David Mimno

Biographic Data

ID154107
NAMEDavid Mimno
GIVEN NAMESDavid
FAMILY NAMEMimno
SIGNATUREMIMNO D
AFFILIATIONSCornell University
ORCID0000-0001-7510-9404
VERIFIEDYes
TOTAL WORKS10
TOTAL CITATIONS27
AUTHOR COUNT10
EDITOR COUNT0
FIRST PUBLICATION YEAR2009
LATEST PUBLICATION YEAR2024
H-INDEX3

  • The Afterlives of Shakespeare and Company in Online Social Readership

    Open AccessMaria Antoniak, David Mimno et al.ARTICLEJournal of Cultural Analytics2024

    The growth of social reading platforms such as Goodreads and LibraryThing enables us to analyze reading activity at very large scale and in remarkable detail. But twenty-first century systems give us a perspective only on contemporary readers. Meanwhile, the digitization of the lending library records of Shakespeare and Company provides a window into the reading activity of an earlier, smaller community in interwar Paris. In this article, we expl…

  • Judicial self fashioning: Rhetorical performance in Supreme Court opinions

    Open AccessRosamond Elizabeth Thalken, David Mimno et al.ARTICLEDiscourse Studies2024References: 26

    Justices on the United States Supreme Court use rhetorical strategies to maintain institutional legitimacy. In the court opinion, a strategy called the monologic voice presents a flattering depiction of the Court. The monologic voice occurs through two tones, the individualistic and collective, which respectively maintain the Justices’ legitimacy through critique and the Court’s legitimacy through unification. We train large language models to id…

  • Separating the wheat from the chaff: A topic and keyword-based procedure for identifying research-relevant text*✰

    Open AccessAlicia Eads, Alexandra Schofield et al.ARTICLEPoetics2021Cited by: 3References: 17

  • The Tell-Tale Hat: Surfacing the Uncertainty in Folklore Classification

    Peter Broadwell, David Mimno et al.OTHER2018

    Classification is a vexing problem in folkloristics. Although broad genre classifications such as "ballad", "folktale", "legend", "proverb", and "riddle" are well established and widely accepted, these formal classifications are coarse and do little more than provide a first level sort on materials for collections that can easily include tens, if not hundreds, of thousands of records. Many large collections of folklore have been classified using …

  • Comparing grounded theory and topic modeling: Extreme divergence or unlikely convergence?

    Open AccessEric P S Baumer, David Mimno et al.ARTICLEJournal of the Association for…2017

    Researchers in information science and related areas have developed various methods for analyzing textual data, such as survey responses. This article describes the application of analysis methods from two distinct fields, one method from interpretive social science and one method from statistical machine learning, to the same survey data. The results show that the two analyses produce some similar and some complementary insights about the phenom…

  • The Tell-Tale Hat: Surfacing the Uncertainty in Folklore Classification

    Open AccessPeter Broadwell, David Mimno et al.ARTICLEJournal of Cultural Analytics2017

    Classification is a vexing problem in folkloristics. Although broad genre classifications such as “ballad”, “folktale”, “legend”, “proverb”, and “riddle” are well established and widely accepted, these formal classifications are coarse and dolittle more than provide a first level sort on materials for collections that can easily include tens, if not hundreds, of thousands of records

  • Applications of Topic Models

    Jordan Boyd‐Graber, Jordan Boyd-Graber et al.BOOKApplications of Topic Models2017

    How can a single person understand what’s going on in a collection of millions of documents? This is an increasingly widespread problem: sifting through an organization’s e-mails, understanding a decade worth of newspapers, or characterizing a scientific field’s research. This monograph explores the ways that humans and computers make sense of document collections through tools called topic models. Topic models are a statistical framework that he…

  • Missing Photos, Suffering Withdrawal, or Finding Freedom? How Experiences of Social Media Non-Use Influence the Likelihood of Reversion

    Open AccessEric P S Baumer, Shion Guha et al.ARTICLESocial Media + Society2015Cited by: 6References: 31

    This article examines social media reversion, when a user intentionally ceases using a social media site but then later resumes use of the site. We analyze a convenience sample of survey data from people who volunteered to stay off Facebook for 99 days but, in some cases, returned before that time. We conduct three separate analyses to triangulate on the phenomenon of reversion: simple quantitative predictors of reversion, factor analysis of adje…

  • Significant themes in 19th-century literature

    Open AccessMatthew L Jockers, Matthew Jockers et al.ARTICLEPoetics2013Cited by: 18References: 6

  • Evaluation methods for topic models

    Open AccessHanna Wallach, Hanna M Wallach et al.CONFERENCEProceedings of the 26th Annual…2009

    A natural evaluation metric for statistical topic models is the probability of held-out documents given a trained model. While exact computation of this probability is intractable, several estimators for this probability have been used in the topic modeling literature, including the harmonic mean method and empirical likelihood method. In this paper, we demonstrate experimentally that commonly-used methods are unlikely to accurately estimate the …