Son Nguyen

Son Nguyen

Ph.D. Student at Department of Computer Science, The University of Texas at Austin

Posts by Collection

articles

expressions

talks

Actor-Network Theory

Social Aspects of Information Systems course, 2013

An introduction to Actor Network Theory for students in the Masters of Information Management and Systems (MIMS) course Read more

Moderating Online Conversation Spaces

Social Aspects of Information Systems course, 2015

An overview of how various online platforms moderate content, discussing issues that link up to the theories discussed in the Social Aspects of Information Systems class. Read more

Peer Production and Wikipedia

Social Aspects of Information Systems course, 2015

An overview of Wikipedia and other peer production platforms, discussing issues that link up to the theories discussed in the Social Aspects of Information Systems class. Read more

Scraping Wikipedia Data

The Hacker Within, BIDS, 2016

A tutorial (with Jupyter notebooks) about how to use APIs to query structured data from Wikipedia articles and the Wikidata project. Read more

Community Sustainability in Wikipedia: A Review of Research and Initiatives

PyData SF, 2016

Wikipedia relies on one of the world’s largest open collaboration communities. Since 2001, the community has grown substantially and faced many challenges. This presentation reviews research and initiatives around community sustainability in Wikipedia that are relevant for many open source projects, including issues of newcomer retention, governance, automated moderation, and marginalized groups. Read more

“The Wisdom of Bots:” An ethnographic study of the delegation of governance work to information infrastructures in Wikipedia

Annual Meeting of the Society for the Social Study of Science (4S), 2016

Wikipedians rely on software agents to govern the ‘anyone can edit’ encyclopedia project, in the absence of more formal and traditional organizational structures. Lessons from Wikipedia’s bots speak to debates about how algorithms are being delegated governance work in sites of cultural production. Read more

Jupyter and the Changing Rituals around Computation

JupyterCon, 2017

We (Stuart Geiger, Brittany Fiore-Gartland, and Charlotte Cabasse-Mazel) share ethnographic findings made observing and working with Jupyter notebooks, focusing on how people use Jupyter to create and deliver computational narratives in particular local contexts, like classrooms, hackathons, research collaborations, and more. Read more

Computational Ethnography and the Ethnography of Computation

Berkeley Institute for Data Science, 2017

Ethnography is traditionally a qualitative and inductive methodology – with its origins in cultural anthropology – that is now widely used to holistically investigate people’s lived experiences in and across cultures. In this talk, I define and discuss two ways of thinking about the role of ethnographic methods around computation, then discuss how my research relates to both. Read more

Are the bots really fighting? Behind the scenes of a reproducible replication

UC-Berkeley Department of Statistics: Reproducible and Collaborative Data Science, 2017

A guest lecture for Fernando Perez’s STAT 159/259 course on Reproducible and Collaborative Data Science, in which I discuss issues of open science and reproducibility around our recent paper Operationalizing conflict and cooperation between automated software agents in Wikipedia: A replication and expansion of ‘Even Good Bots Fight’ Read more

“But it wouldn’t be an encyclopedia; it would be a wiki”: The changing imagined affordances of wikis, 1995-2002

2017 Annual Meeting of the Association of Internet Researchers, 2017

This paper examines the early history of “anyone can edit” wiki software – originally developed in 1995, six years before Wikipedia’s origin. While today, the idea of a wiki is associated with large-scale, massively-distributed encyclopedic knowledge production, this was not always the case. Articles on pre-Wikipedia wikis were often closer to a Joycean stream of consciousness than Wikipedia’s Britannica-inspired texts that speak in single voice, and the underlying wiki platform lacked many of the affordances that are now taken for granted in wiki platforms. In fact, the creator of the first wiki advised Wikipedia’s co-founders that the goals of creating a general-purpose encyclopedia and a wiki were inherently contradictory. Read more

The Humanity of Artificial Intelligence

Bay Area Science Festival, 2017

Today, “artificial intelligence” seems to be everywhere – in our phones, vacuums, hospitals, and inboxes – but it can be hard to separate science fiction from science fact. Many discussions about AI imagine a fully autonomous superintelligence that designs itself with little to no human intervention, making decisions in ways that humans cannot possibly understand. Yet the work of designing, developing, engineering, training, and testing such systems requires a massive amount of human labor, which is typically erased when such systems are released as products. In this talk, I give a human-centered, behind-the-scenes introduction to machine learning, illustrating the creative, interpretive, and often messy work humans do to make autonomous agents work. Understanding the humanity behind artificial intelligence is important if we want to think constructively about issues of bias, fairness, accountability, and transparency in AI. Read more

Computational Ethnography and the Ethnography of Computation: The Case for Context

School of Information and Library Science, University of North Carolina at Chapel Hill, 2018

Ethnography is traditionally a qualitative and inductive methodology that is now widely used to holistically investigate people’s lived experiences in and across cultures. In this talk, I define and discuss two ways of thinking about the role of ethnographic methods around computation, then discuss how my research relates to both. Read more

Computational Ethnography and the Ethnography of Computation: The Case for Context

School of Information Sciences, University of Illinois at Urbana-Champaign, 2018

Ethnography is traditionally a qualitative and inductive methodology that is now widely used to holistically investigate people’s lived experiences in and across cultures. In this talk, I define and discuss two ways of thinking about the role of ethnographic methods around computation, then discuss how my research relates to both. Read more

Computational Ethnography and the Ethnography of Computation: The Case for Context

College of Information Studies, University of Maryland at College Park, 2018

Ethnography is traditionally a qualitative and inductive methodology that is now widely used to holistically investigate people’s lived experiences in and across cultures. In this talk, I define and discuss two ways of thinking about the role of ethnographic methods around computation, then discuss how my research relates to both. Read more

Publics: Witnessing and Measuring

UC-Berkeley: Human Contexts and Ethics of Data course, 2018

A guest lecture for Cathryn Carson and Margo Boenig-Liptsin’s course on Human Contexts and Ethics of Data (HIST 182C, STS 100C), focusing on how various publics generate, analyze, and interpret data. Read more

The Human Contexts of Data: Infrastructures, Institutions, and Interpretations

University of Manchester, Data Science Institute, 2018

In this talk, I discuss the role of qualitative and ethnographic methods in relation to computer, information, and data science. These holistic, reflexive, and meta-level approaches to studying data and computation in context help us better understand how to both support and practice data analytics at various scales. Read more

Computational Ethnography and the Ethnography of Computation: The Case for Context

IT University of Copenhagen, ETHOSlab, 2018

Ethnography is traditionally a qualitative and inductive methodology that is now widely used to holistically investigate people’s lived experiences in and across cultures. In this talk, I define and discuss two ways of thinking about the role of ethnographic methods around computation, then discuss how my research relates to both. Read more

Key Values: What We Talk About When We Talk About ‘Open Science’

Open Science Symposium, Department of Second Language Studies, University of Hawaiʻi at Mānoa, 2018

Openness in science is hard to disagree with as an abstract principle, but what exactly do we mean when we call for science to be made open – or more open than before? In this talk, I introduce and unpack the many different goals, strategies, products, values, and assumptions of the broad open science movement. Read more

The Types, Roles, and Practices of Documentation in Data Analytics Open Source Software Libraries: A Collaborative Ethnography of Documentation Work

2018 European Conference on Computer-Supported Cooperative Work, 2018

Data analytics increasingly relies on open source software (OSS) libraries that extend scripted languages like python and R. Software documentation for these libraries is crucial for people across all experience levels, but documentation work raises many challenges, particularly in open source communities. In this collaboration between ethnographers and data scientists, we discuss the types, roles, practices, and motivations around documentation in data analytics OSS libraries. Read more

Designing and Using Data Science Ethically

Machine Learning and User Experience San Francisco (MLUXSF), 2018

With the rise of Machine Learning and AI to solve human-focused needs, how do we design and use data science ethically to help empower and support people? Read more

Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?

ACM FAT* 2020, 2020

Many machine learning projects for new application areas involve teams of humans who label data for a particular purpose, from hiring crowdworkers to the paper’s authors labeling the data themselves. Such a task is quite similar to (or a form of) structured content analysis, which is a longstanding methodology in the social sciences and humanities, with many established best practices. In this paper, we investigate to what extent a sample of machine learning application papers in social computing — specifically papers from ArXiv and traditional publications performing an ML classification task on Twitter data — give specific details about whether such best practices were followed. Read more

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