Distant Reader - keyword-hubei-cord

Introduction

This is a Distant Reader "study carrel", a set of structured data intended to help the student, researcher, or scholar use & understand a corpus.

This study carrel was created on 2021-05-25 by Eric Morgan <emorgan@nd.edu>. The carrel was created using the Distant Reader cord process, and the input was the result of a query applied to a local mirror of CORD, a data set of scholarly articles on the topic of COVID-19. The actual query was: keywords:hubei. The results of this query were saved in a cache and transformed into a set of plain text files. All of the analysis -- "reading" -- has been done against these plain text files. For example, a short narrative report has been created. This Web page is a more verbose version of that report.

All study carrels are self-contained -- no Internet connection is necessary to use them. Download this carrel for offline reading. The carrel is made up of many subdirectories and data files. The manifest describes each one in greater detail.

Size

There are 28 item(s) in this carrel, and this carrel is 136,673 words long. Each item in your study carrel is, on average, 4,881 words long. If you dig deeper, then you might want to save yourself some time by reading a shorter item. On the other hand, if your desire is for more detail, then you might consider reading a longer item. The following charts illustrate the overall size of the carrel.

Readability

On a scale from 0 to 100, where 0 is very difficult and 100 is very easy, the documents have an average readability score of 57. Consequently, if you want to read something more simplistic, then consider a document with a higher score. If you want something more specialized, then consider something with a lower score. The following charts illustrate the overall readability of the carrel.

Word Frequencies

By merely counting & tabulating the frequency of individual words or phrases, you can begin to get an understanding of the carrel's "aboutness". Excluding "stop words", some of the more frequent words include:

hubei, cases, wuhan, china, covid, number, data, epidemic, cities, province, population, city, transmission, confirmed, study, outbreak, model, virus, patients, rate, coronavirus, time, i, health, spread, january, infection, novel, ncov, reported, also, infected, two, new, february, first, disease, control, may, analysis, day, days, outside, different, measures, sars, people, table, influenza, period

Using the three most frequent words, the three files containing all of those words the most are Human Mobility Restrictions and the Spread of the Novel Coronavirus (2019-nCoV) in China, Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China, and Prediction of Epidemic Spread of the 2019 Novel Coronavirus Driven by Spring Festival Transportation in China: A Population-Based Study.

The most frequent two-word phrases (bigrams) include:

hubei province, confirmed cases, novel coronavirus, outside hubei, floating population, public health, cities outside, incubation period, wuhan city, copyright holder, granted medrxiv, author funder, mainland china, death rate, wuhan lockdown, new cases, made available, international license, city population, population movement, college students, control measures, spring festival, coronavirus disease, travel restrictions, world health, health commission, total number, health organization, acute respiratory, transmission dynamics, mental health, reported cases, imported cases, national health, transmission rate, social distancing, reproduction number, infectious diseases, severe acute, epidemic spread, respiratory syndrome, bacterial pneumonia, official policies, human mobility, coronavirus pneumonia, infected cases, new coronavirus, ncov outbreak, doc id

And the three file that use all of the three most frequent phrases are Prediction of Epidemic Spread of the 2019 Novel Coronavirus Driven by Spring Festival Transportation in China: A Population-Based Study Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China, and Human Mobility Restrictions and the Spread of the Novel Coronavirus (2019-nCoV) in China.

While often deemed superficial or sophomoric, rudimentary frequencies and their associated "word clouds" can be quite insightful:

Keywords

Sets of keywords -- statistically significant words -- can be enumerated by comparing the relative frequency of words with the number of times the words appear in an entire corpus. Some of the most statistically significant keywords in the carrel include:

hubei, china, wuhan, covid-19, sars, province, january, february, turkey, student, population, piba, patient, hcv, hbv, epidemic, ecmo, city, child, chikv, case, ace2

And now word clouds really begin to shine:

Topic Modeling

Topic modeling is another popular approach to connoting the aboutness of a corpus. If the study carrel could be summed up in a single word, then that word might be hubei, and Attitudes towards Wildlife Consumption inside and outside Hubei Province, China, in Relation to the SARS and COVID-19 Outbreaks is most about that word.

If the study carrel could be summed up in three words ("topics") then those words and their significantly associated titles include:

If the study carrel could be summed up in five topics, and each topic were each denoted with three words, then those topics and their most significantly associated files would be:

cases, covid, number - Real-time estimation and prediction of mortality caused by COVID-19 with patient information based algorithm
china, wuhan, population -
wuhan, cities, city - Human Mobility Restrictions and the Spread of the Novel Coronavirus (2019-nCoV) in China
hubei, epidemic, students - Emotional “inflection point” in public health emergencies with the 2019 New Coronavirus Pneumonia (NCP) in China
cities, covid, concentration - Ambient nitrogen dioxide pollution and spread ability of COVID-19 in Chinese cities

Moreover, the totality of the study carrel's aboutness, can be visualized with the following pie chart:

Noun & Verbs

Through an analysis of your study carrel's parts-of-speech, you are able to answer question beyonds aboutness. For example, a list of the most frequent nouns helps you answer what questions; "What is discussed in this collection?":

cases, number, data, cities, epidemic, population, transmission, outbreak, model, rate, city, study, time, patients, virus, province, coronavirus, spread, infection, days, people, disease, period, measures, day, analysis, health, lockdown, control, results, preprint, infections, effect, risk, level, date, regions, deaths, provinces, pneumonia, death, case, license, wildlife, effects, characteristics, participants, factors, individuals, quarantine

An enumeration of the verbs helps you learn what actions take place in a text or what the things in the text do. Very frequently, the most common lemmatized verbs are "be", "have", and "do"; the more interesting verbs usually occur further down the list of frequencies:

used, confirmed, reported, showed, estimate, including, based, found, indicate, increased, predicted, reduced, infected, caused, made, floating, take, spread, followed, provided, considering, displayed, collected, controlling, compared, suggest, identified, decreased, calculated, affected, grant, associated, analyze, reviewed, obtained, implemented, seen, according, eaten, imported, exposed, excluding, became, prevent, presented, given, assumed, represents, led, need

Proper Nouns

An extraction of proper nouns helps you determine the names of people and places in your study carrel.

Hubei, Wuhan, China, COVID-19, January, February, Province, SARS, Fig, Health, Table, Coronavirus, National, City, March, B, ND, CC, BY, Spring, World, β, Festival, Novel, Organization, Henan, Commission, CoV-2, C, Jiangxi, HCV, Shanghai, NC, HBV, PIBA, Turkey, Chinese, medRxiv, i, New, J, December, Zhang, Treat, SEIR, Panel, Baidu, People, Li, Beijing

An analysis of personal pronouns enables you to answer at least two questions: 1) "What, if any, is the overall gender of my study carrel?", and 2) "To what degree are the texts in my study carrel self-centered versus inclusive?"

we, it, our, i, their, they, its, them, us, he, you, itself, his, your, themselves, my, she, s, one, yjay3t38, her, -1840

Below are words cloud of your study carrel's proper & personal pronouns.

Adjectives & Verbs

Learning about a corpus's adjectives and adverbs helps you answer how questions: "How are things described and how are things done?" An analysis of adjectives and adverbs also points to a corpus's overall sentiment. "In general, is my study carrel positive or negative?"

different, new, covid-19, first, novel, public, daily, higher, infected, local, human, high, chinese, infectious, early, available, significant, total, severe, clinical, second, medical, international, epidemiological, many, cumulative, respiratory, large, social, potential, city, important, non, average, official, negative, previous, basic, positive, relative, inter, actual, susceptible, initial, similar, effective, global, lower, third, several

also, respectively, therefore, however, significantly, still, well, first, even, approximately, especially, furthermore, much, moreover, rapidly, mainly, recently, highly, gradually, directly, relatively, namely, generally, statistically, quickly, officially, effectively, particularly, nearly, worldwide, second, similarly, later, initially, specifically, just, finally, together, back, separately, nt, now, newly, meanwhile, currently, besides, previously, overall, greatly, almost

Next steps

There is much more to a study carrel than the things outlined above. Use this page's menubar to navigate and explore in more detail. There you will find additional features & functions including: ngrams, parts-of-speech, grammars, named entities, topic modeling, a simple search interface, etc.

Again, study carrels are self-contained. Download this carrel for offline viewing and use.

Thank you for using the Distant Reader.