What is the Distant Reader and why should I care?

The Distant Reader is a tool for reading. [1]

wall paper by eric

Wall Paper by Eric

The Distant Reader takes an arbitrary amount of unstructured data (text) as input, and it outputs sets of structured data for analysis — reading. Given a corpus of any size, the Distant Reader will analyze the corpus, and it will output a myriad of reports enabling you to use & understand the corpus. The Distant Reader is intended to supplement the traditional reading process.

The Distant Reader empowers one to use & understand large amounts of textual information both quickly & easily. For example, the Distant Reader can consume the entire issue of a scholarly journal, the complete works of a given author, or the content found at the other end of an arbitrarily long list of URLs. Thus, the Distant Reader is akin to a book’s table-of-contents or back-of-the-book index but at scale. It simplifies the process of identifying trends & anomalies in a corpus, and then it enables a person to further investigate those trends & anomalies.

The Distant Reader is designed to “read” everything from a single item to a corpus of thousand’s of items. It is intended for the undergraduate student who wants to read the whole of their course work in a given class, the graduate student who needs to read hundreds (thousands) of items for their thesis or dissertation, the scientist who wants to review the literature, or the humanist who wants to characterize a genre.

How it works

The Distant Reader takes five different forms of input:

  1. a URL – good for blogs, single journal articles, or long reports
  2. a list of URLs – the most scalable, but creating the list can be problematic
  3. a file – good for that long PDF document on your computer
  4. a zip file – the zip file can contain just about any number of files from your computer
  5. a zip file plus a metadata file – with the metadata file, the reader’s analysis is more complete

Once the input is provided, the Distant Reader creates a cache — a collection of all the desired content. This is done via the input or by crawling the ‘Net. Once the cache is collected, each & every document is transformed into plain text, and along the way basic bibliographic information is extracted. The next step is analysis against the plain text. This includes rudimentary counts & tabulations of ngrams, the computation of readability scores & keywords, basic topic modeling, parts-of-speech & named entity extraction, summarization, and the creation of a semantic index. All of these analyses are manifested as tab-delimited files and distilled into a single relational database file. After the analysis is complete, two reports are generated: 1) a simple plain text file which is very tabular, and 2) a set of HTML files which are more narrative and graphical. Finally, everything that has been accumulated & generated is compressed into a single zip file for downloading. This zip file is affectionately called a “study carrel“. It is completely self-contained and includes all of the data necessary for more in-depth analysis.

What it does

The Distant Reader supplements the traditional reading process. It does this in the way of traditional reading apparatus (tables of content, back-of-book indexes, page numbers, etc), but it does it more specifically and at scale.

Put another way, the Distant Reader can answer a myriad of questions about individual items or the corpus as a whole. Such questions are not readily apparent through traditional reading. Examples include but are not limited to:

  • How big is the corpus, and how does its size compare to other corpora?
  • How difficult (scholarly) is the corpus?
  • What words or phrases are used frequently and infrequently?
  • What statistically significant words characterize the corpus?
  • Are there latent themes in the corpus, and if so, then what are they and how do they change over both time and place?
  • How do any latent themes compare to basic characteristics of each item in the corpus (author, genre, date, type, location, etc.)?
  • What is discussed in the corpus (nouns)?
  • What actions take place in the corpus (verbs)?
  • How are those things and actions described (adjectives and adverbs)?
  • What is the tone or “sentiment” of the corpus?
  • How are the things represented by nouns, verbs, and adjective related?
  • Who is mentioned in the corpus, how frequently, and where?
  • What places are mentioned in the corpus, how frequently, and where?

People who use the Distant Reader look at the reports it generates, and they often say, “That’s interesting!” This is because it highlights characteristics of the corpus which are not readily apparent. If you were asked what a particular corpus was about or what are the names of people mentioned in the corpus, then you might answer with a couple of sentences or a few names, but with the Distant Reader you would be able to be more thorough with your answer.

The questions outlined above are not necessarily apropos to every student, researcher, or scholar, but the answers to many of these questions will lead to other, more specific questions. Many of those questions can be answered directly or indirectly through further analysis of the structured data provided in the study carrel. For example, each & every feature of each & every sentence of each & every item in the corpus has been saved in a relational database file. By querying the database, the student can extract every sentence with a given word or matching a given grammer to answer a question such as “How was the king described before & after the civil war?” or “How did this paper’s influence change over time?”

A lot of natural language processing requires pre-processing, and the Distant Reader does this work automatically. For example, collections need to be created, and they need to be transformed into plain text. The text will then be evaluated in terms of parts-of-speech and named-entities. Analysis is then done on the results. This analysis may be as simple as the use of concordance or as complex as the application of machine learning. The Distant Reader “primes the pump” for this sort of work because all the raw data is already in the study carrel. The Distant Reader is not intended to be used alone. It is intended to be used in conjunction with other tools, everything from a plain text editor, to a spreadsheet, to database, to topic modelers, to classifiers, to visualization tools.


I don’t know about you, but now-a-days I can find plenty of scholarly & authoritative content. My problem is not one of discovery but instead one of comprehension. How do I make sense of all the content I find? The Distant Reader is intended to address this question by making observations against a corpus and providing tools for interpreting the results.


[1] Distant Reader – https://distantreader.org

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