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The purpose of this study is to explore linguistic stylometric patterns encompassing lexical, syntactic, structural, sentiment and politeness features that are found in librarians’ responses to user queries.

A total of 462 online texts/transcripts comprising answers of librarians to users’ questions drawn from the Internet Public Library were examined. A Principal Component Analysis, which is a data reduction technique, was conducted on the texts and transcripts. Data analysis illustrates the three principal components that predominantly occur in librarians’ answers: stylometric richness, stylometric brevity and interpersonal support.

The results of the study have important implications in digital information services because stylometric features such as lexical richness, structural clarity and interpersonal support may interplay with the degree of complexity of user queries, the (a)synchronous communication mode, application of information service guideline and manuals and overall characteristics and quality of a given digital information service. Such interplay may bring forth a direct impact on user perceptions and satisfaction regarding interaction with librarians and the information service received through the computer-mediated communication channel.

To the best of the authors’ knowledge, the stylometric features encompassing lexical, syntactic, structural, sentiment and politeness using Principal Component Analysis have not been explored in digital information/reference services. Thus, there is an emergent need to explore more fully how linguistic stylometric features interplay with the types of user queries, the asynchronous online communication mode, application of information service guidelines and the quality of a particular digital information service.

In the context of information retrieval, text genre is as important as its content, and knowledge of the text genre enhances the search engine features by providing customized retrieval. The purpose of this study is to explore and evaluate the use of stylometric analysis, a quantitative analysis for the linguistics features of text, to support the task of automated text genre detection for Classical Arabic text.

Unsupervised clustering and supervised classification were applied on the King Saud University Corpus of Classical Arabic texts (KSUCCA) using the most frequent words in the corpus (MFWs) as stylometric features. Four popular distance measures established in stylometric research are evaluated for the genre detection task.

The results of the experiments show that stylometry-based genre clustering and classification align well with human-defined genre. The evidence suggests that genre style signals exist for Classical Arabic and can be used to support the task of automated genre detection.

This work targets the task of genre detection in Classical Arabic text using stylometric features, an approach that has only been previously applied to Arabic authorship attribution. The study also provides a comparison of four distance measures used in stylomtreic analysis on the KSUCCA, a corpus with over 50 million words of Classical Arabic using clustering and classification.

The digital humanities offer many new methods to scholars interested in metal studies. This chapter demonstrates two such methods, that is, stylometric clustering and topic modelling, by interpreting the collected lyrics of over 8,000 metal bands. This allowed the authors to identify general trends that would be hard to derive by personally reading lyrics. This analysis showed several recurring medieval topics in metal lyrics, for example, a Fight for Glory expressed with words like ‘die’, ‘glory’, ‘warriors’, and ‘victory’. The authors were also able to distinguish metal bands with medieval references from those without – a line of research that could help in categorising (new) bands – and track the stylistic development of bands over time. Such statistical methods might help scholars not only by allowing for large-scale studies, yet also by providing inter-subjective feedback for theories.

The authors’ goal is to investigate variations in the writing style of book reviews published on different social reading platforms and referring to books of different genres, which enables acquiring insights into communication strategies adopted by readers to share their reading experiences.

The authors propose a corpus-based study focused on the analysis of A Good Review, a novel corpus of online book reviews written in Italian, posted on Amazon and Goodreads, and covering six literary fiction genres. The authors rely on stylometric analysis to explore the linguistic properties and lexicon of reviews and the authors conducted automatic classification experiments using multiple approaches and feature configurations to predict either the review's platform or the literary genre.

The analysis of user-generated reviews demonstrates that language is a quite variable dimension across reading platforms, but not as much across book genres. The classification experiments revealed that features modelling the syntactic structure of the sentence are reliable proxies for discerning Amazon and Goodreads reviews, whereas lexical information showed a higher predictive role for automatically discriminating the genre.

The high availability of cultural products makes information services necessary to help users navigate these resources and acquire information from unstructured data. This study contributes to a better understanding of the linguistic characteristics of user-generated book reviews, which can support the development of linguistically-informed recommendation services. Additionally, the authors release a novel corpus of online book reviews meant to support the reproducibility and advancements of the research.

This chapter explores metal albums as media, and their relationship to medieval media, as well as secondary media as a resource for reception studies. Examination of metal music as media reveals broader trends in modern media representations of the medieval in respect to race, gender, and cultural identity. Albums are composite object, using different media and secondary media products. The methodology used to approach these media and which elements of an album are examined in combination have a significant effect on the results. This chapter brings together three authors to discuss and compare methodologies and make the case for a combination of contextual analogue and quantitative digital approaches.

Employing big data analysis tools, this study examines the significance of supply chain integration affecting online financial consumption, analyzes the online financial consumption demand of mobile phone consumers, promotes the optimization of supply chain services with consumers as the focus and proposes full integration of a mobile phone supply chain in terms of product, logistics and marketing, in order to improve the supply and demand relationship between consumers and suppliers; the overall objective is to promote further development of online financial consumption.

In this study, TF-IDF (term frequency–inverse document frequency) and cosine similarity text analysis are used for analyzing online demand for mobile phone products, studying the influence of supply chain services on consumption demand and identifying strategies for promoting overall optimization of the supply chain to meet online financial consumption demands of consumers; the study analyzes online reviews on mobile phone topics from the JingDong (JD) platform and Weibo platform.

Research results show that online demand for mobile phone products is greatly influenced by supply chain links such as product design, logistics transportation and marketing promotion. The consumption demand for different mobile phone products has different emphases, but the differences are not significant. The overall improvement of the supply chain should focus on product research and development, logistics layout optimization and marketing promotion, in order to meet and guide the online financial demand of consumers and improve the effectiveness of supply chain management.

This study only considered data from China's largest online mobile phone sales platform and Weibo text data owing to the data sensitivity involved.

There are few supply chain optimization studies based on online financial consumption reviews from customers. Therefore, this study integrates online consumption trends into a supply chain analysis framework to explore strategies for promoting supply chain optimization according to customer demands, improving the benign interaction of participants in the supply chain and promoting the development of online financial consumption.

The creation and dissemination of fake news can have severe consequences for a company’s brand. Researchers, policymakers and practitioners are eagerly searching for solutions to get us out of the “fake news crisis”. Here, one approach is to use automated tools, such as artificial intelligence (AI) algorithms, to support managers in identifying fake news. The study in this paper demonstrates how AI with its ability to analyze vast amounts of unstructured data, can help us tell apart fake and real news content. Using an AI application, this study examines if and how the emotional appeal, i.e., sentiment valence and strength of specific emotions, in fake news content differs from that in real news content. This is important to understand, as messages with a strong emotional appeal can influence how content is consumed, processed and shared by consumers.

The study analyzes a data set of 150 real and fake news articles using an AI application, to test for differences in the emotional appeal in the titles and the text body between fake news and real news content.

The results suggest that titles are a strong differentiator on emotions between fake and real news and that fake news titles are substantially more negative than real news titles. In addition, the results reveal that the text body of fake news is substantially higher in displaying specific negative emotions, such as disgust and anger, and lower in displaying positive emotions, such as joy.

This is the first empirical study that examines the emotional appeal of fake and real news content with respect to the prevalence and strength of specific emotion dimensions, thus adding to the literature on fake news identification and marketing communications. In addition, this paper provides marketing communications professionals with a practical approach to identify fake news using AI.

By mapping-out the capabilities, challenges and limitations of named-entity recognition (NER), this article aims to synthesise the state of the art of NER in the context of the early modern research field and to inform discussions about the kind of resources, methods and directions that may be pursued to enrich the application of the technique going forward.

Through an extensive literature review, this article maps out the current capabilities, challenges and limitations of NER and establishes the state of the art of the technique in the context of the early modern, digitally augmented research field. It also presents a new case study of NER research undertaken by Enlightenment Architectures: Sir Hans Sloane's Catalogues of his Collections (2016–2021), a Leverhulme funded research project and collaboration between the British Museum and University College London, with contributing expertise from the British Library and the Natural History Museum.

Currently, it is not possible to benchmark the capabilities of NER as applied to documents of the early modern period. The authors also draw attention to the situated nature of authority files, and current conceptualisations of NER, leading them to the conclusion that more robust reporting and critical analysis of NER approaches and findings is required.

This article examines NER as applied to early modern textual sources, which are mostly studied by Humanists. As addressed in this article, detailed reporting of NER processes and outcomes is not necessarily valued by the disciplines of the Humanities, with the result that it can be difficult to locate relevant data and metrics in project outputs. The authors have tried to mitigate this by contacting projects discussed in this paper directly, to further verify the details they report here.

The authors suggest that a forum is needed where tools are evaluated according to community standards. Within the wider NER community, the MUC and ConLL corpora are used for such experimental set-ups and are accompanied by a conference series, and may be seen as a useful model for this. The ultimate nature of such a forum must be discussed with the whole research community of the early modern domain.

NER is an algorithmic intervention that transforms data according to certain rules-, patterns- or training data and ultimately affects how the authors interpret the results. The creation, use and promotion of algorithmic technologies like NER is not a neutral process, and neither is their output A more critical understanding of the role and impact of NER on early modern documents and research and focalization of some of the data- and human-centric aspects of NER routines that are currently overlooked are called for in this paper.

This article presents a state of the art snapshot of NER, its applications and potential, in the context of early modern research. It also seeks to inform discussions about the kinds of resources, methods and directions that may be pursued to enrich the application of NER going forward. It draws attention to the situated nature of authority files, and current conceptualisations of NER, and concludes that more robust reporting of NER approaches and findings are urgently required. The Appendix sets out a comprehensive summary of digital tools and resources surveyed in this article.

This paper presents a co-authorship study of authors who published in Digital Humanities journals and examines the apparent influence of gender, or more specifically, the quantitatively detectable influence of gender in the networks they form.

This study applied co-authorship network analysis. Data has been collected from three canonical Digital Humanities journals over 52 years (1966–2017) and analysed.

The results are presented as visualised networks and suggest that female scholars in Digital Humanities play more central roles and act as the main bridges of collaborative networks even though overall female authors are fewer in number than male authors in the network.

This is the first co-authorship network study in Digital Humanities to examine the role that gender appears to play in these co-authorship networks using statistical analysis and visualisation.