Our Story and Our Results

They say the humanities are in "crisis." Society values the sciences more highly. Students turn to other majors promising apparently predictable paths to jobs. In recessions, universities and colleges cut humanities programs first. And funding support for the humanities continues to be a rounding error in the national budget. Decisively, they say, the humanities are in crisis. (See WE1S bibliography of "humanities crisis.")



"Crisis" (etymology originating in Greek for "discrimination, decision" and "to decide")




Cover of Humanities Indicators 2020 report

Report of the Humanities Indicators Project of the American Academy of Arts & Sciences on The Humanities in American Life (2020).

Yet in 2019 when the American Academy of Arts and Science’s Humanities Indicators project surveyed Americans’ experience and views of the humanities, the top takeaway was that there was "considerable agreement about the personal and societal benefits of the humanities, substantial engagement with a variety of humanities activities at home and in the workplace, and strong support for teaching humanities subjects in the schools."

Also: "relatively few Americans agree with a variety of negative statements about the field" (See The Humanities in American Life, p. 3.).

That’s also what they say.

So what does everyone actually say about the humanities? With what nuance, in what contexts, and in the company of what other, even contrasting views? And who are "they" anyway?




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We read the media . . .




Newspaper bundles





See Our Research


Our WhatEvery1Says (WE1S) project studied these questions by taking an approach complementing, but different from, surveying (though we did some surveying too). We read the media.

In particular, we used computational methods to help us study vast numbers of articles and social media posts mentioning the humanities.

What everyone says about the humanities or anything else in the media, after all, is different than what anyone says one-on-one. The media is an echo chamber producing storylines through common, but crowded, rhetorical scenarios—such as when a politician interviewed for a story addresses the generic taxpayer (or parent) in hopes of reaching a partisan voter. The apparently common wisdom that results from such rhetorical tangles is a media wisdom, a repertoire of memes that take on a life of their own and are hard to change because the myriad push-pull relations among all the stakeholders often lock winning memes in place. (See WE1S bibliography of "Journalism & Media")

Funded by $1.1 million from the Andrew W. Mellon Foundation, we gathered over 1 million (1,028,629) unique English-language journalistic media articles and related documents mentioning the literal word "humanities" (and for some research purposes also the "liberal arts," "the arts," and "sciences") from 1,053 U.S. and 437 international news and other sources from the 1980s through 2019, mostly after 2000 when news media began producing digital texts en masse. For comparison and other uses, we also gathered about 1.38 million unique documents representing a random sampling of all news articles. In addition, we harvested over 6 million posts mentioning the "humanities" and related terms from social media (about 5 million from Twitter and 1 million from Reddit). (See our Research Materials.)

We focused on the U.S. to start with a manageable scope of materials, and also because our researchers—based at the partner institutions of U. California, Santa Barbara; California State U., Northridge; and U. Miami—have a poorer understanding of the social, economic, political, and cultural contexts of the media and the humanities in other nations. (We invite others to use our soon to be released materials (data and topic models on the media we collected), methods, and tools and software to extend our research internationally.)




WE1S GeoD visualization of WE1S Collection 1 (U.S. news media mentioning

WE1S GeoD visualization of WE1S Collection 1 (U.S. news media mentioning "humanities," c. 1989-2019)

Why search for documents containing the literal word "humanities" and related words? We did so not because we think these terms by themselves cast a net over all the humanities. In the vast sea of public discourse, the humanities also appear under the names of "literature," "history," or other specific kinds; and the humanities are name-dropped everywhere in discussions of particular people, books, intellectual movements, controversies, organizations, events, and so on. The humanities are also surprisingly pervasive in media coverage of common life and death events—for example, in wedding announcements and the obituaries of everyday (not just famous) people. There is no predefined, bounded set of media documents for studying public discussion of the humanities.

So we aimed for a strategically chosen subset of materials mentioning the literal word "humanities" (plus sometimes other terms) on the hypothesis that we could thus capture a good swathe of examples on both sides of the line between specialized academic discourse about the humanities (as when universities call a group of disciplines "the humanities") and wider, fuzzier public discussion of the humanities (as when "humanities" comes up in relation to words like "literature," "history," and people, books, organizations, etc.).






Humanities & Liberal Arts

WE1S Collection 1


WE1S Collection 1 (C-1): U.S. News Media (A collection of word-frequency and other data representing 82,324 unique articles mentioning "humanities" published mostly during 1989-2019 in 850 U.S. news sources and their associated blogs.)

Card describing and providing access to data and topic models for WE1S Collection 1 (C-1): U.S. News Media (A collection of word-frequency and other data representing 82,324 articles mentioning "humanities" published mostly during 1989-2019 in 850 U.S. news sources and their associated blogs.) See other WE1S collections.

Then we sliced-and-diced our corpus (whole body of materials) into over thirty “collections,” each of which grouped documents in different ways to facilitate asking different research questions. Our core study collection (Collection 1) consists of about 83,000 articles and other texts mentioning the “humanities” from mainstream and local U.S. newspapers between c. 1989 and 2019. By contrast, for example, Collection 4 includes articles only from 15 top-circulation U.S. newspapers; Collection 14 restricts materials to U.S. college or university student newspapers; Collection 21 includes articles from top-circulation U.S. newspapers mentioning either “humanities” or “science”; Collection 29 includes Twitter posts between 2014 and 2017 containing "humanities" (aggregated by author); and Collection 38 includes Reddits longer than 225 words from 2006 to 2019 containing "humanities," "liberal arts," or "the arts." (See the metadata tags we also add to our data to help analyze groups of publication sources.)

We feature nineteen of these collections on our Collections & Topic Models page, where we describe each and make them available for other researchers in the form of their word frequency counts, computational models and visualizations, and other derived or generated data (but, unfortunately, no readable text due to copyright or licensing constraints except for texts we directly gathered from public and open sources).

So that other researchers can fully benefit from the data we harvested and processed from the materials we studied, we deposited our full datasets in the Zenodo repository operated by CERN to promote "open science." These datasets (six in total) hold all the data and metadata from which our "collections" were extracted for modeling. For example, Collection 1 is a subset of data from our humanities_keywords dataset. (See our Datasets page for more information and access to our Zenodo deposits.)


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Topic modeling helps us find major themes . . .




Graphic of human looking through telescope at a mass of newspaper front pages

But how did we "read" hundreds of thousands of newspaper articles or millions of social media posts at a time in any of our collections to understand "what everyone says" about the humanities?

We used a method of computational machine learning called “topic modeling.” Topic modeling helps us find major themes of discussion in the media, learn their relative importance, and identify specific articles and sources highly associated with those themes. Widely used in the sciences, social sciences, and recently the humanities (in the new field of “digital humanities”), topic modeling is one of the ways computers can now assist humans in understanding large collections of texts and other materials.

(See WE1S 1-page explanation and bibliography of topic modeling. See also WE1S bibliography of "digital humanities.")

What's a Topic Model?


thumbnail of M-2 (Topic Modeling)

One-page explanation of topic modeling, with references to fuller explanations.


A WE1S Topic Model
Collection 1 in WE1S TopicBubbles visualization

Topic Model of WE1S Collection 1 at the granularity of 50 topics (in WE1S TopicBubbles visualization)

Topic Model of WE1S Collection 1 at the granularity of 50 topics (visualized in WE1S TopicBubbles interface). (See the interactive visualization.)

Topic modeling is an unsupervised machine learning method, meaning that it does not presume that a researcher already has a hypothesis or result in mind that “supervises” what the computer is asked to find by prioritizing results that match a predefined example set.

Topic modeling works by discovering in large numbers of texts what appear to humans to be thematically coherent “topics.” It does so by analyzing the statistical co-occurrence of words. That is, it finds out which words tend to appear together in a set of documents (and in individual texts).

An article about a political election, for example, might frequently use words naming a nation’s capital city (e.g., "London”) together with such other words as "Parliament." In a topic model, a list of the most frequent words in such a co-occurring group of words can often suggest that the articles sharing many of such words participate together in a "topic"—in this case, one that might be labeled something like "British government."

Topic modeling not only identifies topical themes but indicates their relative weights. (For example, a topic about economics might be twenty times as important than one about the humanities in a particular collection.)

It also shows which specific documents participate strongly in a topic (or in several topics at once, since an article mentioning "London" might talk partly about politics but also partly about economics because London is also a financial capital).

Topic modeling thus not only helps find gross patterns but guides human readers to specific examples of documents to examine closely because of their statistical association with a topic. Such guidance is an improvement on cherry-picking examples anecdotally or because they fit a researcher’s preconceived "hunch" about what is going on.

Want to use our collections & software?

Materials: Our collections can be downloaded as “bags of words.” (This means that except for open-access documents or social media, we are constrained by copyright to sharing original documents only as word frequency counts, which can be used for computational modeling methods such as topic modeling but not for ordinary human reading). [Materials are soon to be released.]

Tools: You can download our Workspace of Jupyter notebooks and other tools for performing topic modeling our texts (or your own) and then for visualizing models in the interactive interfaces of our Topic Model Observatory. The Workspace is available in a Docker “containerized” form so that you can reproduce everything on your own computer and run it. We also make available our Topic Model Interpretation Protocol—standard instructions and observation steps for researchers using topic models. [Tools are soon to be released.]


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We added an up-close human touch . . .

Topic Model Observatory


WE1S Topic Model Observatory (adapted and original visualization interfaces for the interactive exploration of topic models)

WE1S Topic Model Observatory—adapted and original visualization interfaces for the interactive exploration of topic models. (See our "Guide" to the observatory.) The "observatory" is an apt metaphor for computational "distant reading."

However, computational machine-learning methods like topic modeling (and such other methods as supervised text classification and "word embedding" we also used in a supplementary way) have been called “distant reading” for a reason.

While topic modeling may guide humans to particular articles for “close reading,” there is still an inescapable sense for humans of great distance---as if seeing things from outer space, because that is how far away we have to go to see “what everyone says.”

That's why we called our suite of adapted and original interactive visualization interfaces for exploring data models our "Topic Model Observatory."

So besides closely reading articles that our topic models pointed us to, we added an up-close human touch. We surveyed students and others, and met them in focus groups in which we discussed their views of the humanities.

Our “human subjects research”—as it is called by the “institutional review boards” at our universities, which for ethical reasons must approve research involving people—was small-scale and local by comparison with the Humanities Indicators U.S. survey. We conducted it at two of our three campuses partnering on the WE1S project: U. California, Santa Barbara, and U. Miami.

But the human subjects research was illuminating. The purpose was to give us a gut check on what the media says about the humanities. We needed to hear what students and others near us were saying. Our local and small-scale results helped us better understand the wider media discourse by seeing the humanities from various specific perspectives—for example, the student viewpoint (including that of our project institutions' many first-generation college or first-generation immigrant students).




WE1S human subjects research page, including overview and key findings

WE1S “human subjects” research grounds big data study in our own communities, inspiring local actions that can be scaled and adapted. (Go to our human subjects web page, which provides an overview and key findings.


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See our key findings . . .





Collage of some of the one-page cards reporting on WE1S project's "Key Findings".

Some of the one-page cards reporting on WE1S project's "Key Findings." (See all our Key Findings to date.)

So what did we find?

WE1S's key findings are presented in our "cards" reporting system. These are one-page explanations in public (instead of academic) language that include summaries, examples, and links to our underlying research as well as other resources.

See our Key Findings
(More to be added as research continues.)

Selected key findings include:

  • The Humanities "Crisis"
  • Value of the Humanities
    • The media assesses the value of the humanities both economically and intrinsically -- but the Great Recession changed the balance of the discussion (KF-4-1)
    • Paradoxically, the media both promotes and obscures the value of humanities labor (KF-4-10)
  • Broader Profile of the Humanities in Society
    • “History” gains more traction in public media discourse outside the academy compared to literature and philosophy (KF-5-10)
    • Literature has a split personality in the media. It is tied to the academy, yet bridges to public life in everyday book & reading events (KF-5-11)
  • Humanities and Social Groups
    • Public discourse as represented in the media treats humanists as authorities on cultural discourses about race, racism, and ethics. (KF-3-1)
    • In both mainstream and student journalism, universities appear to be dominated by liberals in ways that impair intellectual diversity (KF-4-2)
    • Contributions of the academic humanities to thought on gender and sexual identity have little impact on media coverage of these issues (KF-3-5)
  • Humanities and Science in the Media
    • The humanities appear to the public to be siloed in universities (unlike the sciences) (KF-5-2)
    • The public likes to take its science with objects (and the humanities have none) (KF-5-4)
    • The media hides core values of science behind a veil of “data-speak” (KF-5-5)
    • The sciences stand out for the public as distinct fields (unlike the humanities, which blur together as just “academics”) (KF-5-7)
    • Student journalists see positive opportunities for interdisciplinary collaboration between the sciences and the humanities (KF-8-5)
  • Students and the Humanities
    • Students Writing about Education Value Cognitive Flexibility and “Soft Skills” Associated with the Humanities and Liberal Arts (KF-8-1)
    • In student newspapers, parents appear to be supportive instead of demanding (KF-8-7)
      Students writing in campus newspapers argue that the humanities help us engage meaningfully with human nature and society. (KF-4-5)
    • Students imagine a bleak future without the humanities (survey result) (KF-8-9)
      Students are twice as likely to think others stereotype them based on their majors than that they stereotype others (survey result) (KF-8-2)
    • Students’ personal and academic commitments to the humanities don’t always align; extracurriculars provide flexible space for engagement (survey result) (KF-8-3)
  • Humanities and Social Media
    • Reddit is a rich, robust forum for discussion of the humanities providing an unfiltered alternative to published media (KF-6-6)
    • Twitter engagement with "humanities" is mostly academic in nature (KF-6-2)
  • The Humanities and Ordinary Life
    • The humanities are the art of ordinary life (KF-5-3)
  • Humanities Funding
    • The media covers only a fraction of the humanities areas the NEH was established by the U.S. Congress to support (KF-7-2)
    • The media covers funding for humanities research, but pays almost no attention to funding for humanities teaching (KF-7-4)
    • Humanities funding for K-12 primary and high-school education receives the least media coverage (KF-7-3)


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Here are our recommendations . . .

Ultimately, how might we better understand public discourse with the dual goal of helping society benefit from the special values of the humanities and the humanities themselves communicate and evolve their role in society? Such is what it would mean to be "4Humanities" and to advocate for society too.

And such is what it would mean to teach the humanities too in ways that innovatively weave together their values and practices with those of other areas of education and broader society. (See the examples of courses we developed and our rich stream of blog posts about them in our Curriculum Lab.)

Here are our recommendations (still in-progress). We also make them in the form of one-page "cards": Calls to Action and Calls to Communication cards.

The full potential of our "cards" reporting system is realized in the Research-to-Action Toolkits we are beginning to build, which—snap together our modular cards of different kinds (for collections, methods, tools, key findings, calls to action, and calls to communication). These are kits for understanding and advocating the vital role of the humanities in today's world.

We invite others to use our data and tools (or to use our tools on other materials) to expand our grasp of "what everyone says" about the humanities, and to add to our findings and recommendations for ensuring that the humanities go forward as full partners in society—past, present, and future.




WE1S is a 4Humanities project




Examples of WE1S recommendations in the form of action cards
Examples of WE1S recommendations in the form of action cards.


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A networked view of our resources in their interrelationships (selected).