Acknowledgements

Institutions

• Northeastern University/Rice University Cultural Observatory

People

• Erez Lieberman Aiden
• Neva Cherniavsky, Martin Camacho, Matt Nicklay, Billy Janitsch, JB Michel.

Acknowledgements

Funders

• Digital Public Library of America
• Harvard Cultural Observatory
• National Endowment for the Humanities

• github.com/Bookworm-project
• bookworm.culturomics.org
• benschmidt.org/moosehead

Textual Metadata: Correspondence Networks

Mapping the Republic of Letters

Textual Metadata: Trial Lengths

Data Mining with Criminal Intent/The Old Bailey Online

ICOADS Deck 701, US Maury Collection (1789-c.1865) ICOADS

ICOADS Deck 735, Russian Research Vessel (R/V) Digitization

Bookworm Open Library

1 million books, 91 billion words

Some Bookworm instances at Northeastern and Rice:

• Science articles:

  bookworm.culturomics.org/arxiv

• Movies and television:

  movies.benschmidt.org

• Historical Newspapers: bookworm.culturomics.org/ChronAm

Mentions of US Presidents in Ngrams and the Chronicling America Bookworm

Yale University: full run of Vogue Magazine

http://bookworm.library.yale.edu/collections/vogue/

Medical Heritage Library (30,000 medical books)

US State Department

Hathi Trust

http://sandbox.htrc.illinois.edu/bookworm/

How do digital libraries expose their texts?

• Search engines
• Full-text dumps

Bookworm Core Philosophy--infrastructures

• Text curators can enable new uses by sharing new forms.
• Large text collections share fundamental problems of presentation.
• Sharing presentation forms will improve them.
• Data visualization should be a primary output.

A single backend can drive multiple representations.

"Focusing Attention"

Bookworm Core Philosophy--data structures

• Even humanists who don't know what it is will benefit from an expressive way to describe text analysis at the very large scale.
• Metadata alone can enable most uses in the humanities.
• What we think of as textual content should be reconceived of as more metadata.
• Comparisons across sets is the most important element.

Why a grammar?

• Forces restrictions on goals.
• Avoids platform dependence.
• Easy integration with other grammatical platforms.

Grammar, part I: Creating a corpus

"search_limits": {
    "publish_country":["United States"],
   "year": {
      "$lte": 1920,
      "$gte": 1890
   },

Grammar, Part II: Defining Text groups and tokenizations.

   {
   "search_limits": {
      "word": ["need to"]
    },
   "database":"movies",
   "aesthetic":{"x":"MovieYear","y":"WordsPerMillion"},
   "plotType":"linechart"
   }

Exams and essays

{"database":"RMP",
    "plotType":"pointchart",
    "compare_limits":{"word":["test","tests","exam","exams","quiz","quizzes"],
        "department__id":{"$lte":20}},
    "search_limits":{"word":["essay","essays","paper","papers"],
        "department__id":{"$lte":20}},
    "aesthetic":{"x":"WordsRatio","y":"department","color":"gender"}
}

A new interactive site: Student assessments and gender prejudices

Grammar, Part II: Defining texts and tokens

{
"plotType": "heatmap",
"search_limits": {
   "word": ["concentrate attention"],"year":{"$lte":1922,"$gte":1850}
},
"aesthetic":{"color":"WordsPerMillion","x":"decade","y":"BenSubject"},
"database":"presidio"
}

Grammar, Part II: Defining texts and tokens

Time interactions.

Bicycles

Library composition

{"search_limits":{
    "publish_country":["United States"],
    "year": {
      "$lte": 1920,
      "$gte": 1890
    },
    "word":["natural selection"]
},

"compare_limits":{
    "publish_country":["United States"],
    "year": {
      "$lte": 1920,
      "$gte": 1890
    }
}

"counttype":["WordsPerMillion"]

Statistics returnable from comparing text and token counts across two collections:

1. Percentage of Texts
2. Uses per million words
3. Average length of books.

Statistics returnable from comparing text and token counts across two collections:

1. Percentage of Texts
2. Uses per million words
3. Average length of books.

4. TF-IDF
5. Dunning Log-likelihood.
6. ???

Dunning-characteristic words by gender in negative reviews of history professors, Ratemyprofessor.com

Dunning-characteristic words by gender in negative reviews of history professors, Ratemyprofessor.com

Differing language in two TV shows

{"database": "movies",
"plotType": "wordcloud",
"search_limits": {
   "MovieYear":[2009],"medium":"TV show"
},
"compare_limits": {
   "MovieYear":[2009],"medium":"movie"
},
"aesthetic": {
   "label": "unigram",
   "size": "Dunning"
}}

Grammar part 3, alternate:

Instead of comparing, returning results.

Simplest Extensions:

1. Lemmatization
2. Random divisions and bootstrapping.
3. Conversion to standard vocabulary.

How Humanists use topic models badly:

1. Only perfunctory efforts to link back into other forms of metadata.
2. Straightforward use as dimensionality-reduction without spot checks.
3. Assumption of stability in a single topic's composition across time, genre, etc.

Differing language in two TV shows

Topics as tokens

{
"database": "movies",
"plotType": "wordcloud",
"search_limits": {
  "TV_show": ["Seinfeld"]
},
"compare_limits": {
  "TV_show": ["Cheers"]
},
"aesthetic": {
  "label": "topic_label",
  "size": "Dunning"
}}

Differing language in two TV shows

Comparing in topic vocabulary:

{"database":"movies","plotType":"wordcloud",
    "search_limits":{
        "TV_show":["The Wire"],
        "topic_label":["fucking shit fuck Fuck Shit ass bitch"]},
    "compare_limits":{
        "TV_show":["Deadwood"],"topic_label":["fucking shit fuck Fuck Shit ass bitch"]},
    "aesthetic":{"label":"unigram","size":"Dunning"}
}

Deadwood and the Wire, Topics

An interactive tool for exploring changing topic composition

Text Encoding Initiative produces micro-texts

In Space and time

{"database": "ChronAm",
"plotType": "map",
"method": "return_json",
"search_limits": {},
"aesthetic": {
  "point": "placeOfPublication_geo",
  "size": "TextCount",
  "time": "publish_year"}}

Newspaper flu coverage, 1917-1919

{"database": "ChronAm",
"plotType": "map",
"method": "return_json",
"search_limits": {"word":["flu","influenza","pneumonia"],
           "publish_year":{"$lte":1920,"$gte":1918}},
"smoothingSpan":25,
"aesthetic": {
    "time":"publish_day",
  "point": "placeOfPublication_geo",
  "size": "TextPercent"}}

Viral Topics

{"database": "viral",
"plotType": "map",
"method": "return_json",
"search_limits": {
  "topic": {
    "$gte": 10
  }
},
"aesthetic": {
  "point": "placeOfPublication_geo",
  "size": "WordCount",
  "time": "topic",
  "label": "topic_label"}}

What does this tell us that we haven't seen already?

• What do you mean by "us," kemosabe?
• Evidence without source criticism is increasingly problematic
• Allows sharing and elaboration of evidence
• Allows new strategies of analysis for metadata
• Evidence of Absence
• Comparison of tools for inference.

Code: github.com/bookworm-project

Docs: http://bookworm-project.github.io/Docs

Plugins: github.com/benmschmidt/Bookworm-Mallet

Project Description: bookworm.culturomics.org

Hathi Browser: http://sandbox.htrc.illinois.edu/bookworm

Ben Schmidt: benschmidt.org

These slides: benschmidt.org/slides/2014-11-12_Bookworm