Increasing digitalization is associated with anxiety and depression: A Google Ngram analysis. by Gisbert Wilhelm Teepe, Edda Magareta Glase, Ulf-Dietrich Reips

“…We extend related work by using the Google Books Ngram Viewer (Google Ngram) to retrieve and adjust word frequencies from a large corpus of books (8 million books or 6 percent of all books ever published) and to subsequently investigate word changes in terms of anxiety disorders, depression, and digitalization. …”
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Exploring mega-corpora: Google Ngram Viewer and the Corpus of Historical American English by Eric Friginal, Marsha Walker, Janet Beth Randall

“…The creation of internet-based mega-corpora such as the Corpus of Contemporary American English (COCA), the Corpus of Historical American English (COHA) (Davies, 2011a) and the Google Ngram Viewer (Cohen, 2010) signals a new phase in corpus-based research that provides both novice and expert researchers immediate access to a variety of online texts and time-coded data. …”
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Les traces d’Ismaël Boulliau dans la poussière de l’histoire by Maguire, Conor

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Les traces de Denis Papin dans la poussière de l’histoire by Maguire, Conor

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Information Potential of a Corpus of Scientific Texts by V. N. Komaritsa

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What Loanwords Tell Us about Spanish (and Spain) by Adriana Soto-Corominas, Javier De la Rosa, Juan Luis Suárez

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Bibliometric and Trend Analysis of Budget Transparency by Molotok Ihor Fedorovych

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Semantic and lexical specifics of the emotives, nominating sadness in English by M. I. Andreeva, D. Z. Gaynutdinova, A. T. Galiakhmetova

“…Further, we focused on the combinatorial features of the emotives based on the British National Corpus texts and Google NGram viewer tool. The verbs be, feel, get are more common to collocate with the emotives under study. …”
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Benford's Law applies to word frequency rank in English, German, French, Spanish, and Italian. by Jennifer Golbeck

“…We calculated the frequency rank of words in the Google Ngram Viewer corpora. Then, using the first significant digit of the frequency rank, we found the FSD distribution adhered to the expected Benford's Law distribution. …”
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The role of semantics, pre-emption and skew in linguistic distributions: the case of the un-construction. by Paul eIbbotson

“…We use the Google Ngram database, a corpus of 5,195,769 digitized books containing ~4% of all books ever published, to test three ideas that are hypothesized to account for linguistic generalizations: verbal semantics, pre-emption and skew. …”
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Examining trends in epidemic typhus using historical texts by Mark David Walker

“…The extent of the problem it caused is uncertain as record keeping in the most affected socioeconomic groups was rare. Google Ngram Viewer details the frequency of word usage in written language over time. …”
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Scaling laws and fluctuations in the statistics of word frequencies by Martin Gerlach, Eduardo G Altmann

“…Analyzing the fluctuations around this average in three large databases (Google-ngram, English Wikipedia, and a collection of scientific articles), we find that the standard deviation scales linearly with the average (Taylorʼs law), in contrast to the prediction of decaying fluctuations obtained using simple sampling arguments. …”
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Global university reputation and rankings: insights from culturomics by KI Stergiou, AC Tsikliras

“…In particular, we used the Google Ngram viewer (available online: http://books.google.com/ngrams) to produce the frequencies of the names of 13 US, 5 UK and 4 Canadian universities in the English books and examined how these frequencies changed with time (1800 to 2008). …”
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Chinese, Japanese and Korean-inspired culinary words in the English language by Kiaer, Jieun, Calway, Niamh, Ahn, Hyejeong

“…It investigates the path of culinary terms into popular usage using Google Ngram, and Google Trends. It also takes an in-depth look at social media to discover the impact such technology has on the adoption of Asian culinary terms and further investigates this phenomenon by conducting a survey of 297 L1 and L2 English speakers, mainly from the United Kingdom. …”
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Moral Conformity through the Lens of Bibliometric and Content Analysis by Marina V. Zlobina, Alexandr A. Fedorov

“…The materials under study were publications in English extracted from the Scopus and Google Scholar databases as well as Google Ngram data. The data were processed using the VOSviewer program and the bibliometrix R-package. …”
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A Serious Matter: Using Computational Text Analysis to Examine Tech CEOs’ Communications About Artificial Intelligence Compared to Tech CEO and Select American Orators’ Communicati... by Terrence Greer

“…I utilized various R packages to process the corpus, and ultimately conducted sentiment analyses, topic-modeling, and word frequency analyses. I also used Google Ngram Viewer to study the identified topics in a corpus of 8 million books. …”
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The most famous fish: human relationships with fish as inferred from the corpus of online English books (1800-2000) by KI Stergiou

“…has never been asked. I used Google Ngram viewer to estimate the frequency of times the common names of 250 fishes appear in the corpus of digitized English books published between 1800 and 2000. …”
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INTERNATIONAL CONVERGENCE OF FINANCIAL REPORTING by Daria Holovina, Olga Karpenko, Iryna Plikus

“…We analyzed the intensity of the use of the concepts of IFRS and GAAP, which are associated precisely with the convergence of these accounting systems using the Google Ngram Viewer (GNV) tool. We have proved that the Convergence of IFRS and GAAP also applies to Ukraine, since the convergence is bilateral, any change in IFRS will ultimately affect the Ukrainian accounting and reporting system.…”
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Updatism: Gumbrecht’s broad present, Hartog’s Presentism and beyond by Mateus PEREIRA, Valdei ARAUJO

“…We argue that certain aspects of the present time can be derived from Heidegger`s descriptions of the temporality of “opening” (Erschlossenheit), in particular from the “inauthentic” or “improper” dimension. On the basis of Google Ngram and other databases, we identify the growth of the semantic field around the word “update” since the early nineteen-sixties. …”
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Information extraction from bibliography data by Leong, Kai Ling

“…Additional data were queried from Google Ngram Viewer as a second set of topics trends and represent the general audience. …”
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