and trauma. This is very much in line with my purpose going into grad school, as I am a QMSS student focusing on trauma in education. My favorite visualization was the one where my frequently used words were connected to one another. This allowed me to see not only the main themes, but how they came together to make one cohesive argument. I also noticed Voyant’s use of the word ‘Reveal’ as the button to generate the statistics. This implies there is a truth hidden within the text that must be unveiled. 

Using Constellate, I decided to explore published texts that contain the word ‘gaelic’ to stay with the theme of my previous mapping project. The visualization generated was a graph that showed the amount of texts with that keyword published over time. Furthermore, I could explore word frequencies within that sample of texts. I had a lot of fun with this feature, looking at what concepts were more and less prevalent at certain times in history. For example, both the percentage and total amount of texts published about gaelic in Ireland was much larger than those published about gaelic in Scotland. During this exploration, I found myself thinking of our readings that insist that data is never neutral and requires close reading and interpretation. For example, if you switch the summary metric from percentage to total count, an entirely different story is told. This not only requires transparency from the statistician, but attention and prior knowledge from the audience. 

Data Visualization Praxis Assignment

For the assignment, I decided to visualize the leading causes of death in New York City. Using data from NYC Open Data, I wanted to study which causes of death affected different racial and ethnic groups. I created two visualizations on Tableau Public. I have never really used the platform before and struggled a little bit. One of the most challenging parts of making the visualizations was the inconsistencies in the dataset. In the Race and Ethnicity columns, there were different ways of labeling the same group. For example, Non-Hispanic Black and Black Non-Hispanic were showing up as other labels. I figured out how to use Tableau’s calculated fields to clean the data and make the data consistent. I also wanted to graph the total death rates but a lot of the data had missing values. At first, I thought about removing these values but reflecting on “Against Cleaning”, I wondered what information would be hidden or lost if I cleaned the data too much.  

The line graph, Leading Causes of Death in NYC, 2007-2022, shows the top 5 causes of death in NYC, including Diseases of the Heart, All other causes, Malignant neoplasms (cancers), Influenza and Pneumonia, and COVID-19. In 2020, you can see the large increase in deaths caused by COVID-19 and the drop that followed as vaccines and public health measures were implemented

The second visualization, “Trends in Leading Causes of Deaths by Race/Ethnicity, NYC (2015-2021), shows how causes of death affected different racial and ethnic groups. The graph uses a gradient where dark larger red squares represent higher death rates.