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The purpose of this study is to illustrate the development of ontology for the heroes of the ancient Greek mythology and religion. At present, a number of ontologies exist in different domains. However, ontologies of epics and myths are comparatively very few. To be more specific, nobody has developed such ontology for Greek mythology. This paper describes the attempts at developing ontology for Greek mythology to fill this gap.

This paper follows a combination of different methodologies, which is assumed to be a more effective way of developing ontology for mythology. It has adopted motivating scenario concept from Gruninger and Fox, developing cycle from Methontology and the analytico–synthetic approach from yet another methodology for ontology, and hence, it is a combination of three existing approaches.

A merged methodology has been adopted for this paper. The developed ontology was evaluated and made to meet with the information needs of its users. On the basis of the study, it was found that Greek mythology ontology could answer 62 per cent of the questions after first evaluation, i.e. 76 out of the 123 questions. The unanswered questions were analyzed in detail for further development of the ontology. The missing concepts were fed into the ontology; the ontology obtained after this stage was an exhaustive one.

This ontology will grow with time and can be used in semantic applications or e-learning modules related to the domain of Greek mythology.

This work is the first attempt to build ontology for Greek mythology. The approach is unique in that it has attempted to trace out the individual characteristics as well as the relationship between the characters described in the work.

Owing to the worldwide outbreak of the SARS-CoV-2, social media conversations have increased. Given the increasing pressure from regulatory authorities and society, green accounting – as a dimension of sustainable development – remains the most discussed topic on most social media platforms. This study aims to incorporate a technological approach to green accounting and sustainability to enhance the innovation process inside and outside organizations.

This study uses the hermeneutic phenomenological technique to investigate Twitter content. Tweets were subjected to a manual coding process to analyze their content, including recent advancements, challenges, cross-country initiatives and promotion strategies in green accounting. Public perception of green accounting and the COP26 climate summit was also studied.

Tweeters view green accounting favorably; however, they are apprehensive about its implementation. Regarding the challenges in green accounting, “corporate green washing” was the most tweeted content. The UK was the top-rated nation with respect to green accounting development. Furthermore, the most discussed breakthrough was the application of artificial intelligence in the domain of green accounting functions. However, Twitter users were observed to have directed heavy criticism at the COP26 climate summit in Glasgow.

This study’s primary innovation is its integration of emerging technologies such as machine learning and data mining with social media platforms such as Twitter. Incorporating manual coding of tweets is a rigorous procedure that amplifies the strength of machine learning software’s auto-coding feature.

Under the background of open science, this paper integrates altmetrics data and combines multiple evaluation methods to analyze and evaluate the indicators' characteristics of discourse leading for academic journals, which is of great significance to enrich and improve the evaluation theory and indicator system of academic journals.

This paper obtained 795,631 citations and 10.3 million altmetrics indicators data for 126,424 published papers from 151 medicine, general and internal academic journals. In this paper, descriptive statistical analysis and distribution rules of evaluation indicators are first carried out at the macro level. The distribution characteristics of evaluation indicators under different international collaboration conditions are analyzed at the micro level. Second, according to the characteristics and connotation of the evaluation indicators, the evaluation indicator system is constructed. Third, correlation analysis, factor analysis, entropy weight method and TOPSIS method are adopted to evaluate and analyze the discourse leading in medicine, general and internal academic journals by integrating altmetrics. At the same time, this paper verifies the reliability of the evaluation results.

Six features of discourse leading integrated with altmetrics indicators are obtained. In the era of open science, online academic exchanges are becoming more and more popular. The evaluation activities based on altmetrics have fine-grained and procedural advantages. It is feasible and necessary to integrate altmetrics indicators and combine the advantages of multiple methods to evaluate the academic journals' discourse leading of which are in a diversified academic ecosystem.

This paper uses descriptive statistical analysis to analyze the distribution characteristics and distribution rules of discourse leading indicators of academic journals and to explore the availability of altmetrics indicators and the effectiveness of constructing an evaluation system. Then, combining the advantages of multiple evaluation methods, The author integrates altmetrics indicators to comprehensively evaluate the discourse leading of academic journals and verify the reliability of the evaluation results. This paper aims to provide references for enriching and improving the evaluation theory and indicator system of academic journals.

This study aims to present scientometric mapping and altmetric analysis of publications related to “Hockey” in the past three decades. By using the advanced analytical techniques of mapping coupled with altmetric analysis, this paper aims to reveal the complex network of collaborations, the dispersion of expertise worldwide and prevailing thematic trends in the field of hockey.

The data was extracted from the Web of Science (WoS) database and Altmetric Explorer for articles related to hockey over the past three decades. VOSviewer was used to conduct network analysis whereas MS-Excel was used for altmetric data analysis. The study focused on the articles retrieved using the key term “Hockey” in English language publications. The altmetric attention scores (AAS) were used to measure the level of online attention on different platforms, complementing traditional bibliometric analysis.

The study reveals a notable increase in the productivity of hockey research over the past 30 years, with a specific focus on major surges in publication output and altmetric attention in recent times. Coauthorship and country-wise mapping analysis highlight global research collaboration trends, while keyword analysis underscores thematic concentrations. Key journals such as British Journal of Sports Medicine and American Journal of Sports Medicine emerge as crucial dissemination platforms. The importance of X posts (Formerly Twitter) and Mendeley in the diffusion of hockey literature is highlighted by altmetric research.

The study provides a concise overview of research conducted on the game of hockey. This research will be advantageous for researchers and individuals involved in the hockey community, as it offers bibliographic insights and aids in identifying suitable media for disseminating their findings.

This study applies digital humanity tools (Gephi and Protégé) for establishing and visualizing ontologies in the cultural heritage domain. According to that, this study aims to develop a novel evaluation approach using five ontology indicators (data overview, visual presentation, highlight links, scalability and querying) to evaluate the knowledge structure presentation of cultural heritage ontology.

The researchers collected and organized 824 pieces of government’s open data (GOD), converted GOD into the resource description framework format, applied Protégé and Gephi to establish and visualize cultural heritage ontology. After ontology is built, this study recruited 60 ontology participants (30 from information and communications technology background; 30 from cultural heritage background) to operate this ontology and gather their different perspectives of visual ontology.

Based on the ontology participant’s feedback, this study discovered that Gephi is more supporting than Protégé when visualizing ontology. Especially in data overview, visual presentation and highlight links dimensions, which is supported visualization and demonstrated ontology class hierarchy and property relation, facilitated the wider application of ontology.

This study offers two contributions. First, the researchers analyzed data on East Asian architecture with novel digital humanities tools to visualize ontology for cultural heritage. Second, the study collected participant’s feedback regarding the visualized ontology to enhance its design, which can serve as a reference for future ontological development.

This study provides an overview of the application of sentiment analysis (SA) in exploring social movements (SMs). It also compares different models for a SA task of Algerian Arabic tweets related to early days of the Algerian SM, called Hirak.

Related tweets were retrieved using relevant hashtags followed by multiple data cleaning procedures. Foundational machine learning methods such as Naive Bayes, Support Vector Machine, Logistic Regression (LR) and Decision Tree were implemented. For each classifier, two feature extraction techniques were used and compared, namely Bag of Words and Term Frequency–Inverse Document Frequency. Moreover, three fine-tuned pretrained transformers AraBERT and DziriBERT and the multilingual transformer XLM-R were used for the comparison.

The findings of this paper emphasize the vital role social media played during the Hirak. Results revealed that most individuals had a positive attitude toward the Hirak. Moreover, the presented experiments provided important insights into the possible use of both basic machine learning and transfer learning models to analyze SA of Algerian text datasets. When comparing machine learning models with transformers in terms of accuracy, precision, recall and F1-score, the results are fairly similar, with LR outperforming all models with a 68 per cent accuracy rate.

At the time of writing, the Algerian SM was not thoroughly investigated or discussed in the Computer Science literature. This analysis makes a limited but unique contribution to understanding the Algerian Hirak using artificial intelligence. This study proposes what it considers to be a unique basis for comprehending this event with the goal of generating a foundation for future studies by comparing different SA techniques on a low-resource language.