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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.

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.

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.

Owing to the increased accessibility of internet and related technologies, more and more individuals across the globe now turn to social media for their daily dose of news rather than traditional news outlets. With the global nature of social media and hardly any checks in place on posting of content, exponential increase in spread of fake news is easy. Businesses propagate fake news to improve their economic standing and influencing consumers and demand, and individuals spread fake news for personal gains like popularity and life goals. The content of fake news is diverse in terms of topics, styles and media platforms, and fake news attempts to distort truth with diverse linguistic styles while simultaneously mocking true news. All these factors together make fake news detection an arduous task. This work tried to check the spread of disinformation on Twitter.

This study carries out fake news detection using user characteristics and tweet textual content as features. For categorizing user characteristics, this study uses the XGBoost algorithm. To classify the tweet text, this study uses various natural language processing techniques to pre-process the tweets and then apply a hybrid convolutional neural network–recurrent neural network (CNN-RNN) and state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) transformer.

This study uses a combination of machine learning and deep learning approaches for fake news detection, namely, XGBoost, hybrid CNN-RNN and BERT. The models have also been evaluated and compared with various baseline models to show that this approach effectively tackles this problem.

This study proposes a novel framework that exploits news content and social contexts to learn useful representations for predicting fake news. This model is based on a transformer architecture, which facilitates representation learning from fake news data and helps detect fake news easily. This study also carries out an investigative study on the relative importance of content and social context features for the task of detecting false news and whether absence of one of these categories of features hampers the effectiveness of the resultant system. This investigation can go a long way in aiding further research on the subject and for fake news detection in the presence of extremely noisy or unusable data.

This paper is the first half of an invited paper given by the author to the international conference, promoted by the UNESCO Philosophy Forum, to celebrate the fiftieth anniversary of the founding of the organisation (Paris, 14–17 March 1995). The second half, which deals with a slightly different theme, is published as a Brief Communication later in this issue.

This research paper explores the roles of electronic texts in research projects in the humanities and seeks to deepen the understanding of the nature of scholars' engagement with e-texts. The study used qualitative methodology to explore engagement of scholars in literary and historical studies with primary materials in electronic form (i.e., e-texts). The study revealed a range of scholars' interactions with e-texts during the whole research process. It uncovered a particular pattern of information-seeking practices in electronic environments called netchaining and the main types of uses and contributions of e-texts to research projects. It was found that e-texts play support and substantive roles in the research process. A number of influences from electronic environment are identified as challenges and aids in working with e-texts. The study does not have statistical significance. It indicates a need for further research into scholarly practices, training requirements, and new forms of service provision. Study results are relevant for the development of digital collections, information services, educational programs, and other forms of support for the use of technology in research. The results can be also used to inform approaches to text encoding and development of electronic information systems and have implications for organizational and industry policies. The study found a range of scholars' interactions and forms of intellectual engagement with e-texts that were not documented and analyzed by earlier studies. It provides insights into disciplinary variations in the humanities and contributes to the understanding of scholarly change catalyzed by information technology.