Author Archives: Kelechi Vincent Iwuagwu

Echoes of Resistance: Revealing Afrobeat’s Cultural Impact – Analyzing Fela Kuti’s Legacy.



Fela Anikulapo Kuti was a Nigerian musician, the original creator of Afrobeat, and an outspoken activist from a family of generational activists – who used his music to awaken African consciousness, challenge political corruption and call out social injustice in Nigeria, and the globe. Growing up as a young Nigerian millennial in the 90’s and being exposed to the sound of this legendary figure, the vibrant and revolutionary genre, which was conceived in Nigeria in the 1970’s, has always been more than just music—it’s a cultural force intertwined with political commentary and social movements. My final project, Echoes of Resistance: Revealing Afrobeat’s Cultural Impact while also Analyzing Fela Kuti’s legacy, researches this unique intersection through the lens of Digital Humanities, while uncovering hidden narratives and reimagining how we understand music’s role in shaping history.

The project focuses on Afrobeat’s representation in Nigerian media during 1996-1997, a politically tumultuous period marked by military rule and social unrest. Drawing from the Archivi.ng digital newspaper repository, my research will be using natural language processing, sentiment analysis, and advanced text analysis to dissect the cultural expressions embedded in the media coverage of Afrobeat. By combining these techniques with innovative data visualization, this project reveals the intricate connections between political events, musical commentary, and grassroots activism.

An interdisciplinary team comprising statisticians, data engineers, and digital humanities researchers will be collaborating to bring this project to life. Through web scraping and interactive visualization development, we will be transforming historical newspaper data into dynamic tools for analysis. The results include an interactive web platform, thematic dashboards, and temporal visualizations that highlights Afrobeat’s evolution and its societal impacts.

Key findings from this research will uncover narratives that highlight Afrobeat’s dual role as a cultural critique and a rallying cry for positive change. The project’s deliverables not only preserve these narratives but will also be making them accessible to a global audience, fostering a deeper appreciation for Afrobeat’s legacy.

Looking ahead, this project aims to contribute to academia and beyond. Open-source code and research tools will be made available on platforms like GitHub, encouraging collaboration and innovation in Digital Humanities. Academic publications will be developed to share insights with a broader scholarly community, while public-facing platforms will bring Afrobeat’s historical significance to light for enthusiasts and researchers alike.

By merging traditional scholarship with digital analysis, Echoes of Resistance demonstrates the transformative potential of integrating music, culture, and technology. It will be reaffirming Afrobeat’s place not just as a musical genre, but as a powerful agent of societal change.

PowerPoint slide for ref. http://Echoes of Resistance_ Revealing Afrobeat’s Cultural Impact – Analyzing Fela legacy..pptx

Kelechi Iwuagwu (Data Analysis & Viz Candidate, CUNY Graduate)

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)

#Blogpost; Understanding Data Feminism for AI: A Talk by Dr. Lauren Klein – Mapping Feminicides, African Languages in AI, Equity and Liberation.

Data Feminism (2020), by Dr. Catherine D’Ignazio’s co-authored with Lauren F. Klein describes how feminist principles can reshape our relationship with data and technology. In a world increasingly dependent on AI and algorithmic systems, their work challenges us to critically examine who holds power, who is excluded, and how to address these imbalances.

A core argument in Dr. Klein’s lecture is the brittleness of AI systems. These systems, optimized for specific groups, often fail when applied to diverse populations. This brittleness extends beyond AI—social, governmental, and technical systems also favor certain groups while marginalizing others. The roots of this inequity often lie in biased training and missing data, which consciously or unconsciously perpetuates harmful exclusions.

Feminism, as outlined by Klein, is not just a belief in equal rights for all genders but a political action and intellectual heritage that seeks to dismantle unequal power structures. Intersectional feminism goes further by addressing the broader dynamics of privilege and oppression. It’s not solely about gender but about understanding and challenging power imbalances across all facets of society.

Data Feminism offers seven principles to guide data work: examining and challenging power, rethinking binaries and hierarchies, elevating emotions and embodiment, embracing pluralism, considering context, and making labor visible. Mimi Onuoha’s Library of Missing Data vividly demonstrates how missing or biased datasets harm marginalized communities. Examples include the lack of data on police violence, hate crimes against trans people, or systemic barriers for older adults with disabilities.

Dr. Marivate Masakhane of South Africa NLP project exemplifies efforts to preserve African languages in AI. These languages, often classified as “low resource,” are overpowered by dominant languages in mainstream AI models. This imbalance underscores how technology perpetuates hierarchies, sidelining voices that do not conform to the default settings established by powerful tech entities. The work of Masakhane challenges this narrative, striving to bake cultural and linguistic context into machine learning models.

One critical reference is the Data Against Feminicide project, which supports activists like Maria Salguero who uses data to map and document feminicides. Through AI co-design workshops, the project asked pivotal questions: Should tools be built at all? If so, how can they reflect the activists’ unique contexts and priorities? Notably, the activists resisted delegating their work to AI, recognizing that optimization often prioritizes efficiency over equity not factoring the nuances that makes us human. Instead, they emphasized building tools that empower their work rather than replacing it. Also noting that the risks of data activism are real.

Most importantly, the takeaway is designing AI and data systems must prioritize liberation over exploitation, amplifying marginalized voices while dismantling oppressive structures.

Finally, Dr. Klein asserts, that the fight for equitable data is essential for challenging systemic inequities and reclaiming power for the marginalized.

Link to Dr. Klein’s Talk

– Kelechi Iwuagwu (CUNY Graduate Candidate, Data Analysis & Viz)

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)

#Blogpost: Intro to R Workshop.

On Thursday, October 11, 2024, at about 6 pm, I attended an insightful introduction to R workshop organized by Zachary Lloyd and Chen Zou, fellows at the Graduate Center Digital Initiatives (GCDI). The session was aimed at beginners and focused on the essentials of using R, an open-source programming language widely utilized for statistical analysis, data transformation, survey analysis, Machine Learning, etc.

While the w/shop was ongoing, I took minutes of the session, noting down highlights which made me miss some initial concepts in setting up the software for practice. I privately chatted with Chen during the workshop who was truly patient in carrying me along irrespective of my slow-paced learning curve of the tool. We were asked to create a posit account, which enabled us to access R on a cloud for practice.

The session began by distinguishing between R and RStudio, where R serves as the engine – the core programming language, while RStudio serves as the user-friendly interface (UI) where coding, data visualization, and manipulation take place that allows users to interact with R seamlessly. The instructor also introduced foundational concepts like Boolean operations (1/0, TRUE/FALSE, YES/NO), arithmetic functions (+,-, /,*), and vectors – which she mentioned are lists of items or variables of the same type. We also learned basic R syntax such as how to assign values to variables using the “<-” operator and how logical operations like `==` (equal to) and `!=` (not equal to) work in R.

One of the key takeaways was the instructor’s reiteration of the importance of practicing typing out codes instead of often falling for the temptation of copying and pasting, as it helps build muscle memory. The workshop also emphasized the value of making mistakes, which is a major part of the learning process in programming. As she mentioned, “Every programming language takes time to understand. It just takes consistent practice to become the best at it”.

We explored libraries like Tidyverse, a collection of packages designed to simplify data wrangling and visualization in R. Functions such as `view()` and `head()` were demonstrated for examining data, with the former opening a new page in RStudio and the latter displaying default the first six rows of a dataset in the console.

After being shown several examples, we did some practice exercises that enabled us to get a glimpse of how R functions. Most of my colleagues in the workshop seemed to be getting it, as there were multiple correspondences on the Zoom chat bar, which also meant that the workshop was hands-on.

On a final note, the workshop was a good introduction to R, emphasizing the importance of continuous practice and exploration. Whether for data visualization, machine learning, or statistical analysis, R offers a flexible and powerful toolset for data professionals.

Interestingly, the workshop ended at 8:04 pm with a Google digital evaluation form, which made me reflect on the increasing importance of digital documentation and feedback within digital humanities. As a potential digital humanist, every evaluation or rating not completed on a digital platform might as well be considered non-existent, reinforcing the role of digital tools in modern academic and research processes.

– Kelechi Iwuagwu (A Data Analysis & Viz CUNY GradCenter Candidate).

Reflecting on “The Possibly Impossible Research Project”, while embracing Failure for intellectual progress.

In “The Possibly Impossible Research Project,” Rebekah Fitzsimmons and Suzan Alteri introduced first-year students at Georgia Tech to archival research focused on forgotten women authors of science books from the Romantic and Victorian (1770s – 1830s) periods. This collaboration with the Baldwin Library of Historical Children’s Literature aimed to expose students to the systemic marginalization of women in the sciences and challenge the idea that women were historically uninvolved in STEM. At an institute that admitted female students into regular classes only in 1952, this feminist-oriented assignment provided an eye-opening opportunity for students to research the overlooked contributions of women to scientific advancement.

Students encountered numerous frustrations as they struggled to locate biographical information for the assigned authors. The challenge reflected real-world research setbacks, pushing students to ask why so little documentation existed about these intellectual lionesses. Their frustrations often became evident through class discussions and social media exchanges. Alongside conventional research methods, students used digital tools like Twitter and WordPress to document their process, fostering collaboration and building professional research skills.

Through this process, students contributed to the recovery of forgotten histories, shedding light on how women’s writing in science education shaped scientific discourse. This initiative illustrates the value of archival research in undergraduate education, promoting both discovery and the importance of documenting the research journey, even when it’s incomplete.

Moreover, Fitzsimmons’ varying approach challenged the conventional classroom dynamic, where students expect professors to have all the answers. This new model allowed students to grapple with failure and setbacks, learning to persist through “brick walls” and to explore creative solutions—valuable lessons that they will carry into future research and professional endeavors.

What is Digital Humanities? Definitions and Perspectives.

Before diving into the different definitions of Digital Humanities (DH), it’s worth exploring the etymology of the words themselves. Understanding the roots of “digital” and “humanities” should give us insight into the field’s core purpose.

The word digital originates from the Latin word “digitus”, meaning “finger or toe”. Historically, humans used fingers for counting and basic calculations, making them the earliest “digital” tools. Over time, “digital” evolved to describe anything manipulated by numbers or “digits,” which led to its use in technology. Towards the mid-twentieth century in the 30s, “digital” came to signify a system of electronic signals based on binary code (a series of 0s and 1s). In this sense, “digital” refers to the way we now manipulate information electronically, a modern parallel to how fingers once helped us count. 

www.vocabulary.com

Now, combine this with humanities—the study of human culture, history, language, and expression. When put together, Digital Humanities reflects a merging of traditional humanistic study with the power of digital technology to study, analyze, and interpret vast amounts of data, offering new methods of exploring human culture with respect to time.

 Defining Digital Humanities

There isn’t one fixed definition of Digital Humanities; rather, it covers a broad range of approaches that vary based on perspective. At its core, DH uses computational tools to advance the study of humanities subjects such as literature, history, philosophy, and art. This could involve analyzing texts using algorithms, visualizing historical data, or creating digital archives. For example, researchers might apply text-mining techniques to large collections of literature to uncover patterns in language or themes that would be impossible to detect manually.

Another major aspect of DH involves digitization and the creation of digital archives. In this sense, digital tools are used to preserve rare texts, manuscripts, or cultural artifacts that can be accessed and studied by a global audience. For instance, projects like the Caribbean Digital Archive make historically significant, pre-20th-century Caribbean texts available online, bringing marginalized voices into the spotlight. This aspect of DH emphasizes inclusivity and open access, helping to democratize knowledge.

Furthermore, collaboration is a key feature of Digital Humanities. Researchers from different fields—historians, coders, data scientists—work together to build interactive platforms like digital exhibitions or visualizations that engage not only academic circles but also the public. This interdisciplinary approach makes DH dynamic and far-reaching.

Lastly, some view DH as having a social justice focus. – Digital tools can be used to expose inequalities or recover stories that have been erased by traditional archives. Projects that map historical immigration patterns or highlight the role of enslaved peoples in shaping culture are examples of how DH plays a crucial role in activism.

DH is an evolving field that blends the “fingerlike” manipulation of data with deep, humanistic questioning. Whether through computational analysis, the creation of digital archives, interdisciplinary collaboration, or social justice projects, Digital Humanities is transforming how we explore and interpret human culture. Just as early humans used their digits—fingers & toes—for counting, we now use digital tools to process and engage with vast amounts of data, offering new ways of seeing the world and its histories. This fusion of the ancient and modern makes Digital Humanities both a continuation of and a revolution in the study of humanity.

A digital humanities world.

Kelechi Iwuagwu (PRagmatic)

Data Analytics & Viz, The Graduate Center, CUNY