Resources

The CHASS Initiative aims to help people curious about computational humanities, arts, and social sciences (including public health) to obtain the information they seek about CHASS. The resources pages described and linked below will get you started on a journey of discovery.

• CHASS Workshops
• CHASS Methods
• CHASS Tutorials
• CHASS Bibliography

Upcoming CHASS Workshops

Upcoming workshops in CHASS Methods will be posted here. Contact chass@bu.edu if you know of any upcoming opportunities.

CHASS Methods

The CHASS toolkit is expanding rapidly. What follows is a sampling of computing and data-science methods deployed in CHASS research.

Big Data Analysis

• Intelligent Data Scraping: Retrieval of web-based data for subsequent analysis. Used in many CHASS fields. At BU: common, e.g. MET (Kalathur).
• Massive Crosscultural Databases: Large-scale database containing cross-cultural information formulated to support computational analysis. Used in anthropology, history, religious studies. At BU: China Historical Christianity Database and World Religions Database are BU efforts (CAS & STH). Also CAS-Anthropology (Garrett).

Humanities Applications

• Bibliometrics: Analyzes publications, authors, institutions, funding sources. Can be used in any CHASS field. At BU: library.
• Cultural Phylogeny: Application of biological phylogenetics to cultural subject matter. Used in cultural anthropology, literature, religious studies, history.

Machine Learning

• Computational Music Composition: Trains computers to create music. Used in music arts. At BU: CAS-CS (Snyder), CFA (Casinghino, Cornell).
• Machine-Learning Classification: Machine learning models to classify complex data into two or more groups. Used in a host of business and academic applications. At BU: common.
• Sentiment Analysis: Machine learning models to assess sentiment in a text, audio, or video. Can be used in many parts of CHASS. At BU: e.g. CAS-Linguistics (Hagstrom).

Modeling

• Artificial Minds: Computational representation of cognitive processes. Used to model medical interventions and analyze behavior.
• Geospatial Analysis: Models geospatial distribution and interconnection of people, organizations, and resources. Used in earth sciences, economics, social sciences. At BU: common (e.g. CAS-History Robichaud).
• Movement processing: Modeling human and other animal movement. Used in computational choreography, art-productions analysis. At BU: e.g. IS&T Giannitrapani, Brisson.
• Network Analysis: Models networks of all kinds, including networks of people and institutions. Used in social science, communications, business, policy. At BU: e.g. CAS-Sociology (Gondal), COM (Wells).
• Social Media Analysis: Models and analyzes social networks. Used in communications, social sciences. At BU: especially COM.
• Semantic Network Analysis: Modeling concepts and relationships between concepts using word proximity to create semantic networks. Used in many text-based aspects of CHASS.
• Visual Processing: Modeling and analyzing visual data. Used in an increasing number of business, economic, security, and military applications. At BU: pushed in CAS-CS and CDS (e.g. Betke, Saenko).

Natural Language Processing

• Natural Language Processing: Models and analyzes human language. Used in humanities and social sciences to extract usable information from datasets often too large for manual handling. At BU: picking up speed through CDS; CAS-Linguistics (Coppock).
• Natural Language Generation: Computational creation of human language. Used in business, clinical, and experimental social-science applications. At BU: picking up through CDS.
• Natural Language Understanding: Modeling human language at such depth that computing machines can perform like human beings using natural language. Mostly near-future applications.

Simulation

• Discrete-Event Simulations: Good for modeling sequences of discrete events. Sometimes used in public health. Often used in factory design or customer service.
• Microsimulations: Agent-based models with dumb agents. Used for traffic analysis, demographics, physical processes. At BU: physics, astronomy, engineering.
• Multi-Agent AI Simulations: Agent-based models with AI agents. Used to model complex behaviors and emergent social effects.
• Policy Simulation Using Artificial Societies: Computer modeling of both social worlds and possible policy initiatives. Used in public health, military, policy, economics, social science. At BU: e.g. CAS-Earth & Environment (Bell), SPH (Heaton)
• System-Dynamics Simulations: Good for modeling nonlinear complex systems with feedback loops and dampening effects. Good with population-level data. Routinely used in public health. At BU: common, especially SPH and Sargent.

Virtual Reality

• Virtual Reality for Archaeology: Virtual-reality models of archaeological sites. Used in anthropology, history, and archaeology.
• Virtual Reality for Health: Virtual-reality as a healthcare treatment (e.g. for addressing nightmare disorder). Used in medicine, public health, and clinical psychology.

Visualization

• Data Visualization: Employs computational methods to create complex and often interactive visualizations. Used not only to communicate research but also to stimulate researcher insights. At BU: common.
• Object Creation: Employs computational methods to create complex and often interactive visualizations. Used not only to communicate research but also to stimulate researcher insights. At BU: e.g. BU Shape Lab (Whiting).

CHASS Tutorials

Bi-weekly tutorials are offered with lunch every other week, in which CHASS faculty explain their methods and how to get started. You can view archived tutorials here:

• CHASS Wired-in Videos

A series of coding and methods tutorial offered by the IS & T department for all students. A schedule of offerings can be found here.

Online tutorials are also available that highlight computational methods in the humanities. These are:

• ACTiSS. “An Interactive Open Online Course on Computational Social Science.” View the course.
• Badham, Jen. NetLogo Tutorial. View the tutorial.
• Carpentras, Dino. “Agent Based Modelling Simply Explained.” Watch the video.
• Hofstede, Gert Jan. “Agent Based Modelling.” Watch the video.
• Mattingly, William. Introduction to Named Entity Recognition, 2021 (2nd ed.). View the tutorial.
• Peirson, Erick, Julia Damerow, and Manfred Laubichler . “Building a Text Collection with Zotero,” Digital HPS, 2012. View the tutorial.
• “Software for the Digital Humanities,” The Graduate Center, CUNY. View the tutorial.
• Elsayed, Yomna, “Machine Learning in Humanities and Social Science Research,” Digital Matters, 2022. View the tutorial. 
• “Digital Humanities: Text Mining and Analysis,” University of Otago, 2022. View the tutorial. 
• Grandchamp, Stephen. “Introduction to Voyant Tools: Basic Distant Reading of Literature,” Digital Humanities Lab at UMF, 2020. View the tutorial.
• “Developing computational skills for digital collections: a new Programming Historian series,” Research Libraries UK, 2022. View the tutorial.
• “Machine Learning, datasets + humanities research.” Research Libraries UK, 2022. View the tutorial.
• Gomes, Daniel and Ricardo Campos. ” Timeline summarization for large-scale past-web events with Python: the case of Arquivo.pt.” Aquivo.pt, 2022. Watch the video.
• “Natural Language Processing for the Digital Humanities.” Venice Centre for Digital and Public Humanities, 2022. Watch the video.
• Sante Fe Institute. “Introduction to Agent-Based Modeling.” View course content.
• Sante Fe Institute.  “Fundamentals of Net Logo.” View course content.
• University of Geneva. “Simulation and Modelling of Natural Processes.” View course content.
• Vierthaler, Paul. “Hacking the Humanities.” University of Lighton, 2018. View the tutorial and accompanying code.
• NLP + CSS 201 Tutorials. 
• “Python for Digital Humanities (01: Introduction to Python).” Python Tutorial for the Digital Humanities, 2019. View the tutorial.

CHASS Bibliography

What follows is a curated bibliography containing papers from BU faculty and others on computational methods and the scope of the field.

• 1st International Workshop on Computational History | Digital Repository Ireland.
• About – Computational Literary Studies (CLS).
• Antons, David, et al. “Computational Literature Reviews: Method, Algorithms, and Roadmap.” Organizational Research Methods, Mar. 2021, p. 1094428121991230.
• —. “Computational Literature Reviews: Method, Algorithms, and Roadmap.” Organizational Research Methods, Mar. 2021, p. 1094428121991230.
• April – University of Galway. 
• Artificial Intelligence – School of Computer Science and Statistics – Trinity College Dublin.
• Athira, U., and Sabu M. Thampi. “Hallmarking Author Style from Short Texts by Multi-Classifier Using Enhanced Feature Set.” Proceedings of the Third International Symposium on Women in Computing and Informatics – WCI ’15, ACM Press, 2015, pp. 284–89.
• Brügger, Niels, et al. “Internet Histories and Computational Methods: A ‘Round-Doc’ Discussion.” Internet Histories, vol. 3, no. 3–4, Oct. 2019, pp. 202–22.
• Cohen, Jonathan. Computational Methods for Historical Research on Wikipedia’s Archives. no. 2, 2014, p. 7.
• “Computational Literary History, Fall 2015.” Work Product, 2 Sept. 2015.
• Computational Literary Studies | KOMPETENZZENTRUM – TRIER CENTER FOR DIGITAL HUMANITIES.
• Computational Methods in Humanities Research. https://johnunsworth.name/florence.09.html.
• Ding, Juncheng. “New Computational Methods for Literature-Based Discovery.” UNT Digital Library, 2022.
• Egan, Gabriel. “Introduction to a Special Section on ‘Computational Methods for Literary–Historical Textual Scholarship.’” Digital Scholarship in the Humanities, vol. 34, no. 4, Dec. 2019, pp. 818–24.
• Egan, Jim. “Literary Data Mining: A Review of Matthew Jockers, Macroanalysis: Digital Methods and Literary History (Urbana: University of Illinois Press, 2013).” Digital Humanities Quarterly, vol. 010, no. 3, July 2016.
• Gibson, Abraham, et al. Focus: Computational History and Philosophy of Science Introduction. no. 3, 2019, p. 5.
• Gibson, Abraham, and Cindy Ermus. “The History of Science and the Science of History: Computational Methods, Algorithms, and the Future of the Field.” Isis, vol. 110, Sept. 2019, pp. 555–66.
• Godioli, Alberto. “OSL Skills Course: Computational Literary Studies.” Netherlands Research School for Literary Studies (OSL), 2 July 2021.
• Helmreich, Anne, et al. KEY TO AUTHOR CONTRIBUTIONS IN ARTICLE: p. 37.
• HIS 207 – Computational Methods in History – Acalog ACMSTM.
• History of Humanities Computing. Accessed 18 Oct. 2022.
• “How the New Science of Computational History Is Changing the Study of the Past.” MIT Technology Review. Accessed 18 Oct. 2022.
• Impett, Leonardo Laurence. Painting by Numbers: Computational Methods and the History of Art. p. 326.
• —. Painting by Numbers: Computational Methods and the History of Art. p. 326.
• Jäger, Gerhard. “Computational Historical Linguistics.” Theoretical Linguistics, vol. 45, no. 3–4, Dec. 2019, pp. 151–82.
• Journal of Computational Literary Studies. Accessed 18 Oct. 2022.
• Keim, Daniel A., and Daniela Oelke. “Literature Fingerprinting: A New Method for Visual Literary Analysis.” 2007 IEEE Symposium on Visual Analytics Science and Technology, IEEE, 2007, pp. 115–22.
• Kuhn, Jonas. “Computational Text Analysis within the Humanities: How to Combine Working Practices from the Contributing Fields?” Language Resources and Evaluation, vol. 53, no. 4, Dec. 2019, pp. 565–602.
• Lang, Sabine, and Björn Ommer. “Transforming Information Into Knowledge: How Computational Methods Reshape Art History.” Digital Humanities Quarterly, vol. 015, no. 3, Aug. 2021.
• Marcinkowski, Michael. “Methodological Nearness and the Question of Computational Literature.” Digital Humanities Quarterly, vol. 012, no. 2, July 2018.
• Mullen, Lincoln A. 1 Introduction | Computational Historical Thinking.
• ORC | Catalog.
• Piotrowski, Michael, and Mateusz Fafinski. Nothing New Under the Sun? Computational Humanities and the Methodology of History. p. 11.
• Preiser-Kapeller, Johannes. “Calculating the Middle Ages? The Project ‘Complexities and Networks in the Medieval Mediterranean and Near East’ (COMMED).” Medieval Worlds, vol. medieval worlds, no. Volume 2015.2, 2015, pp. 100–27.
• Recchia, Gabriel. “The Fall and Rise of AI: Investigating AI Narratives with Computational Methods.” AI Narratives: A History of Imaginative Thinking about Intelligent Machines, edited by Stephen Cave et al., Oxford University Press, 2020, p. 0.
• “Sawchen Lecture: Quinn Dombrowski & Dr. Andrew Janco, ‘Computational Methods for Russian Literature: Current State and Future Directions.’” Department of Central, Eastern, and Northern European Studies.
• School of Humanities, University of Wolverhampton. Brave New Humanities? A Novel Perceptions Symposium on Computational Literary Studies (Part 1). 2022.
• Special Issue # 6 (1.2022) | Textpraxis.
• Srihari, Sargur N. Determining Writership of Historical Manuscripts Using Computational Methods. p. 14.
• Sterman, Sarah, et al. “Interacting with Literary Style through Computational Tools.” Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, ACM, 2020, pp. 1–12.
• “The Digital Humanities Debacle.” The Chronicle of Higher Education, 27 Mar. 2019.
• Theory & Computational Methods. https://clasp.engin.umich.edu/research/theory-computational-methods/.
• UN3612: Introduction to Computational Literary | J. Reeve | Science and Society.
• Unsworth, John. A History of Computational Methods in the Humanities. p. 45.
• Using Computational Methods to Make Art, and to Break Art History – Benjamin C. Tilghman – Computational Visual Aesthetics.
• VII. Digital and Computational History of Science | MPIWG. 
• What Can Computer Algorithms Tell Us About Literature? | View in the digital magazine, Rewired.