Undergraduate Research Fellowship

Undergraduate Research Fellowship

Next deadline - November 1, 2024

The Berkeley Center for New Media is pleased to announce three undergraduate research fellowships are open for application for spring 2024! Selected students will have the opportunity to work closely with new media graduate students on dissertation-level research. Each fellowship comes with a stipend of $1,000.

If you are interested in multiple projects, please submit separate applications for each project.

Applications are now closed. Applications will next be due November 1, 2024. Apply here!

Projects for Undergraduate Research Assistance Spring 2024

Resistance to AI Image Generators in Creator Communities​

This project explores the ways visual artists resist participation in generative text-to-image models, and larger algorithmic impositions. Advancements in text-to-image generators place visual artists in a vulnerable position. These technologies are enabled by deep learning models, including General Adversarial Networks (GANS) and latent diffusion, which learn mathematical patterns in space, color and depth in reference images to construct "new" images. Several creative professionals have reprimanded the unpermitted use of their work towards training text-to-image generators. Civil suits alleging copyright infringement have been filed against major developers and platforms. But, copyright law may not provide enough coverage to protect visual artists' works. This project identifies strategies of resistance to text-to-image generators by analyzing: (a) interviews; (b) legal codes, information policies and practices; (c) historical instances of resistance by artists, artisans, and manual laborers. This project will propose interventions at the intersection of information law and critical data studies. This project is supported by the AI Policy Hub at the Berkeley Center for Long-Term Cybersecurity.

The undergraduate researcher will transcribe interviews from audio recording to text; code transcriptions using qualitative coding software; group, analyze, and summarize codes and develop themes using a thematic analytic approach; and map themes into coherent visual aids. Students should have an interest in critical data studies, and the adverse impacts of "big data" on everyday life, work, and culture. Preferred knowledge in MAXQDA, Figma, Excel. Students with a background in Art Practice, Cognitive Science, Digital Humanities, Information Science, Media Studies, New Media preferred; but all majors may apply.

Media Education Research Lab (MERL)​

The Media Education Research Lab (MERL) is UC Berkeley-based lab led by Professor Abigail De Kosnik that is developing methods for measuring and representing diversity in media. I am the head graduate student researcher in the lab, and I oversee our team of undergraduate research assistants. We currently use a three-step process to research and review media texts. First, the researcher (whom we refer to as the "tagger") views the media text and extensively researches the text’s reception, any instances of biased representation in the text, and the text’s major cast and characters. Next, they use their research to generate a numerical score out of 100. Finally, the tagger writes a 250-500 word review summarizing analysis of the text as pertains to diversity and representation. We are currently tagging, scoring, and reviewing the 10 highest grossing films and 10 best-reviewed and most-discussed television series per year between 2019 and 2022. Each of these 80 texts will be tagged and reviewed twice. When completed, these reviews will be published on the MERL website, which will be launched later this year. We envision this website as a tool that media consumers, parents, and students can use to evaluate their media viewing practices.

Our undergraduate research assistant team is responsible for tagging, scoring, and reviewing the majority of our 160 media texts (80 texts, each tagged twice). We believe that our humanities-based method yields more nuanced results than, for example, using machine learning to generate similar data. However, it is a time- and labor-consuming process, and we would benefit from as much assistance as possible. An additional undergraduate researcher would be invaluable in finishing our 160 texts and getting the website published several weeks sooner than we might otherwise. After being trained in MERL’s tagging scoring method, the undergraduate research fellow would tag and review 8-10 texts over the course of the semester and attend bi-weekly meetings with other undergraduate research assistants and myself.

Some preference will be given to students who are able to continue on the project into the new academic year.

Triggered Identities: Unpacking the Online Persona of Asian American Gun Enthusiasts on Social Media

This research project aims to investigate the online behaviors of Asian American Instagram personalities who actively promote and calcify their identity in connection with firearms. By analyzing their posts, captions, and engagement patterns, I seek to understand the motivations behind this unique intersection of cultural identity and gun culture. In particular, Asian American women have a long history of powerful images with guns that date back to the propaganda used in the Vietnam War. I'm trying to parse how this lineage may be affecting current perceptions of Asian American radicality and why these objects, as prosthesis, related to power, capital, and the upholding of whiteness.

The research assistant will assist in data collection-- monitoring and cataloging Instagram/Reddit/TikTok posts, captions, and engagement metrics related to these Asian American personalities. Furthermore, they could help with literature reviews, providing a comprehensive overview of relevant studies on identity, gun culture, and social media.

Previous Undergraduate Research Fellows and Projects

2024 funded candidates and projects here and here!

2023 funded candidates and projects here and here!

2022 funded candidates and projects here and here!

2021 funded candidates and projects here!

2020 funded candidates and projects here!

2019 funded candidates and projects here!

2018 funded candidates and projects here!

2017 funded candidates and projects here!