Internet Archive

Mentors: Student: Chisom Nnamani

Open Library has millions of book records tagged with inconsistent, unstructured subject strings, for example, "science fiction", "Science Fiction", and "sci-fi" are treated as completely different tags, causing patrons to miss thousands of relevant books depending on how they search. This project solves that by building system-wide support for first-class genre tags on Works, directly addressing issue #11610. The solution works in four connected layers: first, developing a controlled genre vocabulary and a mapping dictionary from messy subject strings to canonical genre labels; second, updating the work schema in infogami to add a genres field storing canonical strings that serve as keys to fetch corresponding Tag objects; third, building a batch backfill pipeline that processes tens of thousands of high-demand works and populates their genres field; and fourth, updating Solr to index and facet by genre, displaying genre chips on book pages, and adding a librarian editing UI with autocomplete. By the end of GSoC, a patron searching for science fiction will get consistent, accurate results regardless of how they type it because works will have clean genre tags, indexed by Solr, backed by canonical Tag objects, and maintainable by librarians.

Mentors: Student: Sudipta Das

Navigating archived pages on the Wayback Machine requires significant manual effort. This project builds an experimental Chrome extension that uses Chrome's built-in Prompt API with Gemini Nano to automatically summarize archived pages and detect broken or soft-404 captures, all processed locally on the user's device for privacy. Deliverables include a working MV3 extension with page summarization, two-layer soft-404 detection using DOM signals and the CDX API, a lightweight overlay UI, and a public evaluation report.

Mentors: Student: Tanishq Sangwan

While Open Library excels at patron aquisition, it struggles with retaining the patrons- this happens due to a break in flow as post sign up patrons are met with an empty dashboard and lack of personalization. Without clear guidance, patrons struggle to find 'What to do next?' and drop off without engaging with the library or features such as reading lists and social connections. This project aims to bridge that very gap between 15 million patrons and 40 million works by introducing a progressive onboarding flow and a highly personalized dashboard for its patrons. By capturing user preference on sign up, we can personalize their dashboard ridding of the empty experience and providing clear Next steps, along with redesign of the dashboard to emphasize the high value features. Deliverables: Progressive Onboarding Flow (A 3-part form that captures and stores broad patron taste, specific book taste and yearly reading goal), Personalized Dashboard (Overhaul of the present 'My Books' page to priortize high-value features and addition of the 'Next reads' system), and the Recommendation Engine that powers the 'Next Reads' components using existing solr infrastructure.

Mentors: Student: Himarsha

Since the rise of social media two decades ago, researchers have been eager to characterize its impact on society, particularly in the area of information diffusion. Yet, one major gap remains: its reach via television remains unmeasured. As a result, studying the mediated influence of social media has been an ongoing challenge for Social, Behavioral, & Economic (SBE) researchers. Internet Archive’s TV News Archive provides access to over 2.6 million U.S. news broadcasts dating back to 2009. We will use these TV news broadcasts to train object detection and image classification models. “SM LogoTrack” will focus on detecting social media platform logos, while “SM PostTrack” will detect social media post screenshots. By developing robust visual detection models - "SM LogoTrack" and "SM PostTrack" our project will offer researchers and journalists new tools to systematically trace the flow of information from social media to television news. More importantly, this work directly contributes to the mission of the TV News Archive by enhancing the discoverability and analytical value of its vast video collections.

Mentors: Student: yzqzss

Enhance Zeno by implementing headless browsing via Rod for improved archiving of dynamic websites, developing a precise CSS parser for accurate URL extraction from both inline and separate CSS files, addressing potential disk exhaustion when downloading large files, creating a dummy test site for better local testing, enabling HQ to control Zeno clients remotely via WebSocket, and addressing existing GitHub issues to improve overall stability and functionality. Deliverables: - A Zeno version with implemented headless browsing, configurable for URL/domain matching patterns - A CSS extractor capable of extracting URLs from inline and separate CSS files. - A Zeno version implementing the --max-content-length and --min-space-urgent parameters. - A dummy test site backend with various test endpoints. - A Zeno version that can be controlled by HQ via WebSocket.