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Understanding and predicting the emergence and evolution of cultural tastes manifested in consumption patterns is of central interest to social scientists, analysts of culture, and purveyors of content. Prior research suggests that taste preferences relate to personality traits, values, shifts in mood, and immigration destination. Understanding everyday patterns of listening and the function music plays in life has remained elusive, however, despite speculation that musical nostalgia may compensate for local disruption. Using more than one hundred million streams of four million songs by tens of thousands of international listeners from a global music service, we show that breaches in personal routine are systematically associated with personal musical exploration. As people visited new cities and countries, their preferences diversified, converging toward their travel destinations. As people experienced the very different disruptions associated with COVID-19 lockdowns, their preferences diversified further. Personal explorations did not tend to veer toward the global listening average, but away from it, toward distinctive regional musical content. Exposure to novel music explored during periods of routine disruption showed a persistent influence on listeners' future consumption patterns. Across all of these settings, musical preference reflected rather than compensated for life's surprises, leaving a lasting legacy on tastes. We explore the relationship between these findings and global patterns of behavior and cultural consumption.

This article investigates the potential for novel research utilising data generated by the Creative and Cultural Industries (CCI) in the UK, focussing on the long tail of metadata associated with the UK's rich cultural events landscape. We conducted semi-structured interviews with 29 researchers and related domain experts to ascertain: (1) How cultural data is valued; (2) The landscape of cultural data; (3) How UK research can make better use of cultural events data; (4) The benefits and pitfalls of an evidence-based approach to cultural policy; and (5) The repercussions of the Covid-19 pandemic on how data-led work is positioned within the CCI. We advocate for the potential value of cultural events data to academic research, policy and industry, and also for a humanities-led approach. We suggest that a centralised cultural events data service for use in research, industry and policy is one way of supporting this. [ABSTRACT FROM AUTHOR]

Throughout the nineteenth and twentieth centuries in the United States and the United Kingdom, rivers and streams were piped, dammed, reversed, straightened, and dried-up, all in service of a growing demand for clean, reliable water in every household. This paper uses an interpretive distant reading methodology for asking how this dramatic change was reflected in English-language literature. As an imaginative space of reflection on culture and material life, how does literature accommodate and make sense of changes in environmental realities? Looking at the diachronic word embeddings surrounding the word "river" in the Novel TM corpus housed within HathiTrust Digital Library, this study identifies a number of trends over time in the shifting semantic fields surrounding "river." It argues that these results indicate a possibly less intimate conceptualization of rivers over time, one more defined by rivers' geographic attributes than by their ecologies and specific natures. (DM) [ABSTRACT FROM AUTHOR]

This article seeks to understand how the Cultural Analytics’ methodological approach and computational tools help interpret large image datasets. A set of 87 730 images of 389 Olympic athletes was collected from Instagram and analyzed, featuring a timespan from September 2011 to November 2020. The image set was structured and organized using computer vision processing combined with interactive visualization tools (Google Vision, PixPlot, Image Network Plotter). The analysis, mixing quantitative and qualitative methods, identified patterns represented as image clusters. Regular personal computers served as the hardware platform. Approximately 60 % of the athletes’ posts were related to non-sports topics, highlighting common characteristics of the visual culture disseminated on Instagram, such as selfies, lifestyle, leisure, travel, and food. Images of sports content, considered a central aspect of the research, had a lower frequency of publications featuring topics such as competitions, training, exercises, and sports practices in general. Beyond this result, the study offers a possible technical framework for similar researchers using large image datasets.

With the emerging era of E‐commerce and online shopping, people are also in a habit to receive default product recommendations on the web pages that they access. Google is already providing such suggestions. Till now recommendations were made only based on previous sentiments or feedback or ratings, but this research has improved the product recommendation method by including one more parameter for the same. This article represents two parameters for making predictions of product allocation to a new customer. These parameters are ratings given by the existing users for that particular product and the region to which the new customer belongs. Following these parameters, a prediction model and an algorithm, Improved_Collab_Similarity, have been implemented. The dataset has been developed where India as a country along with all its States has been considered for products which are popular for their creation based on regional and ancient skills of the people belonging to that area. Results for the mentioned prediction model have been discussed in this article where generally precision increases with the increase in a number of products but at some points, it does not increase when a smaller number of that product was purchased by the customers. [ABSTRACT FROM AUTHOR]

Knowledge communication in cultural heritage and digital humanities currently faces two challenges, which this paper addresses: On the one hand, data-driven storytelling in these fields has mainly focused on human protagonists, while other essential entities (such as artworks and artifacts, institutions, or places) have been neglected. On the other hand, storytelling tools rarely support the larger chains of data practices, which are required to generate and shape the data and visualizations needed for such stories. This paper introduces the InTaVia platform, which has been developed to bridge these gaps. It supports the practices of data retrieval, creation, curation, analysis, and communication with coherent visualization support for multiple types of entities. We illustrate the added value of this open platform for storytelling with four case studies, focusing on (a) the life of Albrecht Dürer (person biography), (b) the Saliera salt cellar by Benvenuto Cellini (object biography), (c) the artist community of Lake Tuusula (group biography), and (d) the history of the Hofburg building complex in Vienna (place biography). Numerous suggestions for future research arise from this undertaking. [ABSTRACT FROM AUTHOR]

This paper proposes a research agenda to study cultural networks that assumes their role both in making meaning and as adaptative tools for humans and their communities. First, the agenda grounds the study of cultural networks in Whitehead’s (1861–1947) metaphysics of process, posing that cultural networks emerge during human’s production of relations with the world of phenomena. Then, the paper identifies two approaches to studying cultural networks, reassembling them or generating them. Thirdly, it contextualizes the importance of understanding cultural networks vis a vis the consolidation of the digital humanities in the academic domain, and the productivity of networks as tools to explore the cultural in networked domains of human experience. The final remarks locate the proposed research agenda within the context of multidisciplinary research on cultural analytics.

In this article we make a case for a synchronic and contextualizing perspective on the scaling of literary data, one which qualifies and expands the data points in terms of depth, or thickness, through the help of metadata on the social and historical conditions of the texts. Our case study is an investigation of the rise and impact of realism in a corpus of more than 800 Danish and Norwegian novels from 1870 to 1899 with three, interlocking critical and methodological aims. We use textual features to model realism in a large corpus of Danish‐language novels. We compare the features driving that model to the ones that were important to the historical and critical development of realism as a literary project. And we use the results of our model to study the interplay between realism and social history. Our findings suggest that realism was more prominent and more widely distributed in Danish‐language novels of the late nineteenth century than the critical tradition has usually acknowledged. Women appear to have written realist fiction not only at rates similar to men, but at times and with features that are difficult to distinguish from their male counterparts. The article relies on—and insists on further—dialogue between digital and analog approaches to the exploration of cultural data. [ABSTRACT FROM AUTHOR]

Theoretical and computational frameworks of complexity science are dominated by binary structures. This binary bias, seen in the ubiquity of pair-wise networks and formal binary operations in mathematical models, limits our capacity to faithfully capture irreducible polyadic interactions in higher-order systems. A paradigmatic example of a higher-order interaction is the Borromean link of three interlocking rings. In this paper, we propose a mathematical framework via hypergraphs and hypermatrix algebras that allows to formalize such forms of higher-order bonding and connectivity in a parsimonious way. Our framework builds on and extends current techniques in higher-order networks — still mostly rooted in binary structures such as adjacency matrices — and incorporates recent developments in higher-arity structures to articulate the compositional behavior of adjacency hypermatrices. Irreducible higher-order interactions turn out to be a widespread occurrence across natural sciences and socio-cultural knowledge representation. We demonstrate this by reviewing recent results in computer science, physics, chemistry, biology, ecology, social science, and cultural analysis through the conceptual lens of irreducible higher-order interactions. We further speculate that the general phenomenon of emergence in complex systems may be characterized by spatio-temporal discrepancies of interaction arity. [ABSTRACT FROM AUTHOR]

As much as users and advertisers have flocked to it, TikTok has been a success. To this point, research on TikTok has been concerned with the nature of TikTok texts and the ways that laterally organized networks on TikTok have intermedia interface points. Instead of focusing on the text or the authors, this article argues that Raymond Williams concept of flow is critical for an initial characterization of a social media platform, proposing and implementing a Markov process model for critical media studies of the experience of TikTok flow. The result of this analysis challenges many commonly held ideas about what TikTok "is" and offers evidence that TikTok is television, not social media. We argue that this shift of perspective is important for advancing understanding of this platform. [ABSTRACT FROM AUTHOR]

Copyright of Economía Creativa is the property of Centro de Diseno y Comunicacion, S.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper examines different methods of predicting bibliographical details (e.g. author, title, and publisher) of books under review in a corpus of approximately 1,100 historical book reviews. The dataset is comprised of book reviews from ProQuest’s American Periodicals Series (APS). This kind of bibliographical profiling is often characterized as a Natural Language Processing (NLP) or Named Entity Recognition (NER) task, but it can be more specifically described as a two-part Named Entity Linking (NEL) task, beginning with a feature extraction stage followed by one of several available matching or classification methods. An attempt has been made to formalize constraints for modular bibliographical profiling (MBP) and shed light on some important choices that are often glossed over or obscured by digital humanities practitioners. Applying these constraints, the paper evaluates combinations of feature selection (naive bag-of-words [BOW], rule-based feature extraction, and NER using a pre-trained model) with a standardized similarity-based matching strategy (cosine similarity). All tasks are performed on derived text data (term frequency tables), so that data can be shared and all methods can be used on materials available only in non-consumptive formats. These comparisons suggest that naive BOW can perform quite robustly, and that using even a basic pre-trained NER model in conjunction with a BOW approach may reduce false positives.

Many aspects of behavior are guided by dispositions that are relatively durable once formed. Political opinions and phonology, for instance, change largely through cohort succession. But evidence for cohort effects has been scarce in artistic and intellectual history; researchers in those fields more commonly explain change as an immediate response to recent innovations and events. We test these conflicting theories of change in a corpus of 10,830 works of fiction from 1880 to 1999 and find that slightly more than half (54.7 percent) of the variance explained by time is explained better by an author’s year of birth than by a book’s year of publication. Writing practices do change across an author’s career. But the pace of change declines steeply with age. This finding suggests that existing histories of literary culture have a large blind spot: the early experiences that form cohorts are pivotal but leave few traces in the historical record.

The article presents research paradigms that have radically changed the contemporary humanities. The most important of these is cultural analytics. It is based on Data Science methods. The author presents the assumptions of data science, and then the characteristics of digital humanities and cultural analytics. The second part of the article presents examples of research and projects conducted as part of cultural analysis. These are projects implemented at the DH Lab at Yale University, Software Studies Initiative, and Media Lab Katowice. Research conducted in these institutions transformed the humanities. Its characteristic features are the study of large data collections, research automation, the use of machine learning and knowledge visualization. The new humanities, the author claims, has become an exact science.

To discern the role social and cultural networks play in the emergence of preeminent historical figures and ideas in History, we use a method based on complex networks analysis to reveal emergent interactions in Wikipedia. We built a network constituted by derivative links, where nodes are connected if they are co-linked by other papers or co-link other papers within Wikipedia. We apply this method, focused on the structural distance, to three significant individuals associated with the Italian Renaissance: Copernicus, Michelangelo, and Pico della Mirandola. The results point to the effectiveness of this approach for discovering new knowledge about the interdisciplinary transactions between people and ideas coming from artistic, scientific and philosophical domains during this period. The emergent network reflects the apparently strong network-level interactions between Michelangelo and Mirandola's clusters; the importance of Hermeticism across the three clusters; and how the so-called "knowledge dealers" related to Neoplatonism contribute to the depiction of the period by future historians. Finally, we advance the notion of "focus reading", in which complex networks analysis allows us to build bridges between close and distant forms of reading historical evidence. [ABSTRACT FROM AUTHOR]

A significant part of modern technologies associated with the development of artificial intelligence systems and digital analytics of diverse data relies on methods of computer text processing (NLP, speech technologies). However, NLP methods are applied primarily to specialized texts, such as scientific literature, technical documentation, news, etc., or social media discourse; fiction texts being usually left out of the focus of NLP practitioners as the fictional world seems to be of less significance or less information value from a practical point of view. Moreover, due to the poetic and metaphorical nature of literary texts, the use of some NLP methods (e.g., topic modeling) for fiction analysis turned out to be more complicated. At the same time, the influence of literature both on the consciousness of individuals and on the formation of social values can hardly be overestimated. Besides, making computers understand fiction in a similar way as humans do would be a real challenge for artificial intelligence. The article puts forward the idea of modeling thematic areas of literature on a national scale, which should reveal the main thematic domains of national literature as a whole. It will allow a better understanding of the cultural traits of the national consciousness in a given historical period and contribute to either literary studies and practical tasks. Methodological approaches to determining and modeling themes of literary works are considered, technical difficulties arising in the process are described, and the ways to solve them are suggested. The proposed methodology has been implemented in the design of the Russian short stories corpus (the first third of the 20th century) and can be applied in the development of artificial intelligence systems that process large volumes of literary texts in any language.

Many aspects of behavior are guided by dispositions that are relatively durable once formed. Political opinions and phonology, for instance, change largely through cohort succession. But evidence for cohort effects has been scarce in artistic and intellectual history; researchers in those fields more commonly explain change as an immediate response to recent innovations and events. We test these conflicting theories of change in a corpus of 10,830 works of fiction from 1880 to 1999 and find that slightly more than half (54.7 percent) of the variance explained by time is explained better by an author's year of birth than by a book's year of publication. Writing practices do change across an author's career. But the pace of change declines steeply with age. This finding suggests that existing histories of literary culture have a large blind spot: the early experiences that form cohorts are pivotal but leave few traces in the historical record. [ABSTRACT FROM AUTHOR]

From 2009 to 2018, social attitudes towards gender in the UK and other western countries significantly shifted, with greater awareness of gender stereotyping and the objectification of women's bodies in the media due to popular social media campaigns. Arguably, postfeminism has provided a channel in which tropes traditionally critiqued by feminism, such as the objectification or stereotyping of the female body, are appropriated by consumption, and reconceptualised as empowering forms of femininity. In two phases, this study explores if and how this social shift has been translated into how men and women's bodies are presented to male and female audiences, respectively, by magazines that claim to represent and reflect them. Firstly, content of the front covers from six popular men's and women's magazines was analysed to identify the framing, clothing, and staging of the body. A second phase adds further novelty to this study by employing a cultural analytics approach to explore the relationship between gender and hue by analysing the brightness, saturation and hue values of each image. The findings show some continuities in how men and women are presented, despite significant changes in which magazines presented these continuities. Furthermore, the colour attributes of the images underlined significant differences in how men and women are presented for viewing. It is argued that these continuities and changes represent a reinforcement of patriarchal gendered relations (including gendered associations with specific colours) re-imagined through a postfeminist logic. [ABSTRACT FROM AUTHOR]

Copyright of Studia Humanistyczne AGH is the property of AGH University of Science & Technology Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

What is the most important reason for using Computer Vision methods in humanities research? In this article, I argue that the use of numerical representation and data analysis methods offers a new language for describing cultural artifacts, experiences and dynamics. The human languages such as English or Russian that developed rather recently in human evolution are not good at capturing analog properties of human sensorial and cultural experiences. These limitations become particularly worrying if we want to compare thousands, millions or billions of artifacts—i.e. to study contemporary media and cultures at their new twenty-first century scale. When we instead use numerical measurements of image properties standard in Computer Vision, we can better capture details of a single artifact as well as visual differences between a number of artifacts–even if they are very small. The examples of visual dimensions that numbers can capture better then languages include color, shape, texture, contours, composition, and visual characteristics of represented faces, bodies and objects. The methods of finding structures and relationships in large numerical datasets developed in statistics and machine learning allow us to extend this analysis to very big datasets of cultural objects. Equally importantly, numerical image features used in Computer Vision also give us a new language to represent gradual and continuous temporal changes—something which natural languages are also bad at. This applies to both single artworks such as a film or a dance piece (describing movement and rhythm) and also to changes in visual characteristics in millions of artifacts over decades or centuries. [ABSTRACT FROM AUTHOR]

En el presente artículo se expone el análisis que se ha realizado sobre las redes de relaciones de los participantes de una serie de bienales de arte seleccionadas en los últimos diez años previos a la epidemia del Covid-19.

Review of Lev Manovich, Cultural Analytics. Cambridge, MA: The MIT Press, 2020. 336 pp. ISBN: 978-0262037105.

In this paper, we take into account both social and linguistic aspects to perform demographic analysis by processing a large amount of tweets in Basque language. The study of demographic characteristics and social relationships are approached by applying machine learning and modern deep-learning Natural Language Processing (NLP) techniques, combining social sciences with automatic text processing. More specifically, our main objective is to combine demographic inference and social analysis in order to detect young Basque Twitter users and to identify the communities that arise from their relationships or shared content. This social and demographic analysis will be entirely based on the automatically collected tweets using NLP to convert unstructured textual information into interpretable knowledge. [ABSTRACT FROM AUTHOR]

Digital Humanities (DH) has been a success story in the academic world over the last twenty years which has opened many new vistas in the humanities research. However, we argue that it is better to consider DH as a transitory phenomenon that needs to be developed into more specific research fields in order to overcome some fundamental shortcomings of DH. First, it is vital to transcend the division between qualitative inquiry into ideographic phenomena and quantification of nomothetic phenomena. This specifically includes the emergence and analysis of organized complexity, which emerges right in between. Secondly, it is important to surpass the dichotomy of singular and general as research objects and replace this by the notion of semiosphere as a research object, defined as the "smallest" functioning element of culture by Juri Lotman. From this perspective, the singular cultural unit is always conditioned by the whole of the semiosphere, but the whole can be always changed by the singular unit. Thirdly, the denominator "digital" is questionable and needs to be left behind, as there are many other forms of computation, which can be analog and/or digital. Indeed, alternative forms of non-digital computation are already seeing a comeback. Fourthly, the label of "humanities" is at the same time too large and too restrictive. We would argue for a more specific study of meaning making practices in human society, yet without confining ourselves to traditional humanities scholarship but learning from the new developments in system biology, evolutionary economics, complexity science and many more. We propose to call this new transdisciplinary field of study "cultural data analytics", aiming to explore the dynamics of meaning making practices by computational means and looking at a spectrum of materials (textual, sonic, visual, multimodal, etc.) both regarding the longue durée and in real time, if not anticipating the future. [ABSTRACT FROM AUTHOR]

Narratives, and other forms of discourse, are powerful vehicles for informing, entertaining, and making sense of the world. But while everyday language often describes discourse as moving quickly or slowly, covering a lot of ground, or going in circles, little work has actually quantified such movements or examined whether they are beneficial. To fill this gap, we use several state-of-the-art natural language-processing and machine-learning techniques to represent texts as sequences of points in a latent, high-dimensional semantic space. We construct a simple set of measures to quantify features of this semantic path, apply them to thousands of texts from a variety of domains (i.e., movies, TV shows, and academic papers), and examine whether and how they are linked to success (e.g., the number of citations a paper receives). Our results highlight some important cross-domain differences and provide a general framework that can be applied to study many types of discourse. The findings shed light on why things become popular and how natural language processing can provide insight into cultural success. [ABSTRACT FROM AUTHOR]

In this article we describe our experiences with computational text analysis involving rich social and cultural concepts. We hope to achieve three primary goals. First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods. Second, we hope to provide a set of key questions that can guide work in this area. Our guidance is based on our own experiences and is therefore inherently imperfect. Still, given our diversity of disciplinary backgrounds and research practices, we hope to capture a range of ideas and identify commonalities that resonate for many. This leads to our final goal: to help promote interdisciplinary collaborations. Interdisciplinary insights and partnerships are essential for realizing the full potential of any computational text analysis involving social and cultural concepts, and the more we bridge these divides, the more fruitful we believe our work will be.

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semanticWeb era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations' websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web. [ABSTRACT FROM AUTHOR]

Digital methods in humanities and social sciences are often presented as disruptive towards existing methods. On the opposite, Noortje Marres claims that online methods are « redistributed » in order to apply to the digital medium. If these methods are not radicaly new, they face important epistemological debates. This article will present three of these debates by focusing on three types of digital research. Digital humanities interrogate the possibilities of a dialogue between sciences and the humanities ; Cultural Analytics challenges the possibility to do research in humanities with large datasets ; digital methods adress the necessity to create new methods to apply to a digital research object. The conclusion will present how the reflexive and experimental nature of these works can benefit to information and communication sciences.