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Research

Cecilia Aragon's Research Group Archives

The following research group descriptions are archived because they are no longer offered, the faculty member is on sabbatical, or the group is taking a break. Please contact the faculty member or an advisor to learn more about these groups.

Winter 2024

A Systematic Literature Review of Research on Recommender Systems

Led by Sourojit Ghosh, HCDE PhD Candidate
Advised by Cecilia Aragon, HCDE Professor

The goal for this 3-credit DRG is to conduct a systematic literature review of research on recommender systems, to establish a comprehensive understanding of how researchers and designers of recommender systems define "success" in their work. Students who join the DRG will receive the experience of working on a full research project, from start to finish, which will be submitted to a conference at the end of the quarter. 

Participants in this DRG will need to be available on Tuesdays at 11 a.m. and commit to closely reading and analyzing a body of academic research papers. Additionally, they will be expected to contribute to the writing and preparing of the submission. 

We are looking for 6-8 students (grad or undergrad) who meet the following qualifications:

  • Able to manage a heavy research-reading load throughout the quarter.
  • Able to commit to at least 6 hours of work a week (including DRG meetings).

Autumn 2022 - Spring 2023

Research Design for Games to Teach Data Ethics

Co-directed by Cecilia Aragon, Bernease Herman, and Sarah Evans

This research group will co-design a game, along with faculty and students from the University of North Texas (UNT), a Hispanic-Serving Institution, to explore issues of ethics and diversity in data science. Students will be hands-on in exploring examples of educational games, brainstorming and providing ideas for games, creating prototypes, and playtesting. Some themes we may consider include data privacy, trust of algorithmic systems, predictive policing, fairness, and others. Our goal is to produce a working prototype of a game, playtest it, and study our own design processes to gain insight into how conflicts in norms and culture may change the learning process. 

This will be a two-quarter (with option to continue for the full year) directed research group with the goal of writing and submitting a paper to a top venue in spring 2023. All group members will be offered the opportunity to be co-authors on the paper.

We are looking for a relatively small group of people who are each interested in between 2 and 5 credit hours of credit/no credit grade in HCDE 496/596 for Fall, Winter, and Spring Quarters in 2022-2023. Interested undergraduate and graduate students may apply. Graphic design experience and familiarity with a wide variety of games is recommended but not required for motivated students.

Winter 2023

Comparing Content Recommendations based on User-Centered Content Analysis

In this 2-credit DRG, we are looking for 2-5 students for a research project which intends to compare content recommendation processes for designing social recommender systems. Group members will analyze user-generated content on online fanfiction communities and provide content recommendations to direct users to consume. Some prior experience with qualitative coding or content analysis is preferred, but not required. 

Winter 2023

Data Visualization and Analytics for Diversity and Inclusion Research

Co-directed by Kimberly Perkins and Cecilia Aragon

Did you know that 95% of airline pilots are men? Did you know that 94% are white? We don’t know the LGBTQI+ percentages because nobody asked.

This research explores why a demographic majority persists despite the industry being open to diversity more than 50 years ago. PhD student Kimberly Perkins has collected over 26,500 answered survey questions (both text and quantitative) from pilots in leadership roles at one major airline based in the United States. The DRG will focus on data cleaning and using Tableau to gain insights and prepare data visualizations from this data set.

Students must have extensive experience with Tableau and data cleaning. Completion of HCDE 411, HCDE 511, or similar data visualization/analysis class is a plus.

We are looking for a relatively small group of people who are each interested in between 2 and 5 credit hours of credit/no credit grade in HCDE 496/596 for Winter Quarter 2023. Interested undergraduate and graduate students may apply. Successful completion of this research may result in co-authorship of an academic paper.

Winter 2023

Turning Visualization Research into Product: Traffigram

This DRG will be led by UW CSE alum/Microsoft software engineer Ken Aragon and HCDE professor Cecilia Aragon.

Are you interested in the process through which a novel visualization algorithm is taken from research prototype to viable product? Do you think interactive maps could be improved by combining the science of visual perception with efficient algorithms?

Work with industry engineers and HCDE faculty to turn novel visualization techniques sponsored by UW’s CoMotion Labs into a marketable product. Participants will have the opportunity to work in areas such as user research, design, and software engineering.

Preference will be given to those with proficiency in programming/software development, data visualization, usability testing, design, or entrepreneurship. Meetings will take place once a week on campus at a time (most likely late afternoon) that best suits all participants’ needs. We are looking for a team of 5-6 dedicated students with a variety of skills and backgrounds to work together with the ultimate goal of creating a viable and successful product.

More information about the research can be found here: Human-Centered Data Science Lab » Traffigram: A Design Methodology for Distance Cartograms.

Autumn 2022

Human-Centered Natural Language Processing and Text Visualization

This research group will apply human-centered techniques in the fields of natural language processing (NLP) and visualization to study very large text corpora, with a specific focus on text visualization. We’re looking for students with experience in either (a) programming and analysis of large text datasets or (b) machine learning and data science. Data visualization or NLP experience is a plus but not required. 

Autumn 2022

Exploring Positionality in Qualitative Coding

This 2-credit DRG will take students through the process of qualitative coding of data. Students will be asked to perform open coding on datasets, and meet to discuss agreements/disagreements as they work towards formulating a codebook. As they code, they will be asked to consider how their own positionalities affect their interpretation of data, and what external contexts they applied in their work. The DRG will meet on Wednesdays, 10 a.m. - 12 noon. 

We are looking for 5-7 students with little or no prior experience with qualitative coding, but interest in qualitative research. If you are interested, please email Sourojit Ghosh (ghosh100@uw.edu) with a brief statement of intent, CV and unofficial transcript. Please use the subject line "Interest in Positionality in Qualitative Coding DRG:<your name>.

Spring 2022

Safety Culture for Professional Pilots

This research group investigates the interactions between socio-cultural issues and the construction and maintenance of a safety culture for professional pilots in the US aviation industry. These interactions will be explored via several methods, including grounded theory explorations of survey data and trace ethnography as well as a genealogy of literature in the safety systems field. 

We will explore the tension between ideas of excellence and competence which are embedded in gendered understandings and prioritize individual pilots against the understanding in the safety systems world that safety is achieved collectively–specifically in our early investigations we discovered that reactions to collective definitions of excellence and competence were highly gendered. Shifts in safety culture which stress the importance of revealing power dynamics which were previously invisible seem to produce a community threat response to protect traditional or historical ways of piloting. Changes to safety culture are politicized as socially progressive changes to the culture at large, and the culture at large becomes a reservoir for tools and mechanisms for reinforcing the hegemonic status quo. 

The goal of this research group is to write a paper to submit at a top venue.  All members of the group will be offered the opportunity to be co-authors on the paper. Themes for the paper may include but are not limited to: the collective ethos of professional pilots in the United States; individualist propensities as a threat to collective safety systems; gender as a lens of threat response to traditionalism or historical preservation; or, politicking as a form of silencing equity.  

This DRG will contain a subgroup to learn Python and use it to extract specific data points to augment the overall research. 

This is a closed DRG and will not be accepting applications. The group will meet either via zoom or in-person on Tuesdays, Wednesdays, or Thursdays for two-hour blocks based on members’ availability.  This DRG will be 3-6 credit hours of credit/no credit grade in HCDE 496 (undergrads) /596 (grads) for the Spring Quarter in 2022.

Winter 2022 - Spring 2022

Research Design for Games to Teach Data Ethics

Co-directed by Cecilia Aragon, Sarah Evans, Bernease Herman, and Andrea Figueroa

This research group will co-design a game, along with faculty and students from the University of North Texas (UNT), a Hispanic-Serving Institution, to explore issues of ethics and diversity in data science. Students will be hands-on in exploring examples of educational games, brainstorming and providing ideas for games, creating prototypes, and playtesting. Some themes we may consider include data privacy, trust of algorithmic systems, predictive policing, fairness, and others. Our goal is to produce a working prototype of a game, playtest it, and study our own design processes to gain insight into how conflicts in norms and culture may change the learning process. 

This will be a two-quarter directed research group with the goal of writing and submitting a paper to a top venue in June 2022. All group members will be offered the opportunity to be co-authors on the paper.

We are looking for a relatively small group of people who are each interested in between 2 and 5 credit hours of credit/no credit grade in HCDE 496/596 for Winter and Spring Quarters in 2022. Interested undergraduate and graduate students may apply. Graphic design experience and familiarity with a wide variety of games is recommended but not required for motivated students.

The group will meet virtually over Zoom to accommodate the UNT students, although we may meet a few times in person at UW before the UNT semester starts. Meetings will be on Thursdays at either 11:30-1, 12-1:30, or 12:30-2 depending on group availability. 

Autumn 2021 - Spring 2022

Human Centered Natural Language Processing and Text Visualization

This research group will apply human-centered techniques in the fields of natural language processing (NLP) and visualization to study very large text corpora, with a specific focus on text visualization. We’re looking for students with experience in either (a) programming and analysis of large text datasets or (b) machine learning and data science. Data visualization or NLP experience is a plus but not required.

Aviation Safety Research:
A sub-section of the research group will focus on analyzing survey responses and creating various forms of data visualization to convey survey results. Experience with survey analysis, data visualization tools, and a working knowledge of the aviation industry is a plus, but not required. 

Spring 2021

Emotions and Relationship-Building in Online Fanfiction Communities

Led by HCDE PhD student Sourojit Ghosh, with guidance from Professor Cecilia Aragon

This research group will investigate the role played by shared or conflicting emotions in the process of relationship-building in online communities. We aim to explore that role through extensive qualitative coding of individual fanfiction reviews. This work will be the final quarter of an ongoing research project on this topic, utilizing subsets of a large dataset of fanfiction data collected by the Human-Centered Data Science Lab in previous years. Past explorations with this dataset have put forward the theory of distributed mentoring, a phenomenon where people from all over the world and all age groups collaboratively give and receive support through an informal yet substantive network of constructive advice. The goal for this DRG will be to finish our qualitative coding via a novel collaborative coding and visualization tool, and to contribute to a research paper to be submitted to CSCW this academic year.

Participants in this DRG will gain hands-on experience with large datasets, learning to qualitatively analyze each data point for its rich content while also looking at it in the larger context of the entire set.

Spring 2021

Human Centered Natural Language Processing and Text Visualization

This research group will apply human-centered techniques to the field of natural language processing (NLP) to study very large text corpora, with an additional focus on text visualization. We’re looking for students with experience in either (a) programming and analysis of large text datasets or (b) machine learning and data science. No NLP experience is required as we will be reading seminal papers in the field and applying those techniques to a text dataset.

We plan to use a previously-collected dataset of over 61.5 billion words (the largest fiction dataset outside of the Google Books corpus) of stories, reviews, and associated metadata from fanfiction sites as a test dataset for human-centered NLP techniques.

Winter - Spring 2020

Human Centered Natural Language Processing

This research group will apply human-centered techniques to the field of natural language processing (NLP) to study very large text corpora. We’re looking for students with experience in either (a) programming and analysis of large text datasets or (b) machine learning and data science. No NLP experience is required as we will be reading seminal papers in the field and applying those techniques to a text dataset.

We plan to use a previously-collected dataset of over 61.5 billion words (the largest fiction dataset outside of the Google Books corpus) of stories, reviews, and associated metadata from fanfiction sites as a test dataset for human-centered NLP techniques.

Distributed mentoring and fanfiction data analytics

Spring 2019

Co-directed by Cecilia Aragon and Jenna Frens

This ongoing research project studies informal learning in online fanfiction communities. We are looking for students with experience in either (a) programming and analysis of large text datasets or (b) machine learning and data science, to join an existing research group. 

We’ve collected a vast, rich text dataset of over 61.5 billion words (the largest fiction dataset outside of the Google Books corpus) of stories, reviews, and associated metadata from fanfiction sites and have applied both qualitative (ethnography) and quantitative techniques (machine learning, statistical analysis, data visualization) to investigate the relationship between distributed mentoring and writing quality (e.g., grammar, reading level). We have published multiple papers on our research and are in the process of submitting others.

We have found quantitative evidence that distributed mentoring plays a positive role in fanfiction authors’ development as writers, and this quarter’s project continues our efforts with a specific focus on quantitative analysis of our large dataset. 

Games for Good: Designing a Data Science Ethics Game

Winter 2019

Co-directed by Cecilia Aragon and data scientist Bernease Herman

This research group will explore the use of analog and digital games to introduce users to ethical and human-centered issues in data science and computing. Students will be hands-on in exploring examples of educational games, brainstorming and providing ideas for games, and creating prototypes using paper and/or a computer game engine such as Unity3D. Some themes we will consider include data privacy, trust of algorithmic systems, predictive policing, fairness, and others.

At the end of ten weeks, we aim to produce a working prototype of the game, including several rounds of playtesting.

We are looking for a relatively small group of people who are each interested in between 2 and 5 credit hours of credit/no credit grade in HCDE 496/596. Interested undergraduate and graduate students may apply. Graphic design experience or programming experience is recommended, but not required for motivated students. 

Cultural differences in data privacy perspectives on social media

Winter 2019

The Cambridge Analytica scandal has triggered a discussion about data privacy in social media. As the news regarding this issue has traveled around the world, a worldwide public discussion about data privacy has emerged. Motivated by this context, we aim to answer this research question: Does the public online debate reveal different perspectives on data privacy across countries/cultures? To do so, we have collected Twitter activity associated with data privacy and the Cambridge Analytica scandal in both English and Spanish. Our work will result in insights about the different aspects of data privacy that are emphasized by people in different countries; a characterization of how geography, time, and bots influence the worldwide online conversation on data privacy; and, lessons learned about how best to apply human-centered data science techniques to support cross-cultural comparisons of social media data.

We have collected a large-scale Twitter dataset around this issue and are in the process of analyzing the data through both qualitative coding and automated analysis. The research group will take a mixed-methods approach to understanding the data, and as a result we are currently focused on qualitative coding of a large Twitter dataset.

The group is open both graduate and undergraduate students. Qualitative research experience in grounded theory and qualitative coding is desirable but not required. Bilingualism is a plus, particularly in Spanish. We strongly encourage interested undergrads to apply, even if you have little or no experience with this type of research. This is an excellent opportunity to be introduced to the methods of human-centered data science, as well as a chance to gain valuable insight into the way that research is carried out.

Distributed mentoring and fanfiction data analytics

Co-directed by Jenna Frens, PhD student; Cecilia Aragon, Professor

Winter 2019

Are you interested in applying human-centered data science to study how people learn from online fandom?

This ongoing research project studies informal learning in online fanfiction communities. We are looking for students with experience in either (a) programming and analysis of large text datasets or (b) qualitative research in online fandoms, to join an existing research group. We have published multiple papers on our research and are in the process of submitting others.

We have found quantitative and qualitative evidence that distributed mentoring plays a positive role in fanfiction authors’ development as writers, and this quarter’s project continues our efforts with a specific focus on visual analytics of a large dataset. We’ve collected a vast, rich text dataset of over 61.5 billion words (the largest fiction dataset outside of the Google Books corpus) of stories, reviews, and associated metadata from fanfiction sites and have applied both qualitative (ethnography) and quantitative techniques (machine learning, statistical analysis, data visualization) to investigate the relationship between distributed mentoring and writing quality (e.g., grammar, reading level).

Data Science Ethnography

Big data, the data deluge, the information explosion... there have been many names to describe the overwhelming amount of data that is being generated in just about every scientific domain today. Data science is the term that has emerged to describe the study of the extraction of knowledge from this flood of data, and it can include elements of various fields from computer science to applied mathematics to human centered design and engineering.

However, little is known about the culture and human processes surrounding the emerging practice of data science. A recent five-year, $37.8 million award to UW, UC Berkeley, and NYU from the Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation seeks to address this gap.

In this research group, we will utilize ethnographic practices including contextual inquiry, interviews, and participant observation to delve more deeply into the culture of data science on the UW campus. We will participate in scientific efforts in astronomy, oceanography, sociology, and other exciting data-driven fields on the cutting edge of science today.

We are looking for students with a background or interest in ethnography, who are each interested in between 2 and 5 credit hours of credit/no credit grade of HCDE 496/596. This group will meet Mondays from 3:30–4:30 p.m. in Sieg Hall, room 420.

Distributed Mentoring in Online Fanfiction Communities

Are you both a fan and a hacker? Are you interested in studying how people learn from online fandom?

This ongoing research project studies informal learning in online fanfiction communities. We are looking for a small number of experienced programmers interested in fandom to join an existing research group. We have already published one paper on our research (arXiv:1510.01425v2) and are in the process of submitting others.

We suspect that the novel concept of distributed mentoring plays a positive role in fanfiction authors’ development as writers, and this quarter’s project attempts to quantify this effect. We intend to scrape stories, reviews, and associated metadata from fanfiction sites and apply quantitative techniques (machine learning, statistical analysis, data visualization) to investigate the relationship between distributed mentoring and writing quality (e.g., grammar, reading level). Applicants must have spent substantial time outside of class writing scripts to scrape the web and process text, in languages such as python, perl, or bash. No experience in machine learning or visualization is required, although it is a plus.

Qualitative Coding and Analysis of Affect (Emotion) in Text

Co-directed by Cecilia Aragon & Taylor Scott

We are studying creative collaboration in a distributed team of astrophysicists and have collected a large amount of longitudinal data in the form of chat logs. We have been qualitatively analyzing this data to detect and classify emotional content, relate it to events occurring in the group's history, and form a theoretical framework of Distributed Affect. Our initial methods have been successful and promising, and we plan to refine and verify them through further qualitative coding and analysis of the data.

We strongly encourage interested undergraduates to join this group, even if you have little or no experience with qualitative research. This is an excellent opportunity to be introduced to various methods of analyzing text data, and gain insight into the way that such research is carried out.

Participation in this research group should be a good opportunity to:

  • Gain valuable practice in qualitative coding of chat log data
  • Learn more about the application of methods and theoretical perspectives in qualitative data analysis
  • Apply visual analysis as a means of exploring a large data set
  • Discover how these methods can be applied to your own areas of interest and research

Visualization of Large Text Data Sets

The amount of informal text communication (e.g. chat, texting, microblogs) in the world is increasing exponentially. Submerged within this text data deluge lies a wealth of information that is potentially valuable to businesses, governments, social scientists, and all human communities. In this research group, we will develop text visualizations with a specific focus on visual concordances that can be applied to very large text data sets.

This will be a two-quarter directed research group with the goal of submitting a paper to Vis 2016 in March 2016. During the first quarter, we will sketch, stretch our visual imagination with hands-on design exercises and critiques, and build and test visualization prototypes in javascript and d3. During the second quarter, we will iterate on the research questions, refine our visual prototypes, conduct usability tests of our designs, and write a paper on our results.
 

Analyzing Online Community Data

We have been studying distributed creative collaboration in an online community of children creating programmable media such as games, interactive stories, music and art on a YouTube-like website developed at MIT (scratch.mit.edu). We are interested in analyzing chat log data from the site in order to develop a measure of learning effectiveness in such distributed communities. We will base our analysis on a theoretical framework developed by Turns and an already completed coding taxonomy of a subset of the data developed by Aragon.
Participation in this research group should be a good opportunity to:
  • Experience how theory is used to guide analysis of data
  • Understand how collaborative analysis of data can be organized
  • Learn a new set of theories (externalization of knowledge, creative resonance)
  • Learn about publication venues
Gain insight into what students are thinking about when they engage in educational activities
We are looking for a relatively small group of people who are each
interested in between 2 and 5 credits. The actual organization of the work will be based on the number of people interested. 
 

Visualization of Large Text Data Sets

The amount of informal text communication (e.g. chat, texting, microblogs) in the world is increasing exponentially. Submerged within this text data deluge lies a wealth of information that is potentially valuable to businesses, governments, social scientists, and all human communities. In this research group, we will develop text visualizations with a specific focus on visual concordances that can be applied to very large text data sets.

This will be a two-quarter directed research group with the goal of submitting a paper to Vis 2016 in March 2016. During the first quarter, we will sketch, stretch our visual imagination with hands-on design exercises and critiques, and build and test visualization prototypes in javascript and d3. During the second quarter, we will iterate on the research questions, refine our visual prototypes, conduct usability tests of our designs, and write a paper on our results.

 

Understanding and Analyzing Eye Tracking Data

The belief that eyes are the windows to the soul has driven the study of human gaze tracking for over a century. However, it is only recently that technological advances in eye tracking technology have led to dramatic reductions in both the intrusiveness and cost of eye tracking systems. This has led to a recent surge in interest in this technology, which is now widely used in such diverse areas as market research, usability, reading diagnostics, neuroscience, psychology, robotics, games, and more. Even a passing familiarity with eye tracking technology is a highly desirable skill in today’s competitive job market.
 
This research group will allow you to develop knowledge and familiarity with the theory and history of eye tracking, the process of conducting eye tracking experiments, the use of eye tracking equipment, and the analysis of eye tracking data. The group will produce a set of research posters based on their data analysis with the aim of eventually publishing their work in appropriate conferences.
 
I am looking for a relatively small group of people who are each interested in between 2 and 5 credits. The actual organization of the work will be based on the number and background of people who sign up. If you are interested, please send an email to Cecilia Aragon (aragon@uw.edu) with a few paragraphs describing why you are interested in the project, your background in eye tracking or other relevant research, if any, and the number of credits you are seeking. This group will meet Wednesdays from 2 - 3:30 in Sieg 427.
Reading Group: Remixing User Research Methods
 
Winter 2014
 
This reading group will explore user research that adapts and combines methods from both within and beyond HCI. By reading and discussing a broad range of studies demonstrating appropriate, contextualized user research design, students will expand their toolkit of user research approaches. Topics will cover the spectrum from formative user research of values and process to evaluation of technological interventions, but with an emphasis on open-ended, exploratory, and/or qualitative methods.
Facilitated by Katie Kuksenok, Computer Science PhD student (supervised by Cecilia Aragon).
 
Informal Learning in Online Fan Communities
 
The internet has opened up unprecedented opportunities for people of all ages to discover and connect with others who share their interests. Among the most popular interest-based communities are those that bring together fans of various media texts, including movies, TV shows, music bands, novels, and video games. Whether formed around classics like Star Trek, Doctor Who, or Blade Runner, or newer media texts such as Breaking Bad, the Twilight series, or World of Warcraft, these online fan communities make it easier than ever before for people to meet other fans and engage in discussions and creative endeavors around their mutual interests.
 
Though scholars have begun to explore the learning that takes place in online fandoms, we still lack a complete understanding of the skills youth develop through their fan-based activities; the roles that identity, motivation, and emotion play in young people’s informal learning online; and the novice to expert trajectories made available in different online fan communities. This research group will shed light on each of these areas of inquiry through an ethnographic investigation of online fan communities currently popular among U.S. teens. The group will produce a technical report of this investigation with the aim of publishing the work in appropriate conference proceedings.
 
We encourage both graduate and undergraduate students to join this group. Qualitative research experience (e.g., contextual analysis, ethnography) is desirable but not required.
 
Analyzing Emotional Content of Text-Based Communication
 
The automated detection and classification of emotion in text-based communication is an open problem, yet recent research has indicated the importance of emotion in collaborative creativity. We have been studying creative collaboration in a distributed team of researchers from an international astrophysics group studying supernovae to learn more about the expansion history of the universe. As a result, we have collected a large amount of longitudinal data in the form of chat logs. We have been exploring methods to analyze this data to detect and classify emotional content, and relate it to events occurring in the group's history.
 
Analysis of the data has been carried out through both manual and automated coding of the chat logs. The coding scheme we have developed is grounded in this data set, and informed by existing taxonomies of emotion. The group had developed an automated approach to this type of classification, which is now freely available and open-source, that uses the manually coded data as the training set for our classification algorithms. We will continue to build a corpus of coded data, and will be leveraging human interaction to train better automatic emotion classifiers via a ‘human-in-the-loop’ model.
 
Research conducted by this group has been published in multiple papers in competitive conferences, and all contributing group members are co-authors.
We strongly encourage interested undergraduates to join this group, even if you have little or no experience with this type of research. This is an excellent opportunity to be introduced to the various methods we are using, as well as a chance to gain valuable insight into the way that research is carried out.
 
Games for Good: Designing and Building Collaborative Games for Engineering and Science
 
This research group will explore the use of computer gaming for collaborative science and engineering learning and discovery. Students will be highly hands-on in developing and designing aspects of a game, using the Unity3D game engine to build new levels and environments, constructing narratives, and integrating player feedback as part of an iterative design processes. We are currently designing a futuristic crime scene investigation game that embeds bioinformatics concepts as players work together to save the world from a fictional bioterrorist organization.
 
Research questions we will explore include:
  • What makes for compelling game mechanics and narrative storytelling?
  • What is the role of social game play and how can game environments support collaboration?
  • How are affect and emotion supported in a game environment to promote greater scientific creativity?
Programming experience or graphic design experience is highly recommended (yet not required if the student is motivated to get up to speed quickly in one of these areas).
 
 

Cecilia Aragon
Social Media Qualitative Analysis: Methods and Practice (SMQA DRG)

Large data sets that reflect activity on Facebook, Twitter, and other social platforms can be vital for investigating human phenomena as well as using social media activity to understand product or policy impact. However, existing HCI research methodologies do not scale in a straightforward way, resulting in ad-hoc data collection and analysis that may or may not achieve desired goals. This research group will develop an integrative methodological framework for analyzing social media data, focusing on text in context of time, network structure, and other associated metadata. In this group, we will have two complementary foci:
  • Methods
    • developing an integrated methodological framework that can be used by researchers in designing and evaluating studies of social media texts
    • relating existing practice with epistemological bases for existing empirical social science methodologies
    • involves understanding related methodological research in HCI and extensive analysis of existing literature on social media data studies
  • Practice
    • identifying and engaging with stakeholders in social media data analysis (including both researchers and, for example, managers who make decisions on the basis of research findings) across both academia and industry
    • contextual inquiry into the practices of researchers and their tool ecosystem
    • involves understanding related CSCW studies of similar populations, designing and distributing surveys, conducting interviews, and observation
In the course of this year-long project, we will spend the fall digging into the methods branch and designing studies for the practice branch, and conduct those studies during the winter and spring. This work will culminate in CSCW and/or CHI submission(s) and any other external deliverables that we determine valuable to relevant stakeholders.
HCDE professional masters and undergraduate students are especially encouraged to apply. Meeting time and location will be determined based on the schedules of group members. 
 
Understanding and Analyzing Eye Tracking Data
 
The belief that eyes are the windows to the soul has driven the study of human gaze tracking for over a century. However, it is only recently that technological advances in eye tracking technology have led to dramatic reductions in both the intrusiveness and cost of eye tracking systems. This has led to a recent surge in interest in this technology, which is now widely used in such diverse areas as market research, usability, reading diagnostics, neuroscience, psychology, robotics, games, and more. Even a passing familiarity with eye tracking technology is a highly desirable skill in today's competitive job market.
 
This research group will allow you to develop knowledge and familiarity with the theory and history of eye tracking, the process of conducting eye tracking experiments, the use of eye tracking equipment, and the analysis of eye tracking data. The group will produce a set of research posters based on their data analysis with the aim of eventually publishing their work in appropriate conferences.
 

Winter 2019

Games for Good: Designing a Data Science Ethics Game

Co-directed by Cecilia Aragon and data scientist Bernease Herman

This research group will explore the use of analog and digital games to introduce users to ethical and human-centered issues in data science and computing. Students will be hands-on in exploring examples of educational games, brainstorming and providing ideas for games, and creating prototypes using paper and/or a computer game engine such as Unity3D. Some themes we will consider include data privacy, trust of algorithmic systems, predictive policing, fairness, and others.

At the end of ten weeks, we aim to produce a working prototype of the game, including several rounds of playtesting.

We are looking for a relatively small group of people who are each interested in between 2 and 5 credit hours of credit/no credit grade in HCDE 496/596. Interested undergraduate and graduate students may apply. Graphic design experience or programming experience is recommended, but not required for motivated students. To apply, fill out the following form explaining your interest in the project, and attach a resume and an unofficial transcript here.

Please send any questions to both Bernease Herman <bernease@uw.edu> and Cecilia Aragon <aragon@uw.edu>.

The group will meet on Thursdays from 3-4:20 p.m. in Sieg 427.

Winter 2019

Cultural differences in data privacy perspectives on social media

Note: This DRG is at capacity for Winter 2018

The Cambridge Analytica scandal has triggered a discussion about data privacy in social media. As the news regarding this issue has traveled around the world, a worldwide public discussion about data privacy has emerged. Motivated by this context, we aim to answer this research question: Does the public online debate reveal different perspectives on data privacy across countries/cultures? To do so, we have collected Twitter activity associated with data privacy and the Cambridge Analytica scandal in both English and Spanish. Our work will result in insights about the different aspects of data privacy that are emphasized by people in different countries; a characterization of how geography, time, and bots influence the worldwide online conversation on data privacy; and, lessons learned about how best to apply human-centered data science techniques to support cross-cultural comparisons of social media data.

We have collected a large-scale Twitter dataset around this issue and are in the process of analyzing the data through both qualitative coding and automated analysis. The research group will take a mixed-methods approach to understanding the data, and as a result we are currently focused on qualitative coding of a large Twitter dataset.

The group is open both graduate and undergraduate students. Qualitative research experience in grounded theory and qualitative coding is desirable but not required. Bilingualism is a plus, particularly in Spanish. We strongly encourage interested undergrads to apply, even if you have little or no experience with this type of research. This is an excellent opportunity to be introduced to the methods of human-centered data science, as well as a chance to gain valuable insight into the way that research is carried out.

Note: This DRG is at capacity for Winter 2018

 

Winter 2019

Distributed mentoring and fanfiction data analytics

Co-directed by Jenna Frens, PhD student; Cecilia Aragon, Professor

Note: This DRG is at capacity for Winter 2018

Are you interested in applying human-centered data science to study how people learn from online fandom?

This ongoing research project studies informal learning in online fanfiction communities. We are looking for students with experience in either (a) programming and analysis of large text datasets or (b) qualitative research in online fandoms, to join an existing research group. We have published multiple papers on our research and are in the process of submitting others.

We have found quantitative and qualitative evidence that distributed mentoring plays a positive role in fanfiction authors’ development as writers, and this quarter’s project continues our efforts with a specific focus on visual analytics of a large dataset. We’ve collected a vast, rich text dataset of over 61.5 billion words (the largest fiction dataset outside of the Google Books corpus) of stories, reviews, and associated metadata from fanfiction sites and have applied both qualitative (ethnography) and quantitative techniques (machine learning, statistical analysis, data visualization) to investigate the relationship between distributed mentoring and writing quality (e.g., grammar, reading level).

Note: This DRG is at capacity for Winter 2018