Social Network Analysis

Concepts and Measurements in Social Network Analysis

October 22, 2024
by Christian Caballero. We live in an interconnected world, more so now than ever. Social Network Analysis (SNA) provides a toolkit to study the influence of this interconnectivity. This blog post introduces some key theoretical concepts behind SNA, as well as a family of metrics for measuring influence in a network, known as centrality. These concepts and measurements help form the basis for a theoretically informed study of social relationships in an era where the availability of relational data has dramatically increased thanks to technological advances.

Addison Pickrell

IUSE Undergraduate Advisory Board
Mathematics
Sociology

Addison is an aspiring mathematician and social scientist (Class of '27). He loves collecting books he'll never read, is an open-source and open-access advocate, and an aspiring community organizer and systems disrupter. Ask me about community-based participatory action research (CBPAR), critical pedagogy, applied mathematics, and social science.

Tonya D. Lindsey, Ph.D.

Data Science Fellow
Institute of Governmental Studies (IGS)

Tonya D. Lindsey is a visiting scholar at the Institute of Governmental Studies and the project director of CRB Nexus: Where Policy Meets Research, an initiative of the California Research Bureau (CRB) at the California State Library. As project director of CRB Nexus, she is developing a community of practice space for California’s policy staff and public scholars. As a CRB senior researcher she uses her expertise in research methods to analyze a wide variety of policy questions at the request of legislators, the governor’s office, and their staff. She received her PhD in sociology...

The More Things Change the More They Stay the Same?

December 18, 2023
By Tonya D. Lindsey, Ph.D. Think about how often you hear someone gripe about the deterioration of society and then blame the Internet or social media. This blog suggests that the things we are exposed to virtually are not new but instead present us with more and frequent opportunities to reflect on perennial social problems and find solutions even as we better understand ourselves as individuals in a global community.

Why We Need Digital Hermeneutics

July 13, 2023
by Tom van Nuenen. Tom van Nuenen discusses the sixth iteration of his course named Digital Hermeneutics at Berkeley. The class teaches the practices of data science and text analysis in the context of hermeneutics, the study of interpretation. In the course, students analyze texts from Reddit communities, focusing on how these communities make sense of the world. This task combines both close and distant readings of texts, as students employ computational tools to find broader patterns and themes. The article reflects on the rise of AI language models like ChatGPT, and how these machines interpret human interpretations. The popularity and profitability of language models presents an issue for the future of open research, due to the monetization of social media data.

My Summer Exploring Data Science for Social Justice: Learnings, Tensions & Recommendations

September 5, 2023
by Genevieve Smith. This summer I joined the D-Lab hosted Data Science for Social Justice workshop at UC Berkeley diving into Python – including TF-IDF, sentiment analysis, word embeddings, and more – with a lens towards leveraging data science for social justice. My team explored a Reddit channel on abortion and used computational analysis to answer key questions related to abortion access from before versus after Roe vs. Wade was overturned. Computational social science is incredibly powerful, but I continue to grapple with tensions particularly as it relates to employing machine learning and large language in international research, and end with key recommendations for CSS practitioners.

Spencer Le

Data Peer Consultant, UTech
Computer Science
Data Science

I am a senior majoring in Computer Science and minoring in Data Science. I love crunching down big data and analyzing it in order to help solve real-life issues. In my free time, I like jamming out to music, drawing, studying history, and posting on my foodstagram. If you have any questions regarding Computer Science or Data Science, please stop by!

Eileen Cahill

D-Lab Alumni
School of Information

Eileen is currently a first year Information Management and Systems student committed to studying human-centered design for the utility and usability of healthcare systems. She spent the last few years working in genomic research program analysis and management at the National Human Genome Research Institute. Prior to that, Eileen attended Georgetown University where she studied biology and studio art. During this time, she performed research on water contaminants in an analytical chemistry lab as well as research on estrogen mimicking compound effects on Zebrafish in a brain...

Working with spatial networks using NetworkX

December 7, 2021

I have always been interested in working with spatial networks. My first introduction to spatial network modeling was in Prof. John Radke’s Geographic Information Systems class when I learned about building and analyzing spatial networks using the Network Analyst extension in ArcMap. This extension provides powerful tools to solve common network problems, such as finding the best route across a city, finding the closest...

Adam Anderson, Ph.D.

Research Training Manager; Postdoc Lecturer
Digital Humanities

I’m an interdisciplinary data scientist, with a background in Middle Eastern languages (Hebrew, Arabic, and historical languages like Sumerian, Akkadian, Assyrian and Babylonian). I’ve worked in Syria, Lebanon, Israel, and Turkey with archaeological sites and museums. My technical skills include: translation and data storytelling, data forensics (3D imaging, mapping, modeling), computational linguistics (CTA, NLP, OCR), and network analysis (SNA). My roles on campus include: Research Training Manager of the Computational Social Science Training Program; Postdoc Lecturer...