Kristoffer L. Nielbo

Kristoffer L. Nielbo

Is professor of humanities computing at Aarhus University and director of Centre for Humanities Computing Aarhus

Posts by Collection

portfolio

publications

Hierarchical Organization of Segmentation in Non-Functional Action Sequences

Published in Journal for the Cognitive Science of Religion, 2013

Event segmentation of cultural behaviors

Recommended citation: Nielbo, K.L., Schjoedt, U. & Sørensen, J. (2013). "Hierarchical Organization of Segmentation in Non-Functional Action Sequences." Journal for the Cognitive Science of Religion. 1(1).

Prediction Error During Functional and Non-Functional Action Sequences: A Computational Exploration of Ritual and Ritualized Event Processing

Published in Journal of Cognition and Culture, 2013

Computer simulation of human action perception under cultural influence

Recommended citation: Nielbo, K.L. & Sørensen, J. (2013). "Prediction Error During Functional and Non-Functional Action Sequences: A Computational Exploration of Ritual and Ritualized Event Processing." Journal of Cognition and Culture. 13.

Structural differences among individuals, genders and generations as the key for ritual transmission, stereotypy and flexibility

Published in Behaviour, 2017

We analysed a Zulu wedding ritual, posing two questions: (i) what makes a ritual stereotyped and rigid along with preserving certain flexibility; and (ii) does a ritual pass between generations and individuals en bloc, or as a smaller subset of acts? We found that the ritual repertoire constituted only one act that was common to all individuals that performed the ritual. Repetitive performance of this act conveyed the impression of a stereotyped ritual. This structure eases the transmission of the ritual, since it is only necessary to learn the performance of one act that can then be embedded in a sequence of ‘free-style’ acts. Gender difference was minimal, but young women performed more acts than adults, perhaps as a reflection of them being inexperienced actors. Altogether, the present study unveils underlying mechanisms that seem to characterize the evolution of rituals and thereby highlighting a foundation of human cultural behaviour in general.

Recommended citation: Nielbo, K. L., Fux, M., Mort, J., Zamir, R., & Eilam, D. (2017). " Structural differences among individuals, genders and generations as the key for ritual transmission, stereotypy and flexibility." Behaviour, 154(1), 93-114. https://doi.org/10.1163/1568539X-00003412

Kan man validere et selvopgør?

Published in Nordisk Netværk for Editionsfilologer. Skrifter, 2017

Download paper here

Recommended citation: Baunvig, I. K. F. & Nielbo, K. L., 2017, " Kan man validere et selvopgør?" Textkritik som analysmetod: bidra till en konferens anordnad av Nordiskt Nätverk för Editionsfilologer 2-4 oktober 2015. Svenska Vitterhetssamfundet (Stockholm), s. 45-67 (Nordisk Netværk for Editionsfilologer. Skrifter, Bind 12).

Modeling the Contested Relationship between Analects, Mencius, and Xunzi: Preliminary Evidence from a Machine-Learning Approach

Published in The Journal of Asian Studies, 2018

This article presents preliminary findings from a multi-year, multi-disciplinary text analysis project using an ancient and medieval Chinese corpus of over five million characters in works that date from the earliest received texts to the Song dynasty. It describes “distant reading” methods in the humanities and the authors’ corpus; introduces topic-modeling procedures; answers questions about the authors’ data; discusses complementary relationships between machine learning and human expertise; explains topics represented in Analects, Mencius, and Xunzi that set each of those texts apart from the other two; and explains topics that intersect all three texts. The authors’ results confirm many scholarly opinions derived from close-reading methods, suggest a reappraisal of Xunzi’s shared semantic content with Analects, and yield several actionable research questions for traditional scholarship. The aim of this article is to initiate a new conversation about implications of machine learning for the study of Asian texts.

Recommended citation: Nichols, R., Slingerland, E., Nielbo, K., Bergeton, U., Logan, C., & Kleinman, S. (2018). "Modeling the Contested Relationship between Analects, Mencius, and Xunzi: Preliminary Evidence from a Machine-Learning Approach." The Journal of Asian Studies, 77(1), 19-57. https://doi.org/10.1017/S0021911817000973

The Distant Reading of Religious Texts: A “Big Data” Approach to Mind-Body Concepts in Early China

Published in Journal of the American Academy of Religion, 2018

This article focuses on the debate about mind-body concepts in early China to demonstrate the usefulness of large-scale, automated textual analysis techniques for scholars of religion. As previous scholarship has argued, traditional, “close” textual reading, as well as more recent, human coder-based analyses, of early Chinese texts have called into question the “strong” holist position, or the claim that the early Chinese made no qualitative distinction between mind and body. In a series of follow-up studies, we show how three different machine-based techniques—word collocation, hierarchical clustering, and topic modeling analysis—provide convergent evidence that the authors of early Chinese texts viewed the mind-body relationship as unique or problematic. We conclude with reflections on the advantages of adding “distant reading” techniques to the methodological arsenal of scholars of religion, as a supplement and aid to traditional, close reading.

Recommended citation: Edward Slingerland, Ryan Nichols, Kristoffer Neilbo, Carson Logan; "The Distant Reading of Religious Texts: A “Big Data” Approach to Mind-Body Concepts in Early China." Journal of the American Academy of Religion, Volume 85, Issue 4, 30 December 2017, Pages 985–1016. https://doi.org/10.1093/jaarel/lfw090

Mining the Past – Data-Intensive Knowledge Discovery in the Study of Historical Textual Traditions

Published in Journal of Cognitive Historiography, 2018

Text-heavy and unstructured data constitute the primary source materials for many historical reconstructions. In history and the history of religion, text analysis has typically been conducted by systematically selecting a small sample of texts and subjecting it to highly detailed reading and mental synthesis. But two interrelated technological developments have rendered a new data-intensive paradigm – one that can usefully supplement qualitative analysis – possible in the study of historical textual traditions. First, the availability of significant computing power has made it possible to run algorithms for automated text analysis on most personal computers. Second, the rapid increase in full text digital databases relevant to the study of religion has considerably reduced costs related to data acquisition and digitization. However, a limited understanding of the scope, advantages, and limitations of data-intensive methods have created real obstacles to the implementation of this paradigm in historical research. This is unfortunate, because history offers a rich and uncharted field for data-intensive knowledge discovery, and historians already have the much sought after and necessary domain expertise. In this article we seek to remove obstacles to the data intensive paradigm by presenting its methods and models for handling text-heavy data.

Recommended citation: Nielbo, K. L.; Nichols, R.; Slingerland, E. (2018) "Mining the Past : Data-Intensive Knowledge Discovery in the Study of Historical Textual Traditions." Journal of Cognitive Historiography 3(1-2), pp. 93-118. https://doi.org/10.1558/jch.31662

Predictive minds in Ouija board sessions

Published in Phenomenology and the Cognitive Sciences, 2018

Ouija board sessions are illustrious examples of how subjective feelings of control – the Sense of Agency (SoA) - can be manipulated in real life settings. We present findings from a field experiment at a paranormal conference, where Ouija enthusiasts were equipped with eye trackers while using the Ouija board. Our results show that participants have a significantly lower probability at visually predicting letters in a Ouija board session compared to a condition in which they are instructed to deliberately spell out words with the Ouija board planchette. Our results also show that Ouija board believers report lower SoA compared to sceptic participants. These results support previous research which claim that low sense of agency is caused by a combination of retrospective inference and an inhibition of predictive processes. Our results show that users in Ouija board sessions become increasingly better at predicting letters as responses unfold over time, and that meaningful responses from the Ouija board can only be accounted for when considering interactions that goes on at the participant pair level. These results suggest that meaningful responses from the Ouija board may be an emergent property of interacting and predicting minds that increasingly impose structure on initially random events in Ouija sessions.

Recommended citation: Andersen, M., Nielbo, K. L., Schjoedt, U., Pfeiffer, T., Roepstorff, A., & Sørensen, J. (2018). "Predictive minds in Ouija board sessions." Phenomenology and the Cognitive Sciences. https://doi.org/10.1007/s11097-018-9585-8

Are We Exposed to the Same “News” in the News Feed?

Published in Digital Journalism, 2018

In the backdrop of interests in social media news and polarization the aim of this study is to examine to what extent we are exposed to the same “news” in the News Feed? The article defines news not from a classical position but from the content that is judged relevant to the user and made visible by the algorithm. The study examines filter bubbles as information simi- larity and more specifically nonoverlapping content segments in a unique snapshot dataset of 14 days of personal Facebook News Feeds for 1,000 Danes mirroring the Danish Facebook population. Deploying methods to analyze both link sources and content semantics the study finds that less than 10% in the link source analysis and 27.8% in the semantic analysis are in a filter bubble. The article tests and discusses suitable conceptual and empirical thresholds for information similarity that can inspire future studies. The best significant predictors for being in a filter bubble are what we term sociality: number of page likes, group memberships and friends. The study does not find age, gender, education or area of residence isolated to be sig- nificant predictors of participants in filter bubbles.

Recommended citation: Anja Bechmann & Kristoffer L. Nielbo (2018): " Are We Exposed to the Same News in the News Feed?" Digital Journalism, DOI: 10.1080/21670811.2018.1510741 https://doi.org/10.1080/21670811.2018.1510741

A curious case of entropic decay: Persistent complexity in textual cultural heritage

Published in Digital Scholarship in the Humanities, 2018

To understand an author’s developmental trajectory, the static traits and properties of author reconstruction and profiling are not sufficient. Instead, it is necessary to focus on high-level indicators of the complex set of variables that underlie the author’s transient mental states during his or her creative production. We propose a method that combines information theory with random fractal theory to study the mental dynamics of an author as indicated by text complexity. To illustrate its application, we analyze the developmental trajectory of the culturally influential and ‘graphomanic’ 19th-century Danish pastor N. F. S. Grundtvig. This approach can detect an age-related trend (entropic decay), a significant Kehre (turning point), and multiple event-related change points in his production. We argue that the approach is applicable beyond the specific case and can be extended to comparative analysis within and between authors, and, finally, to dynamic analysis of cultural information systems.

Recommended citation: Kristoffer L Nielbo, Katrine F Baunvig, Bin Liu, Jianbo Gao (2018). "A curious case of entropic decay: Persistent complexity in textual cultural heritage." Digital Scholarship in the Humanities, fqy054 https://doi.org/10.1093/llc/fqy054

How do humans process ritualized actions?

Published in The Cognitive Science of Religion: A Methodological Introduction to Key Empirical Studies, 2019

Recommended citation: Sørensen, J. & Nielbo, K. L. (2019): " How do humans process ritualized actions?" in (Slone, J. & McCorkle, W. (eds.)) The Cognitive Science of Religion: A Methodological Introduction to Key Empirical Studies. Bloomsbury Academic.

talks

Tutorial in Text & Data Mining w. Python

Published:

The recent explosion in digitized and digital text-media is rapidly changing the evidential basis for the humanities. While the humanities used to be the principal scientific consumers of text-based data, the majority of text analysis is now performed by ‘machines’ outside traditional humanistic domains. Text & Data Mining (TDM) applies machine learning in order to extract useful knowledge from from large collections of linguistic data. This workshop will showcase multiple techniques for building TDM tools based on the popular programming language Python.

Tutorial in Text & Data Mining w. Python

Published:

DESCRIPTION

The recent explosion in digitized and digital text-media is rapidly changing the evidential basis for the humanities. While the humanities used to be the principal scientific consumers of text-based data, the majority of text analysis is now performed by ‘machines’ outside traditional humanistic domains. Text-Analytics applies automated and data-intensive techniques in order to extract useful knowledge from from large collections of linguistic data. In this PhD course, the participant will acquire experience with two major machine learning paradigms (supervised and unsupervised learning) in order to answer research questions fundamental to the humanities: can we classify texts by genres, periods and status and how do surface structures reveal latent semantic properties. The workshop consists of a series of hands-on tutorials with Python combined with useful explanations and illustrations through use-cases. Programming experience is not a requirement, but participants are should to prepare by installing Python and completing three introductory tutorials available on-line.

Published:

teaching

Text-Mining the Great Unread

Under-graduate and graduate course, Aarhus University, School of Culture and Society, 2017

Three weeks intensive course about text-mining, natural language processing and information retrieval with Python and Unix.

Text-Mining Bootcamp

Postgraduate course, Dansk Sprog og Litteraturselskab & University of Copenhagen, 2017

Five days hands-on workshop about text-mining literature with Python. Curriculum, slides and code samples are avaialbe in GitHub repository: Repository here