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Digital humanities aim to use a digital-based revolutionary new way to carry out enhanced forms of humanities research more effectively and efficiently. This study develops a character social network relationship map tool (CSNRMT) that can semi-automatically assist digital humanists through human-computer interaction to more efficiently and accurately explore the character social network relationships from Chinese ancient texts for useful research findings.

With a counterbalanced design, semi-structured in-depth interview, and lag sequential analysis, a total of 21 research subjects participated in an experiment to examine the system effectiveness and technology acceptance of adopting the ancient book digital humanities research platform with and without the CSNRMT to interpret the characters and character social network relationships.

The experimental results reveal that the experimental group with the CSNRMT support appears higher system effectiveness on the interpretation of characters and character social network relationships than the control group without the CSNRMT, but does not achieve a statistically significant difference. Encouragingly, the experimental group with the CSNRMT support presents remarkably higher technology acceptance than the control group without the CSNRMT. Furthermore, use behaviors analyzed by lag sequential analysis reveal that the CSNRMT could assist digital humanists in the interpretation of character social network relationships. The results of the interview present positive opinions on the integration of system interface, smoothness of operation, and external search function.

Currently, the system effectiveness of exploring the character social network relationships from texts for useful research findings by using the CSNRMT developed in this study will be significantly affected by the accuracy of recognizing character names and character social network relationships from Chinese ancient texts. The developed CSNRMT will be more practical when the offered information about character names and character social network relationships is more accurate and broad.

This study develops an ancient book digital humanities research platform with an emerging CSNRMT that provides an easy-to-use real-time interaction interface to semi-automatically support digital humanists to perform digital humanities research with the need of exploring character social network relationships.

At present, a real-time social network analysis tool to provide a friendly interaction interface and effectively assist digital humanists in the digital humanities research with character social networks analysis is still lacked. This study thus presents the CSNRMT that can semi-automatically identify character names from Chinese ancient texts and provide an easy-to-use real-time interaction interface for supporting digital humanities research so that digital humanists could more efficiently and accurately establish character social network relationships from the analyzed texts to explore complicated character social networks relationship and find out useful research findings.

The purpose of this paper is to propose new directions for human resource management (HRM) research by drawing attention to online data as a complementary data source to traditional quantitative and qualitative data, and introducing network text analysis as a method for large quantities of textual material.

The paper first presents the added value and potential challenges of utilising online data in HRM research, and then proposes a four-step process for analysing online data with network text analysis.

Online data represent a naturally occuring source of real-time behavioural data that do not suffer from researcher intervention or hindsight bias. The authors argue that as such, this type of data provides a promising yet currently largely untapped empirical context for HRM research that is particularly suited for examining discourses and behavioural and social patterns over time.

While online data hold promise for many novel research questions, it is less appropriate for research questions that seek to establish causality between variables. When using online data, particular attention must be paid to ethical considerations, as well as the validity and representativeness of the sample.

The authors introduce online data and network text analysis as a new avenue for HRM research, with potential to address novel research questions at micro-, meso- and macro-levels of analysis.

This paper is an exploratory study of one approach to incorporating situational information into information retrieval systems, drawing on principles and methods of discourse linguistics. A tenet of discourse linguistics is that texts of a specific type possess a structure above the syntactic level, which follows conventions known to the people using such texts to communicate. In some cases, such as literature describing work done, the structure is closely related to situations, and may therefore be a useful representational vehicle for the present purpose. Abstracts of empirical research papers exhibit a well‐defined discourse‐level structure, which is revealed by lexical clues. Two methods of detecting the structure automatically are presented: (i) a Bayesian probabilistic analysis; and (ii) a neural network model. Both methods show promise in preliminary implementations. A study of users' oral problem statements indicates that they are not amenable to the same kind of processing. However, from in‐depth interviews with users and search intermediaries, the following conclusions are drawn: (i) the notion of a generic research script is meaningful to both users and intermediaries as a high‐level description of situation; (ii) a researcher's position in the script is a predictor of the relevance of documents; and (iii) currently, intermediaries can make very little use of situational information. The implications of these findings for system design are discussed, and a system structure presented to serve as a framework for future experimental work on the factors identified in this paper. The design calls for a dialogue with the user on his or her position in a research script and incorporates features permitting discourse‐level components of abstracts to be specified in search strategies.

Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text expresses a positive or negative tone. Although this novel method has opened an exciting new avenue for organizational research – mainly due to the abundantly available text data in organizations and the well-developed sentiment analysis techniques, it has also posed a serious challenge to many organizational researchers. This chapter aims to introduce the sentiment analysis method in the text mining area to the organizational research community. In this chapter, the authors first briefly discuss the central role of sentiment in organizational research and then introduce the traditional and modern approaches to sentiment analysis. The authors further delineate research paradigms for text analysis research, advocating the iterative research paradigm (cf., inductive and deductive research paradigms) that is more suitable for text mining research, and also introduce the analytical procedures for sentiment analysis with three stages – discovery, measurement, and inference. More importantly, the authors highlight both the dictionary-based and machine learning (ML) approaches in the measurement stage, with special coverage on deep learning and word embedding techniques as the latest breakthroughs in sentiment and text analyses. Lastly, the authors provide two illustrative examples to demonstrate the applications of sentiment analysis in organizational research. It is the authors’ hope that this chapter – by providing these practical guidelines – will help facilitate more applications of this novel method in organizational research in the future.

The purpose of this study is to examine the extent to which dynamic network analysis (DNA), text mining and natural language processing (NLP) are helpful research tools in identifying the key actors in a complex international crisis. The study uses these tools to identify the key actors in the Syrian crisis as a case study to validate the proposed algorithm.

To achieve its main purpose, the study uses a collection of three methodologies, namely, DNA, text mining and NLP.

The results of the analysis show four key actors in the Syrian crisis, namely, Russia, the USA, Turkey and China. The results also reveal changes in their powerful positions from 2012 to 2016, which matches the changes that occurred in the real world. The matching between the findings of the proposed algorithm and the real world events that happened in Syria validate our proposed algorithm and proves that the algorithm can be used in identifying the key actors in complex international crises.

The importance of the study lies in two main points. It proposes a new algorithm that mixes NLP, network extraction from textual unstructured data and DNA to understand and monitor changes occurring in a complex international crisis. It applies the proposed algorithm on the Syrian crisis as a case study to identify the key actors and hence validate the proposed algorithm.

COVID-19, a causative agent of the potentially fatal disease, has raised great global public health concern. Information spreading on the COVID-19 outbreak can strongly influence people behaviour in social media. This paper aims to question of information spreading on COVID-19 outbreak are addressed with a massive data analysis on Twitter from a multidimensional perspective.

The evolutionary trend of user interaction and the network structure is analysed by social network analysis. A differential assessment on the topics evolving is provided by the method of text clustering. Visualization is further used to show different characteristics of user interaction networks and public opinion in different periods.

Information spreading in social media emerges from different characteristics during various periods. User interaction demonstrates multidimensional cross relations. The results interpret how people express their thoughts and detect topics people are most discussing in social media.

This study is mainly limited by the size of the data sets and the unicity of the social media. It is challenging to expand the data sets and choose multiple social media to cross-validate the findings of this study.

This paper aims to find the evolutionary trend of information spreading on the COVID-19 outbreak in social media, including user interaction and topical issues. The findings are of great importance to help government and related regulatory units to manage the dissemination of information on emergencies, in terms of early detection and prevention.

The purpose of this study to shed light on the importance of social media hosted content related to socially-motivated discussions. Moving from the field of communication for development, the research leverages social media as a powerful tool for collecting and analyse peer-to-peer communication towards the conceptualization of eVoices of Unheard. The deep understanding of these conversation can generate recommendations for organizations and governments designing and providing interventions fostering local socio-economic development.

The study presents a large-scale analysis of social media interactions on the topic “#favela” to generate insights into a social network structure, narrative contents and meaning generated.

Structurally, the analysed networks are comparable with those presented in current academic literature; automatic text analysis confirmed the promise of the inner value of communication for development opening the floor to conceptualization of the “eVoices of unheard”, which is the collective and conscious use of social media to mediate community discussions about tangible and intangible issues related to socio-economic development.

Framed within the rise of interactive communication for development this research show that social media an support the notion of voice proposed by Couldry (2010) moving from process (i.e. the recording of the voice) towards value (i.e. the possibility of giving an account of one’s life and its conditions to have an impact on human life and resources) thereby understanding intangible issues related with socio-economic development.

Philosophical reflection is a reflection of a school’s organizational structure. This study employs formal and computational methods to examine closely the culture/structure duality in the Frankfurt School’s formation and fragmentation over several decades by examining the homology between its social and conceptual networks.

On the one side, I produce social structural data from archival research on the Frankfurt School’s set of social relations. On the other side, I use computer-assisted textual analysis to produce concept maps of key texts by the same thinkers. Analyzing these networks jointly, I then investigate the dyadic social and cultural processes that contributed to the school’s fragmentation and show that:

1. The Frankfurt School’s social structure and idea structure were positively correlated over three decades as the school moved from an era of social and intellectual coherence to an era of fragmentation.

2. While we normally imagine the duality of structure and culture as a positive correlation between social and cultural relations, it can also appear as a strong negative correlation. Leo Löwenthal’s expulsion from the school is such a case. As a peripheral member, Löwenthal’s attempt to engage more strongly with the school’s core ideas was interpreted as presumptuous and low quality by core members who strictly policed the social and intellectual structure of the school. As a result of his ambition, Löwenthal was expelled.

The Frankfurt School’s social structure and idea structure were positively correlated over three decades as the school moved from an era of social and intellectual coherence to an era of fragmentation.

While we normally imagine the duality of structure and culture as a positive correlation between social and cultural relations, it can also appear as a strong negative correlation. Leo Löwenthal’s expulsion from the school is such a case. As a peripheral member, Löwenthal’s attempt to engage more strongly with the school’s core ideas was interpreted as presumptuous and low quality by core members who strictly policed the social and intellectual structure of the school. As a result of his ambition, Löwenthal was expelled.

This paper develops a semantic network approach to analyzing the relation between structural and cultural ties while illustrating the complex ways in which cultural and structural facets of a philosophical school develop in a duality.

Integrated reporting (IR) is increasingly becoming a practice useful not only for accountability but also for managerial purposes because of its potential role as a signifying practice for integrated thinking (IT). In this perspective, this paper aims to explore which of the objects that are represented in integrated reports provide materiality and common understanding to the concept of IT for its effective implementation within organizations.

This paper is based on a vocabulary approach for interpreting the texts of integrated reports as systems of words that are able to provide meaning for a common understanding of the concept of IT. In particular, by focusing on words and their relationships, the authors combine textual analysis and network text analysis to examine the structure of meaning embedded in the texts of integrated reports of five organizations, which serve as empirical cases for analysis during the period 2012-2018.

The concept of IT is dynamic in its meaning since in integrated reports it is represented by referring to different objects, in the case different types of capital (i.e. financial, human, social-relational, process, organizational and commercial), which are related to each other while following different paths over time. The dynamic nature of the meaning of IT affects the semantic orientation of the reports in a mutual relationship between IT (which conveys flows of information within the reports) and integrated reports (through which flows of meaning are made visible).

This paper opens the way to a linguistic approach for analyzing the different concepts related to IT to make them meaningful in creating (at least temporarily) a common understanding, as well as facilitating coordination within organizations and between organizations and their environment.

Sustainable construction is widely considered to be the best practice in construction, helping to create a healthy built environment. Social media is identified as a valuable data source for research on sustainable construction, and Twitter is a popular social media platform in relation to the construction. Green Building construction is identified as one of the methods that promotes sustainable construction. The purpose of this study is to characterise “Green Building” as a topic in Twitter.

Social network analysis methods were applied to a large set of Twitter data related to “green building”. Time sequence analysis and network visualisation were used to characterise Twitter activity and to identify influential users. Text analytics and visualisation methods were applied to the same data set to visualise the text content of Twitter posts relating to green building.

Peaks in Twitter activity were associated with physical “green building” events. The network visualisation of the Twitter data revealed a complex structure and a range of types of interactions. The most “influential” users depended on the ranking method used; however, a number of users had high influence in all measures used. The tweet text visualisation showed evidence of a global and interactive audience on Twitter engaged in conversations about green building. Also, it was found that external links, emoji and popular terms related to a particular topic can be used to increase the engagement of Twitter users on that topic.

Certain Green Building events were observed to be associated with high levels of Twitter activity. The virtual was found to be closely linked to the physical, and for the promotion of green building construction, their respective impact is potentially the most powerful when used in conjunction. The most influential Twitter accounts did not belong to one class of user, including both individuals and organisations. Twitter offers a platform for a range of stakeholders in the area of green building construction to reach a substantial audience and to be influential in the public sphere. The findings of this research provide a valuable reference for industry practitioners and researchers to deepen their understanding of the application of Twitter to green building construction, and the methods of using Twitter to promote important information related to sustainable construction.