Digital Humanities

Public Talk: Teaching Bias through Word Embeddings

May 16, 2022, 9:00am
This talk by guest speaker, Tom van Nuenen, discusses findings from the Discovering and Attesting Digital Discrimination project which focuses on biases in Machine Learning which proposed a data-driven approach to discover language biases encoded in the vocabulary of discourse communities on social media.

Digital Humanities Working Group (November 2023)

November 28, 2023, 12:30pm
The UC Berkeley Digital Humanities Working Group is a research community founded to facilitate interdisciplinary conversations in the digital humanities and cultural analytics. Our gatherings are participant driven and provide a place for sharing research ideas (including brainstorming new ideas and receiving feedback from others), learning about the intersection of computational methods and humanistic inquiry, and connecting with others working in this space at Berkeley.

MaxQDA Fundamentals

February 6, 2024, 1:00pm
This two-hour introductory workshop will teach you MaxQDA from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the MaxQDA software, upload multiple forms of data then how to use manual and autocode features. We will review some of the additional analytic features including visual, memo and the Questions, Themes and Theories (QTT) tools. We will briefly touch on the MaxQDA Team cloud-based version. Instructors will share recommended resources.

Digital Humanities Working Group Meetup

March 17, 2023, 12:00pm
The UC Berkeley Digital Humanities Working Group is a research community founded to facilitate interdisciplinary conversations in the digital humanities and cultural analytics. Our gatherings are participant driven and provide a place for sharing research ideas (including brainstorming new ideas and receiving feedback from others), learning about the intersection of computational methods and humanistic inquiry, and connecting with others working in this space at Berkeley.

Sand Mining - Plugging a Critical Data Gap

May 14, 2024
Suraj Nair
by Suraj Nair. Excessive sand mining is causing a global ecological crisis. In this blog post, I present why sand mining is one of the most pressing challenges facing the planet, and why persistent data gaps hinder accountability and monitoring. I also discuss an ongoing research project of mine where we combine freely available satellite imagery and machine learning models to build open-source sand mine detection tools that can plug some of these data gaps.

Conceptual Mirrors: Reflecting on LLMs' Interpretations of Ideas

April 23, 2024
María Martín López
by María Martín López. As large language models begin to engrain themselves in our daily lives we must leverage cognitive psychology to explore the understanding that these algorithms have of our world and the people they interact with. LLMs give us new insights into how conceptual representations are formed given the limitations of data modalities they have access to. Is language enough for these models to conceptualize the world? If so, what conceptualizations do they have of us?

Tactics for Text Mining non-Roman Scripts

April 15, 2024
Hilary Faxon, Ph.D.
by Hilary Faxon, Ph.D. & Win Moe. Non-Roman scripts pose particular challenges for text mining. Here, we reflect on a project that used text mining alongside qualitative coding to understand the politicization of online content following Myanmar’s 2021 military coup.

Hilary Faxon, Ph.D.

Data Science Fellow
Environmental Science, Policy, and Management
Dr. Faxon is an ethnographer who uses social media and critical remote sensing to understand and reimagine social justice in technology, environment, and development in the Global South. She is an Assistant Professor of Environmental Social Science at the University of Montana.

Computational Social Science in a Social World: Challenges and Opportunities

March 26, 2024
José Aveldanes
by José Aveldanes. The rise of AI, Machine Learning, and Data Science are harbingers of the need for a significant shift in social science research. Computational Social Science enables us to go beyond traditional methods such as Ordinary Least Squares, which face challenges in addressing complexities of social phenomena, particularly in modeling nonlinear relationships and managing high-dimensionality data. This paradigmatic shift requires that we embrace these new tools to understand social life and necessitates understanding methodological and ethical challenges, including bias and representation. The integration of these technologies into social science research calls for a collaborative approach among social scientists, technologists, and policymakers to navigate the associated risk and possibilities of these new tools.

Using Big Data for Development Economics

March 18, 2024
Leïla Njee Bugha
by Leïla Njee Bugha. The proliferation of new sources of data emerging from 20th and 21st century technologies such as social media, internet, and mobile phones offers new opportunities for development economics research. Where such research was limited or impeded by existing data gaps or limited statistical capacity, big data can be used as a stopgap and help accurately quantify economic activity and inform policymaking in many different fields of research. Reduced cost and improved reliability are some key benefits of using big data for development economics, but as with all research designs, it requires thoughtful consideration of potential risks and harms.