Search

Empirical aesthetics is associated with two research questions: How the mind generally assigns value to sensory stimuli and how it responds specifically to art objects. Researchers have debated whether these phenomena share enough to warrant being collapsed into a single field. To ask how these particular questions came to be associated with aesthetics, we conducted Google Ngram analyses over a corpus of books spanning two centuries. Analyses trace the frequency of “big questions” about art and beauty, and how the term aesthetic appears relative to other concepts. Results indicate the 19th century was dominated by notions of beauty and an aesthetic sense. Questions about art and aesthetic experience become more frequent during the 20th century. Results are interpreted with respect to associated affective and evaluative concepts, art movements, and scientific debates. Understanding how aesthetics is used over time can cast light on the ways current work is being conceived and pursued.

BackgroundAt the time of the COVID-19 epidemic, it is useful to look at what lessons (digital) history can give us about the past pandemics and dealing with them. We show that the Google Ngram (GNV) can discover hidden patterns in history and, therefore, can be used as a window into history. By using the approach of Digital Humanities, we analysed the epidemiological literature on the development of the Russian flu pandemic for hints on how the COVID-19 might develop in the following years.ObjectiveOur study is searching for evidence that the COVID-19 is not a unique phenomenon in human history. We are testing the hypothesis that the flu-like illness that caused loss of taste and smell in the late 19th century (Russian flu) was caused by a coronavirus. We are aware that it is difficult to formulate a hypothesis for a microbiological aetiology of a pandemic that occurred 133 years ago. But differentiating an influenza virus infection from a COVID-19 patient purely on the clinical ground is difficult for a physician because the symptoms overlap. The most crucial observation of similarities between the Russian flu pandemic and COVID-19 is the loss of smell and taste (anosmia and ageusia). The objective was to calculate the ratio of increasing to decreasing trends in the changes in frequencies of the selected words representing symptoms of the Russian flu and COVID-19.MethodsThe primary methodological concept of our approach is to analyse the ratio of increasing to decreasing trends in the changes in frequencies of the selected words representing symptoms of the Russian flu and COVID-19 with the Google NGram analytical tool. Initially, keywords were chosen that are specific and common for the Russian flu and COVID-19. We show the graphic display on the Y-axis what percentage of words in the selected corpus of books (collective memory) over the years (X-axis) make up the word. To standardise the data, we requested the data from 1800 to 2019 in English, German and Russian (to 2012) book corpora and focused on the ten years before, during and after the outbreak of the Russian flu. We compared this frequency index with “non-epidemic periods” to test the model’s analytical potential and prove the signification of the results.ResultsThe COVID-19 is not a unique phenomenon because the Russian flu was probably the coronavirus infection. Results show that all the three analysed book corpora (including newspapers and magazines) show the increase in the mention of the symptoms “loss of smell” and “loss of taste” during the Russian flu (1889-1891), which are today undoubtedly proven to be key symptoms of COVID-19.In the English corpus, the frequency rose from 0.0000040433 % in 1880 to 0.0000047123 % in 1889. The frequency fell sharply after the pandemic stopped in 1900 (0.0000033861%). In the Russian corpus, the frequency rises from 0 % in 1880 to 0.0000004682 % in 1889 and decreased rapidly after the pandemic (1900 = 0.0000011834 %). In the German corpus, the frequency rose from 0.0000014463 % in 1880 to 0.0000018015 % in 1889 and decreased also rapidly after the pandemic (1900 = 0.0000016600 %).According to our analysis of historical records with the approach of GNV, 1) the ‘natural’ length of a pandemic is two to five years; 2) the pandemic stops on their own; 3) the viruses weaken over time; 4) the so-called “herd immunity” is not necessary to stop the pandemic; 5) history has shown that a significant crisis does not need to occur after the COVID-19 pandemic.ConclusionAccording to our study, the Google Books Ngram Viewer (GNV) gives a clear evidence of the influence that social changes have on word frequency. The results of this study open a discussion on the usefulness of the Google Ngram insights possibilities into past socio-cultural development, i.e. epidemics and pandemics that can serve as lessons for today. We showed hidden patterns of conceptual trends in history and their relationships with current development in the case of the pandemic COVID-19.The benefit of this method could help complement historical medical records, which are often woefully incomplete. However, this method comes with severe limitations and can be useful only under cautious handling and testing. Despite the numerous indications we have shown, we are aware that this thesis still cannot be confirmed and that it is necessary to require further historical and medical research.

Computer communication is revolutionizing modern society to the same extend as the invention of writing or the printing press have unsettled the archaic or the ancient society, respectively. In the present article, this idea will be exemplified by a demonstration of how the Google Ngram viewer – an online graphing tool which charts annual counts of words or sentences as found in the largest available corpus of digitalized books – allows for checks and challenges of familiar self-definitions of modern society. As functional differentiation is considered the central unique feature of modern societies, the hypotheses focus on the testing of prominent modern trend statements and predictions, such as the secularization, politicization, economization, and mediatization of society. All hypotheses are tested through a comparative analysis of word frequency time-series plots produced by means of the Google Ngram Viewer. The results show that the importance of individual function systems to society features significant change in time and considerable regional differences. Furthermore, the findings suggest adopting a skeptical position on some of the most frequent common senses of trends in functional differentiation and corresponding self-definitions of society.

Cliodynamics is a fairly recent research field that considers history as an object of scientific study. Thanks to its transdisciplinary nature, cliodynamics tries to explain historical dynamical processes such as the rise or collapse of empires or civilizations, economic cycles, population booms, fashions through mathematical modeling, datamining, econometrics or cultural sociology. "Big data" aggregating historical, archaeological or economic informations is the material to feed these quantitative models. It can also incluse empirical analysis to validate assumptions and predictions of dynamic models using historical data. Cliodynamics is part of the cliometrics approach or "new economic history" which studies history through econometrics.ObjectivesOn the one hand, we designed a robust lexical analysis method able to deal with a very large dated corpus series whose content evolves over time (big data) with the challenge of identifying societal evolutions and major historical periods in a cliodynamics perspective. Lexical analysis also examined the teachings to be learned from the Google books Ngram database, which details the number of annual words occurrences in scanned publications available in the Google Books search engine . It is assumed that this database has compiled about 20% of all books ever published in major languages. We focused our study on English-language books published in the United States and Great Britain. The objective was to identify the words frequencies evolving from year 1860 to 2008.Method PrinciplesThe method was to constitute, as a first step, a dictionary of the most commonly used English words, disregarding two-way terms, preposition, articles, pronouns. This dictionary has collected 1592 words covering many aspects of social and cultural life with terms related to politics, religion, arts and sciences, industry, objects, family and sentiments.In a second step, the percentage representation of each word in the dictionary was determined for each year after loading the huge Ngram Google Books (1-gram) database on Postgresql. Some words like "king" or "queen" are very well represented in the 19th century dictionary with the reign and power of royalties in Europe, but the use of these phrases declined in the 20th century. The words frequency in books is constantly evolving as time goes by.The third step was to perform a centered and standardized principal component analysis (PCA) on the table describing the representation of words in % by years from 1860 to 2008. A clustering of "years" is carried out using a neural network (artificial intelligence Kohonen map).The results show 8 different periods in history according to 3 different major tendancies in speeches : Humanist versus Scientific ; Chaos versus Organization ; Individualist versus Collectivist., La cliodynamique est un domaine de recherche assez récent qui considère l’histoire comme un objet d’étude scientifique. De nature transdisciplinaire, la cliodynamique tente ainsi d’expliquer les processus dynamiques historiques comme la montée ou l’effondrement des empires ou civilisations, les cycles économiques, les booms de population, les modes grâce à la modélisation mathématique, le datamining, l’économétrie ou encore la sociologie culturelle. Les big data agrégeant des données historiques, archéologiques ou économiques sont la matière permettant d’alimenter ces modèles quantitatifs. La cliodynamique comporte également un volet empirique examinant l’adéquation des hypothèses et des prévisions de modèles dynamiques avec les données historiques. Elle s’inscrit dans la démarche de la cliométrie ou « new economic history » qui étudie l’histoire grâce à des méthodes tirées de l’économétrie.ObjectifsIl s’agissait d’une part de concevoir une méthode robuste d’analyse lexicale capable de traiter une série de très gros corpus datés dont le contenu évolue à travers le temps (big data) avec l’enjeu de cerner les évolutions sociétales et les grandes périodes historiques clés dans le cadre de la cliodynamique. L'analyse lexicale s’est d’autre part penchée sur les enseignements à tirer de la base de données Google books Ngram qui détaille le nombre d'occurrences des mots utilisés année après année dans les publications scannées et intégrées au moteur de recherche Google Books. On considère que cette base a compilé environ 20% des livres publiés dans les langues majeures. Nous nous sommes focalisés sur les ouvrages en langue anglaise publiés aux Etats-Unis et en Grande Bretagne. L'objectif a été de cerner l'utilisation plus ou moins forte de certains mots selon les époques, la période d'étude ayant été fixée de 1860 à 2008.Principe de la méthodeLa méthode a été de constituer dans un premier temps un dictionnaire des mots anglais les plus usités en faisant abstraction des termes à double sens, préposition, articles, pronoms. Ce dictionnaire a dans cette version initiale rassemblé 1592 mots couvrant de nombreux aspects de la vie sociale et culturelle avec des termes liés à la politique, à la religion, aux arts et aux sciences, à l'industrie, aux objets, à la famille et aux sentiments.Dans un second temps, il a été déterminé la représentation en % de chacun de ses mots au sein du dictionnaire année après année après avoir mis l'imposante base Ngram Google Books (1-gram) sur Postgresql. Certains mots comme "king" ou "queen" sont très bien représentés dans le dictionnaire au 19ème siècle avec le règne et la puissance des pouvoirs royaux en Europe, mais l'usage de ces locutions décline au 20ème siècle. On constate donc une évolution constante de la fréquence des mots dans les ouvrages au fur et à mesure des époques.La troisième étape a été d'effectuer une analyse factorielle en composantes principales centrée et normée sur le tableau décrivant la représentation des mots en % selon les années de 1860 à 2008. Une classification des années en groupes est réalisée grâce à un réseau de neurones (carte auto-adaptative de Kohonen en IA).Les résultats montrent 8 périodes différentes dans l'histoire selon 3 tendances différentes dans les discours : humaniste versus scientifique; chaos versus organisation; individualiste versus collectiviste.

International audience; The present article considers the economization of society a hypothesis rather than a fact. The hypothesis is tested against the results of a Google ngram viewer analysis of the most frequent function system references in the Google Books corpus for the years 1800-2000. Despite the remarkable growth figures in the English, French, and German language corpora as related to economic word frequency shares, the results suggest the rejection of the economization hypothesis. In fact, the growth trends of economic word frequencies are stopped in all of the three language areas, in none of which the economy ever reached a dominant position throughout the entire 200 years. The results give reason to assume that the idea of an economized society is an intellectual artifact rather than a fact. This fact is emphasized not to prove the marginal relevance of research in economic risks and benefits, but rather in terms of a suggestion to consider refocusing research foci and drawing increased attention to function systems beyond the politicoeconomic double stars of social science. Maybe even the solution to the present "economic" "crises" is not in more, but rather in less attention to the economy.