Will Kent discussed the origins of Wikipedia articles and the efforts to make Wikipedia more representative and equitable. He discussed how Wikidata taps into other language versions of Wikipedia to generate lists of pieces missing in English. Additionally, It allows instructors and students to create and track their contributions to Wikipedia. This effort is part of a larger mission to encourage community involvement in expanding Wikipedia’s scope. The goal is to enhance Wikipedia’s representation, ensuring it embodies the world of knowledge it aims to portray.
Thanks to Will’s expertise and active involvement in making Wikipedia more inclusive and equitable, I gained a deeper understanding of Wikipedia. Interacting with him gave us a more precise direction on best working with Wikidata this semester. It provided us with hands-on knowledge and an appreciation for the importance of diversifying content.
Because of Will’s visit, I was able to appreciate the importance of digital humanities and the profound impact technology can have on global communities. His use of digital tools made me realize geographical boundaries regarding community engagement no longer bind us. Digital technology has enabled new global connectivity, sharing, and collaboration levels. His work with Wikidata demonstrated the integration of data, technology, and efforts that can create positive change. As a result of this experience, I have gained a deeper understanding of digital humanities, not just as an academic concept but as a real way of getting involved in the community. Rather than merely consuming these tools, I now understand how they can contribute to a universal purpose.
Wills visit connects to the Digital Humanities Coursebook because network analysis can serve as an essential tool in the context of Wikipedia and its efforts to be more representative. Just as we can map relationships between individuals, poems, or artists, we can also map the origin and connection of Wikipedia articles. Using network analysis on Wikipedia can help identify gaps and biases in content, revealing which demographics or topics are underrepresented