CDH 2020

# Current Challenges in Computational Literary Studies
 
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 Christof Schöch (TCDH, Trier University, Germany)
 
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<emph>CDH 2020</emph> | Shanghai Library | Oct 19-21 2020
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<img height="60" data-src="img/basics/uni-trier.png">

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- Hello, everyone. 
- Thank you for the invitation to this conference
- I'm sorry I can only join you by videoconference
- I will speak about "Computational Literary Studies" today
- I know many of you are interested in Digital History
- The challenges I will speak about are quite general,
- so I hope my remarks will still be relevant to you

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## Overview
1. [Operationalization | 操作化](#/3)
1. [Metadata | 元数据](#/4)
1. [Diversity | 多样性](#/5)
1. [Open Access | 开放获取](#/6)

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- I will briefly introduce the field of Computational Literary Studies
- And I will then speak about four major, high-level challenges I see for the field
- I believe it is really important that these challenges continue to be addressed, going into the future
- Computational Literary Studies (CLS) is a subfield of Digital Humanities concerned with using methods from Computer Science and Statistics for the investigation of literary texts. 
- CLS has a long tradition within Digital Humanities. 
- Research questions in CLS are in part those of established literary studies, in part those arising from the transposition of methods from Computer Science and Statistics into the literary domain.

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## Challenge 1: Operationalization 操作化

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### What is "operationalization"?
* Operationalization is an essential step in a CLS research design 
* Operationalization makes a research question or a hypothesis computable and verifiable 
* Operationalization creates an explicit link between concepts and formal features

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### Challenges in operationalization 
* Operationalization creates increased complexity 
* But it also makes complexity more manageable

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### Example: "Keyness" in the *Zeta and Company* project
<img height="400" data-src="img/zeta-project.jpg"></img>
 https://zeta-project.eu/en/

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- This is a project about measures of keyness
- "Keyness" refers to words, or other features, that are characteristic of a group of texts when comparing it to another group of texts
- We want to... model keyness conceptually
- ... Implement various measures of keyness
- ... Evaluate those measures
- ... Use them for literary analysis

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### Crime fiction vs. science fiction
crime fiction (Gallimard, Série noire): <img height="180" data-src="img/serie-noire.jpg"></img>
 
science fiction (Denoël, Présence du futur): <img height="180" data-src="img/presence-futur.jpg"></img>

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- Our data is the contemporary French novel
- We focus on popular subgenres like crime fiction, science fiction, and sentimental novels
- Here you see some book covers for crime fiction and science fiction

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### What is keyness?
* Concepts 
 * aboutness
 * distinctiveness
 * salience
 * etc.
* measures 
 * log-likelihood ratio
 * t-test
 * Burrows Zeta
 * etc.

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- Concepts: 
- Aboutness: words that express the content of the subgenres
- Distinctiveness: words that help distinguish one subgenre from another
- Salience: words that readers notice when reading novels from the subgenre
- Measures
- log-likelihood: compares frequencies across groups
- t-test: compares frequency distibutions
- Zeta: compares the dispersion of words
- **Key issue**: 
- how do measures and concepts relate to each other?
- t-test has a bias for function words, and is related to distinctiveness
- Burrows Zeta has a bias for content words, and is related to aboutness
- This needs to be clarified before using such measures.

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## Challenge 2: Metadata 元数据

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### Why is metadata crucial? 
* Small, well-understood corpora: metadata is easy to generate, less needed 
* Large, unexplored corpora: metadata is crucial, hard to generate 
* Crucial for going beyond the canon 
 * knowing the population: sampling
 * interpreting quantitative results

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- First wave of CLS used relatively small corpora (dozens, sometimes a few hundred texts)
- Current wave of CLS is about ever larger text collections
- First thousands, now millions of books: HathiTrust: now 17 million volumes
- So metadata also helps us do analyses beyond the canon (see challenge #2: diversity)
- (a) Without a documentation of the statistical population, you can't create representative samples 
- (b) When you can't read the documents in your corpus, you need metadata to explain results

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### Challenge of metadata
* CLS needs a complete record of literary production 
* CLS needs relevant, detailed, accurate metadata on texts 
 * author: gender, related authors, etc.
 * setting: space, time
 * type: subgenre, etc.
 * content: themes, plot, protagonists
 * properties: e.g. proportion of direct speech

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- First need: complete documentation of literary production (population)
- Second need: relevant, detailed, accurate metadata on individual texts
- But: Library catalogs usually don't have these
- So we need to generate metadata ourselves! (Agenda)

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### Example: *Mining and Modeling Text*
<img height="450" data-src="img/mimotext-page.jpg"></img>
 https://mimotext.uni-trier.de/aktuelles/english

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### CLS Agenda: Generate metadata
<img height="500" data-src="img/mimotext-candide.jpg"></img>
 Example: *Candide* by Voltaire

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- from bibliographies and library catalogs
- from scholarly publications
- from primary texts
- Then, model it as an information network
- In the form of Linked Open Data
- Goal: Wikidata for Literary History

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## Challenge 3: Diversity 多样性

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- The DH open up an opportunity for diversity
- We have the chance to base our research on more than just a few most famous works
- in CLS, this means we can better understand the dynamics of literary history
- And better understand the meaning of literature for people

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### Distant Reading for European Literary History
<a href="img/map-of-action-members.html"><img height="450" data-src="img/map-of-action-members-logo.jpg"></img></a>
 https://distant-reading.net

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- I would  like to illustrate the challenges of this in CLS by speaking about a project called "Distant Reading for European Literary History"
- The key aim of this project is to create a multilingual corpus of novels
- 100 novels from as many different European languages as we can
- At the moment, we are working on 17 different languages

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### Dimensions of diversity
* In corpus building and text analysis 
 * Literary production in multiple languages 
 * Highly-canonized and forgotten novels
 * Gender balance in authorship
 * Wide range of kinds of novels
* In the community we create 
 * Diversity of career stages
 * Gender balance
 * Researchers from less research-intensive countries

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- In this project, there are several dimensions of diversity that are relevant
- regarding corpus building and text analysis (name them)
- regarding the community we create in the project (name them)
- Very important to me: diversity not for the people, but with the people! 
- In this sense, this is really a "grass-roots" project

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### Challenges
* Digitization tends to be non-diverse (!) 
 * driven by canonicity
 * focused on few authors
* Corpus building: "paradox of diversity" 
 * support diversity of languages
 * support diversity within each collection

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- Digitization
  - Digitization is driven by canonicity
- Paradox
  - We want to encourage a diversity of languages, including smaller literary traditions
  - And we want to encourage a diverse corpus composition (women, non-canonized, early)
  - But: for smaller languages, diverse composition is hard!
  - So: by pushing on one side, we lose on the other side
  - We need to balance both

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## Challenge 4: Open Access 开放获取

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### Open Access
* Open Access means 
 * the right to access, modify, analyse, redistribute materials
 * with minimal technical, financial and legal obstacles 
* Open Access concerns 
 * scholarly publications
 * primary sources (literary texts) 
* Open Access is an essential component of Open Science

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### Why is Open Access important?
* Open Access enables research into our most recent cultural history 
* Open Access is an essential component of Open Science 
 * transparent
 * reproducible
 * sustainable

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### A challenge in all our projects
* Distant Reading 
 * Focus determined by copyright: pre-1920 texts only
 * this allows us to practice Open Science
* Zeta and company  
 * 20th-century novels: we can't distribute our corpora
 * limited transparency and reproducibility
* Mining and Modeling Text 
 * We rely on a copyright exception for Text Mining; 
 * But we can't make our research our extracted metadata

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### Challenges of Open Access
* Create a new balance
  * between legitimate copyright interests
  * and the benefits of excellent research for society
* Create new institutional roles of academia
  * As distributors of scholarly publications  
  * As creators of digital resources

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- New balance
- New roles
- Also: work beyond academia

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## Conclusion

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### Four Challenges of CLS
1. Operationalization: "deconstruction" of concepts and measures 
2. Diversity: increase diversity in research objects and the research community 
3. Metadata: generate metadata for very large corpora 
4. Open Access: look beyond purely scholarly concerns

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# Thank you for your kind attention 谢谢您的关注
 
 
 
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 Trier Center for Digital Humanities: <a href="https://www.tcdh.uni-trier.de/en/">tcdh.uni-trier.de/en/</a>
 
 Licence: <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution (CC BY)</a>, 2020. 

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