Larry Birnbaum

About

Larry Birnbaum is a Professor of Computer Science at Northwestern University, affiliated with the Master of Science in Artificial Intelligence program. His research and teaching focus on applied artificial intelligence and human-AI collaboration. He and his students develop, study, and apply new technologies in natural language processing (NLP), conversational interfaces, intelligent information systems, social media data analytics, machine learning, and computational journalism and media. His key areas of research include methods for the automatic generation of content by machine, specifically the automatic generation of narratives from data, and natural human-AI collaboration via conversational interaction. Birnbaum's work also spans intelligent information systems, including models of automatic and contextual search, information diversity, preference prediction, and recommendation using social media data. His research contributes to applications of AI in journalism and media.

Research topics

• Artificial Intelligence
• Computer Science
• Natural Language Processing
• Information Retrieval
• Machine Learning
• Engineering
• Mathematics
• Psychology
• Human–computer interaction

Selected publications

• Thousands of small, constant rallies

  2019-08-27 · 35 citations

  articleSenior author

  There is growing concern about the use of social platforms to push political narratives during elections. One very recent case is Brazil's, where WhatsApp is now widely perceived as a key enabler of the far-right's rise to power. In this paper, we perform a large-scale analysis of partisan WhatsApp groups to shed light on how both right-wingers and left-wingers used the platform in the 2018 Brazilian presidential election. Across its two rounds, we collected +2.8M messages from +45k users in 232…

  PublisherDOI

• HCI for Accurate, Impartial and Transparent Journalism

  2019-04-30 · 25 citations

  article

  While new media technologies hold the potential to serve journalism's dual goals of informing and engaging the public, these technologies also challenge the journalistic norms of accuracy, impartiality and transparency. The key question in this workshop is: How can HCI support accurate, impartial and transparent journalism? This question is ever more timely as the need for accurate and credible journalism is growing amid the proliferation of disinformation and opinion manipulation. In this works…

  PublisherDOI

• Extracting Commonsense Properties from Embeddings with Limited Human Guidance

  2018-01-01 · 23 citations

  articleOpen access

  Intelligent systems require common sense, but automatically extracting this knowledge from text can be difficult. We propose and assess methods for extracting one type of commonsense knowledge, object-property comparisons, from pretrained embeddings. In experiments, we show that our approach exceeds the accuracy of previous work but requires substantially less hand-annotated knowledge. Further, we show that an active learning approach that synthesizes common-sense queries can boost accuracy.

  PublisherOA PDFDOI

• Definition Modeling: Learning to Define Word Embeddings in Natural Language

  Proceedings of the AAAI Conference on Artificial Intelligence · 2017-02-12 · 19 citations

  preprintOpen access

  Distributed representations of words have been shown to capture lexical semantics, based on their effectiveness in word similarity and analogical relation tasks. But, these tasks only evaluate lexical semantics indirectly. In this paper, we study whether it is possible to utilize distributed representations to generate dictionary definitions of words, as a more direct and transparent representation of the embeddings' semantics. We introduce definition modeling, the task of generating a definitio…

  PublisherOA PDFDOI

• Jupybara: Operationalizing a Design Space for Actionable Data Analysis and Storytelling with LLMs

  2025-04-25 · 8 citations

  articleOpen access

  Mining and conveying actionable insights from complex data is a key challenge of exploratory data analysis (EDA) and storytelling.To address this challenge, we present a design space for actionable EDA and storytelling.Synthesizing theory and expert interviews, we highlight how semantic precision, rhetorical persuasion, and pragmatic relevance underpin effective EDA and storytelling.We also show how this design space subsumes common challenges in actionable EDA and storytelling, such as identify…

  PublisherOA PDFDOI

Recent grants

• III: Small: An Architecture and Platform for Frictionless Information Systems

  NSF · $504k · 2009–2014

Frequent coauthors

• Kristian J. Hammond

  23 shared

• Doug Downey

  12 shared

• Jacob D. Herbst

  Baum Consult

  8 shared

• Francisco Iacobelli

  Loyola University Chicago

  7 shared

• Victor S. Bursztyn

  7 shared

• Ray Bareiss

  6 shared

• Christopher Johnson

  Newcastle University

  6 shared

• Jiahui Liu

  5 shared

Awards & honors

• Best paper award (2013)

Similar researchers at Northwestern University

• Tobin J. Marksh-176
• Joseph Hupph-171
• James D Thomash-160