LDA Topic Modeling: Contexts for the History & Philosophy of Science

In this paper we discuss the application of LDA topic modeling to questions that interest historians & philosophers of science, which we illustrate primarily through our own work on modeling Charles Darwin's reading and writing behavior. We discuss the need to go beyond simplistic presentations of topic models that tend to give scholars the idea that the algorithms produce results that are superficial and perhaps unreliable. The ways in which topic models are often misrepresented and misunderstood frame our attempt to convince readers that, despite appearances, topic modeling provides a lot more of value to HPS research than merely providing for enhanced search and information retrieval from large sets of documents. Rather than "topics" we prefer to think of these topic models as revealing contexts for individual reading and wrote, leading us to ask questions about the individual exploration and exploitation of the materials to which a scientist such as Darwin had access. We discuss the use of topic models as tools for identifying influence and measuring creativity within those contexts and conclude that the interplay between human intelligence and sophisticated algorithms will expand the range of questions about science that HPS scholars will ask, and can answer

LODE: Linking Digital Humanities Content to the Web of Data

Numerous digital humanities projects maintain their data collections in the form of text, images, and metadata. While data may be stored in many formats, from plain text to XML to relational databases, the use of the resource description framework (RDF) as a standardized representation has gained considerable traction during the last five years. Almost every digital humanities meeting has at least one session concerned with the topic of digital humanities, RDF, and linked data. While most existing work in linked data has focused on improving algorithms for entity matching, the aim of the LinkedHumanities project is to build digital humanities tools that work "out of the box," enabling their use by humanities scholars, computer scientists, librarians, and information scientists alike. With this paper, we report on the Linked Open Data Enhancer (LODE) framework developed as part of the LinkedHumanities project. With LODE we support non-technical users to enrich a local RDF repository with high-quality data from the Linked Open Data cloud. LODE links and enhances the local RDF repository without compromising the quality of the data. In particular, LODE supports the user in the enhancement and linking process by providing intuitive user-interfaces and by suggesting high-quality linking candidates using tailored matching algorithms. We hope that the LODE framework will be useful to digital humanities scholars complementing other digital humanities tools

Exploration and Exploitation of Victorian Science in Darwin's Reading Notebooks

Search in an environment with an uncertain distribution of resources involves a trade-off between exploitation of past discoveries and further exploration. This extends to information foraging, where a knowledge-seeker shifts between reading in depth and studying new domains. To study this decision-making process, we examine the reading choices made by one of the most celebrated scientists of the modern era: Charles Darwin. From the full-text of books listed in his chronologically-organized reading journals, we generate topic models to quantify his local (text-to-text) and global (text-to-past) reading decisions using Kullback-Liebler Divergence, a cognitively-validated, information-theoretic measure of relative surprise. Rather than a pattern of surprise-minimization, corresponding to a pure exploitation strategy, Darwin's behavior shifts from early exploitation to later exploration, seeking unusually high levels of cognitive surprise relative to previous eras. These shifts, detected by an unsupervised Bayesian model, correlate with major intellectual epochs of his career as identified both by qualitative scholarship and Darwin's own self-commentary. Our methods allow us to compare his consumption of texts with their publication order. We find Darwin's consumption more exploratory than the culture's production, suggesting that underneath gradual societal changes are the explorations of individual synthesis and discovery. Our quantitative methods advance the study of cognitive search through a framework for testing interactions between individual and collective behavior and between short- and long-term consumption choices. This novel application of topic modeling to characterize individual reading complements widespread studies of collective scientific behavior.Comment: Cognition pre-print, published February 2017; 22 pages, plus 17 pages supporting information, 7 pages reference

Multi-level computational methods for interdisciplinary research in the HathiTrust Digital Library

We show how faceted search using a combination of traditional classification systems and mixed-membership topic models can go beyond keyword search to inform resource discovery, hypothesis formulation, and argument extraction for interdisciplinary research. Our test domain is the history and philosophy of scientific work on animal mind and cognition. The methods can be generalized to other research areas and ultimately support a system for semi-automatic identification of argument structures. We provide a case study for the application of the methods to the problem of identifying and extracting arguments about anthropomorphism during a critical period in the development of comparative psychology. We show how a combination of classification systems and mixed-membership models trained over large digital libraries can inform resource discovery in this domain. Through a novel approach of “drill-down” topic modeling—simultaneously reducing both the size of the corpus and the unit of analysis—we are able to reduce a large collection of fulltext volumes to a much smaller set of pages within six focal volumes containing arguments of interest to historians and philosophers of comparative psychology. The volumes identified in this way did not appear among the first ten results of the keyword search in the HathiTrust digital library and the pages bear the kind of “close reading” needed to generate original interpretations that is the heart of scholarly work in the humanities. Zooming back out, we provide a way to place the books onto a map of science originally constructed from very different data and for different purposes. The multilevel approach advances understanding of the intellectual and societal contexts in which writings are interpreted

Topic Modeling the Reading and Writing Behavior of Information Foragers

How do individuals create a knowledge base over a lifetime? Charles Darwin left detailed records of every book he read from The Voyage of the Beagle to just after publication of The Origin of Species. Additionally, he left copies of his drafts before publication. I use these records to build a case study of how reading and writing interact to create conceptual novelties, such as the theory of natural selection and modification by descent. The model is extended to cover entire disciplines by bootstrapping reading and writing histories from bibliographies in scientific publications, scaling the model to address the question of how we move from an individual psychology to society? There are two central components from cognitive science that impact the proposed models. The first is bounded cognition. People have limited attention, and that attention is further limited by an individual’s information processing ability. Information foraging is a framework for managing the trade-off between exploration of new information and exploitation of existing knowledge when searching for information. Most existing work on information foraging and bounded cognition examine short-term information foraging problems, such as formulating web search queries in a laboratory setting with a known information goal. Through the case study of Charles Darwin, we use real-world datasets to explore this problem at a timescale of decades with unknown information goals. The base of the reading model is topic modeling with Latent Dirichlet Allocation (LDA). This method reduces the dimensionality of text by reducing each document to a topic distribution, where each topic is defined as a probability distribution over the words in the collection. With these probability distributions, we are able to apply information theoretic measures to calculate the divergence between texts. These divergences characterize a particular reading decision as exploiting the topics exposed by previously read texts or exploring new topics. I train these topic models not on the records, but identify each volume in the Hathi Trust Digital Library and train the topic model on the full text of the books. While Darwin’s reading notebooks and manuscript drafts provide relatively precise information on reading and writing behaviors at a day-level granularity, that type of data is rare. I explore three extensions of the models, dealing with progressively more “fuzzy” data. First, I look at the contents of Darwin’s Library at the time of his death to infer readings 1860-1882. These readings are used to provide a preliminary analysis of his work on The Descent of Man and the latter editions of the Origin of Species. Then, I look at another historical figure: Thomas Jefferson, whose working library formed the basis of the Library of Congress. We examine the bibliography of his retirement library and tie it into his correspondence to find possible evidence for when certain volumes were read. Finally, I scale the model up to the discipline of neuroscience. I extract citation graphs from the Web of Science to infer reading histories for neuroscientists based on the articles they cited. I use the text of the abstracts of these articles to perform a similar analysis to the Darwin case study on readings and writings. These extensions of the model highlight the potential to work with less precise data and illuminate future problems. Throughout the work, I emphasize the notion of multiple realizability and interpretive pluralism. Each model is itself a population of models, and while simpler term-frequency-based models may show many of the same effects as the topic models, an argument is made for the explanatory power of the topic model with respect to causality