Abstract The issue of quantitative measurement and automatic processing is a significant problem in determining the markers of the manipulative potential of media texts, since linguistic indicators are the basis of machine parameterization. The purpose of the research is to analyse the possibilities of the main language parameters of the manipulativeness of media discourse, which can be identified using machine learning. To achieve the research goals, the following methods were used: system, content analysis, computer modelling, and comparative. The results of the article determined that such language indicators as use of the subjunctive mood of verbs, capital letters, high frequency of use of the ‘not’ particle, punctuation marks, questions, or exclamations of a rhetorical nature, use of quotation marks for the purpose of irony, double negative sentences, use of the word ‘no’, and verbal structures calling to action act as computer classification parameters. In order to cover the above purpose, PYTHON software was implemented that allowed texts to be analysed and visualized in algorithmic and lexical-vocabulary ways. In addition, it was determined that by integrating the PYTHON tool, it became possible to use language transformation markers that formed linguistic patterns in the analysed text. The list of parameters for diagnosing manipulative texts is non-exhaustive, which emphasizes the possibility of machine measurement of the manipulative component of mass media discourse.

Abstract Hollywood film remakes, as old as the cinema itself, have attracted much professional, critical, and academic attention. They have been viewed by art critics as products of cultural derivativity and imperialism and commended by financial experts as low-risk business investments, closely linked to other forms of brand extension, such as sequels and bestseller adaptations. In this article, we adopt a film-historical quantitative approach to Hollywood film remakes by analysing metadata obtained from the Internet Movie Database (IMDb) and verified against reliable print and web sources. We analyse 986 Hollywood remakes produced between 1915 and 2020 in terms of raw and relative frequencies of annual releases, genre (in)stability, and patterns of transnational reproduction. We contrast our findings with those outlined by Henderson (2014a) in his statistical survey of Hollywood sequels, series films, prequels, and spin-offs, presented in his monograph The Hollywood Sequel: History and Form, 1911–2010. Having completed his list with recent sequential productions released between 2011 and 2020, we investigate the potential parallels between Hollywood remaking and sequelization practices. Our findings demonstrate historical discrepancies in various ‘content recycling’ trends, which help better characterize the cultural and commercial significance of remakes and serial forms in the American film industry.

Abstract An early industrial town’s spatial segregation is studied using empirical data concerning the Russian population of the town of Vyborg. Several hypotheses for explaining segregation are considered using spatial analysis. The spatial data are derived from historical maps and demographic data from various tax records. Socioeconomic segregation is studied as a possible cause of ethnic segregation. The main drivers of spatial segregation were the explicit policies of segregation enforced by both the Russian military administration and the town’s civilian administration. While the effects of segregation gradually diminished due to social diffusion, the impact of policy decisions driving segregation in the 18th and early 19th centuries was still visible in the population’s later 19th-century segregation. Yet neither the different preferences of Russians and others nor the income differences between areas explains the distribution of Russians. Segregation based on the membership of a guild was insignificant, with a few exceptions. Other factors such as discrimination, prejudice, and differences in housing market information probably contributed to segregation, but they cannot be studied with the data used.

Abstract Distant viewing approaches have typically used image datasets close to the contemporary image data used to train machine learning models. To work with images from other historical periods requires expert annotated data, and the quality of labels is crucial for the quality of results. Especially when working with cultural heritage collections that contain myriad uncertainties, annotating data, or re-annotating, legacy data is an arduous task. In this paper, we describe working with two pre-annotated sets of medieval manuscript images that exhibit conflicting and overlapping metadata. Since a manual reconciliation of the two legacy ontologies would be very expensive, we aim (1) to create a more uniform set of descriptive labels to serve as a “bridge” in the combined dataset, and (2) to establish a high-quality hierarchical classification that can be used as a valuable input for subsequent supervised machine learning. To achieve these goals, we developed visualization and interaction mechanisms, enabling medievalists to combine, regularize and extend the vocabulary used to describe these, and other cognate, image datasets. The visual interfaces provide experts an overview of relationships in the data going beyond the sum total of the metadata. Word and image embeddings as well as co-occurrences of labels across the datasets enable batch re-annotation of images, recommendation of label candidates, and support composing a hierarchical classification of labels.

Abstract To what extent do Digital Humanities (DH) platforms support access to diverse user cohorts? We take, as a case study, the Automated Text Recognition (ATR) platform Transkribus and its Transkribus Scholarship Programme (TSP), which provides free processing credits to eligible users. Using a mixed methods approach we address the following questions: What are the demographics of those using the TSP scheme? What work is enabled by such a scheme? How can this inform more equitable access to DH platforms? The findings demonstrate how ATR tools are currently used and made accessible. TSP applicants are overwhelmingly students (n = 111/156, 71.15 per cent) drawn from universities and research institutes, mostly in Europe, but are globally distributed; representing institutions that do not hold shares in Transkribus, and indicating a diverse user pipeline. Further work is required to increase potential benefits of the scholarship and to ensure sustainability. Increased dialogue between the Recognition and Enrichment of Archival Document-COOP and applicants would assist in the calculation of processing costs. We show financial—or in-kind—support is necessary to increase access to paid-for platforms, ensuring a diversity of DH research. We also provide recommendations for platform providers and funding bodies regarding access and the impact this can have, including locating a sustainable balance between absorbing the costs of maintaining DH or digital scholarship tools and providing sufficient support and training to further enable diverse research.

Abstract As the capital of Tang Dynasty, Chang’an was one of the most prosperous cities in the world at that time and had a profound influence on Tang poetry. Poets described Chang’an to illustrate the cultural features of the Tang Dynasty while also invoking emotions in readers. The study of Tang Chang’an poetry has important literary and historical value. In order to understand the interpretation and emotional expression of Tang Chang’an poetry more conveniently and clearly, we conducted a study using deep learning to classify Chang’an poetry into four classes: imperially assigned poetry (应制), emotional poetry (感怀), parting poetry (离别), and other poetry (其他). We suggested a comprehensive framework of text classification based on deep learning, including a text input module, feature encoder module, and classification module. We applied several mainstream deep neural network structures to extract features in different ways, which comprised convolutional neural network (CNN), Fasttext, bi-direction long-short-term memory network, and Attention mechanism. Based on our experimental findings, the CNN-based method achieved the best performance for the task. Our inference was that, in Chinese ancient poetry, the analysis of semantic content is more facilitated by local textual features rather than contextual features. We combined this inference with the theory of image in Chinese ancient poetry to analyze the suitability of the deep learning techniques for the study of Chinese ancient poetry.

Abstract Since the Internet is a breeding ground for unconfirmed fake news, its automatic detection and clustering studies have become crucial. Most current studies focus on English texts, and the common features of multilingual fake news are not sufficiently studied. Therefore, this article uses English, Russian, and Chinese as examples and focuses on identifying the common quantitative features of fake news in different languages at the word, sentence, readability, and sentiment levels. These features are then utilized in principal component analysis, K-means clustering, hierarchical clustering, and two-step clustering experiments, which achieved satisfactory results. The common features we proposed play a greater role in achieving automatic cross-lingual clustering than the features proposed in previous studies. Simultaneously, we discovered a trend toward linguistic simplification and economy in fake news. Furthermore, fake news is easier to understand and uses negative emotional expressions in ways that real news does not. Our research provides new reference features for fake news detection tasks and facilitates research into their linguistic characteristics.

Abstract The Russia–Ukraine War has emerged as a highly contentious global issue since 2022. While China and the UK are not directly involved in the conflict, considerable attention has been drawn to their positions and perspectives on this event. In such context, conducting a comparative study on how the British and Chinese mainstream media cover the Russia–Ukraine conflict can provide valuable insights into the influence of ideological differences on news framing and shed light on the respective stances of these two news agencies. Employing an interdisciplinary methodology, this study integrates corpus tools, critical discourse analysis, text mining, and emotion computation to systematically analyze news reports covering the Russia–Ukraine War from Reuters and Xinhua between 2022 and 2023. Results show different patterns in the news reports from the two investigated news agencies, including the monthly publication of news articles, the occurrence of prominent entities, and the thematic emphasis. Additionally, significant variations are identified in specific dimensions of emotion and emotional intensity, indicating the divergent stances of the two news agencies on a range of significant issues.

Abstract This article presents the WRITE data model and dataset, a comprehensive collection of Chinese contemporary calligraphic data, utilizing Linked Open Data (LOD) principles. Calligraphy plays a pivotal role in Chinese culture, reflecting national identity and cultural transformations. The objective of this study is to enhance understanding and provide new tools for exploring Chinese contemporary calligraphy through LOD. The WRITE data model comprises artistic, linguistic, and socio-political-economic aspects. The WRITE data model, developed collaboratively with domain specialists, represents four collections: Contemporary Visual Art, Performance, Graffiti, and Decorative and Applied Arts. Metadata describing the artworks is structured by reusing and extending the Wikidata model. Complex relations are established between artworks and contextual elements, (e.g. people, exhibition history, organizations, and literary works). The artistic and linguistic metadata recorded over the ‘calli-writing units’ provide insights into shared and diverging characteristics with traditional calligraphy. Traditional and contemporary calligraphy practices are compared, highlighting how contemporary calligraphy challenges traditional rules. Two case studies demonstrate the formalization of specific items in the WRITE collection, showcasing the study of graffiti art’s socio-political meaning in China and the multidimensional nature of musicalligraphy performance. The WRITE dataset and data model contribute to advancing knowledge and understanding of Chinese contemporary calligraphy, offering valuable resources for artistic analysis and interdisciplinary research.