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Article
Publication date: 19 October 2015

Eugene Ch'ng

The purpose of this paper is to present a Big Data solution as a methodological approach to the automated collection, cleaning, collation, and mapping of multimodal

Abstract

Purpose

The purpose of this paper is to present a Big Data solution as a methodological approach to the automated collection, cleaning, collation, and mapping of multimodal, longitudinal data sets from social media. The paper constructs social information landscapes (SIL).

Design/methodology/approach

The research presented here adopts a Big Data methodological approach for mapping user-generated contents in social media. The methodology and algorithms presented are generic, and can be applied to diverse types of social media or user-generated contents involving user interactions, such as within blogs, comments in product pages, and other forms of media, so long as a formal data structure proposed here can be constructed.

Findings

The limited presentation of the sequential nature of content listings within social media and Web 2.0 pages, as viewed on web browsers or on mobile devices, do not necessarily reveal nor make obvious an unknown nature of the medium; that every participant, from content producers, to consumers, to followers and subscribers, including the contents they produce or subscribed to, are intrinsically connected in a hidden but massive network. Such networks when mapped, could be quantitatively analysed using social network analysis (e.g. centralities), and the semantics and sentiments could equally reveal valuable information with appropriate analytics. Yet that which is difficult is the traditional approach of collecting, cleaning, collating, and mapping such data sets into a sufficiently large sample of data that could yield important insights into the community structure and the directional, and polarity of interaction on diverse topics. This research solves this particular strand of problem.

Research limitations/implications

The automated mapping of extremely large networks involving hundreds of thousands to millions of nodes, encapsulating high resolution and contextual information, over a long period of time could possibly assist in the proving or even disproving of theories. The goal of this paper is to demonstrate the feasibility of using automated approaches for acquiring massive, connected data sets for academic inquiry in the social sciences.

Practical implications

The methods presented in this paper, together with the Big Data architecture can assist individuals and institutions with a limited budget, with practical approaches in constructing SIL. The software-hardware integrated architecture uses open source software, furthermore, the SIL mapping algorithms are easy to implement.

Originality/value

The majority of research in the literature uses traditional approaches for collecting social networks data. Traditional approaches can be slow and tedious; they do not yield adequate sample size to be of significant value for research. Whilst traditional approaches collect only a small percentage of data, the original methods presented here are able to collect and collate entire data sets in social media due to the automated and scalable mapping techniques.

Details

Industrial Management & Data Systems, vol. 115 no. 9
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 27 December 2021

Zohreh Doborjeh, Nigel Hemmington, Maryam Doborjeh and Nikola Kasabov

Several review articles have been published within the Artificial Intelligence (AI) literature that have explored a range of applications within the tourism and…

1732

Abstract

Purpose

Several review articles have been published within the Artificial Intelligence (AI) literature that have explored a range of applications within the tourism and hospitality sectors. However, how efficiently the applied AI methods and algorithms have performed with respect to the type of applications and the multimodal sets of data domains have not yet been reviewed. Therefore, this paper aims to review and analyse the established AI methods in hospitality/tourism, ranging from data modelling for demand forecasting, tourism destination and behaviour pattern to enhanced customer service and experience.

Design/methodology/approach

The approach was to systematically review the relationship between AI methods and hospitality/tourism through a comprehensive literature review of papers published between 2010 and 2021. In total, 146 articles were identified and then critically analysed through content analysis into themes, including “AI methods” and “AI applications”.

Findings

The review discovered new knowledge in identifying AI methods concerning the settings and available multimodal data sets in hospitality and tourism. Moreover, AI applications fostering the tourism/hospitality industries were identified. It also proposes novel personalised AI modelling development for smart tourism platforms to precisely predict tourism choice behaviour patterns.

Practical implications

This review paper offers researchers and practitioners a broad understanding of the proper selection of AI methods that can potentially improve decision-making and decision-support in the tourism/hospitality industries.

Originality/value

This paper contributes to the tourism/hospitality literature with an interdisciplinary approach that reflects on theoretical/practical developments for data collection, data analysis and data modelling using AI-driven technology.

Details

International Journal of Contemporary Hospitality Management, vol. 34 no. 3
Type: Research Article
ISSN: 0959-6119

Keywords

Book part
Publication date: 30 November 2017

Dennis Jancsary, Renate E. Meyer, Markus A. Höllerer and Eva Boxenbaum

In this article, we develop and advance an understanding of institutions as multimodal accomplishments. We draw on social semiotics and the linguistic concept of…

Abstract

In this article, we develop and advance an understanding of institutions as multimodal accomplishments. We draw on social semiotics and the linguistic concept of metafunctions to establish the visual as a specific mode of meaning construction. In addition, we make semiotic modes conducive to institutional inquiry by introducing the notion of distinct “modal registers” – specialized configurations of linguistic signs within a particular mode that are adapted and applied in the reproduction of institutions or institutional domains. At the core of our article, we operationalize metafunctions to develop methodology for the analysis of visual registers. We illustrate our approach with data from Corporate Social Responsibility (CSR) reporting in Austria.

Article
Publication date: 11 May 2015

Eugene Ch'ng

The purpose of this paper is to explore the formation, maintenance and disintegration of a fringe Twitter community in order to understand if offline community structure…

971

Abstract

Purpose

The purpose of this paper is to explore the formation, maintenance and disintegration of a fringe Twitter community in order to understand if offline community structure applies to online communities.

Design/methodology/approach

The research adopted Big Data methodological approaches in tracking user-generated contents over a series of months and mapped online Twitter interactions as a multimodal, longitudinal “social information landscape”. Centrality measures were employed to gauge the importance of particular user nodes within the complete network and time-series analysis were used to track ego centralities in order to see if this particular online communities were maintained by specific egos.

Findings

The case study shows that communities with distinct boundaries and memberships can form and exist within Twitter’s limited user content and sequential policies, which unlike other social media services, do not support formal groups, demonstrating the resilience of desperate online users when their ideology overcome social media limitations. Analysis in this paper using social networks approaches also reveals that communities are formed and maintained from the bottom-up.

Research limitations/implications

The research data is based on a particular data set which occurred within a specific time and space. However, due to the rapid, polarising group behaviour, growth, disintegration and decline of the online community, the data set presents a “laboratory” case from which many other online community can be compared with. It is highly possible that the case can be generalised to a broader range of communities and from which online community theories can be proved/disproved.

Practical implications

The paper showed that particular group of egos with high activities, if removed, could entirely break the cohesiveness of the community. Conversely, strengthening such egos will reinforce the community strength. The questions mooted within the paper and the methodology outlined can potentially be applied in a variety of social science research areas. The contribution to the understanding of a complex social and political arena, as outlined in the paper, is a key example of such an application within an increasingly strategic research area – and this will surely be applied and developed further by the computer science and security community.

Originality/value

The majority of researches that cover these domains have not focused on communities that are multimodal and longitudinal. This is mainly due to the challenges associated with the collection and analysis of continuous data sets that have high volume and velocity. Such data sets are therefore unexploited with regards to cyber-community research.

Details

Industrial Management & Data Systems, vol. 115 no. 4
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 19 May 2020

Xu Du, Juan Yang, Jui-Long Hung and Brett Shelton

Educational data mining (EDM) and learning analytics, which are highly related subjects but have different definitions and focuses, have enabled instructors to obtain a…

Abstract

Purpose

Educational data mining (EDM) and learning analytics, which are highly related subjects but have different definitions and focuses, have enabled instructors to obtain a holistic view of student progress and trigger corresponding decision-making. Furthermore, the automation part of EDM is closer to the concept of artificial intelligence. Due to the wide applications of artificial intelligence in assorted fields, the authors are curious about the state-of-art of related applications in Education.

Design/methodology/approach

This study focused on systematically reviewing 1,219 EDM studies that were searched from five digital databases based on a strict search procedure. Although 33 reviews were attempted to synthesize research literature, several research gaps were identified. A comprehensive and systematic review report is needed to show us: what research trends can be revealed and what major research topics and open issues are existed in EDM research.

Findings

Results show that the EDM research has moved toward the early majority stage; EDM publications are mainly contributed by “actual analysis” category; machine learning or even deep learning algorithms have been widely adopted, but collecting actual larger data sets for EDM research is rare, especially in K-12. Four major research topics, including prediction of performance, decision support for teachers and learners, detection of behaviors and learner modeling and comparison or optimization of algorithms, have been identified. Some open issues and future research directions in EDM field are also put forward.

Research limitations/implications

Limitations for this search method include the likelihood of missing EDM research that was not captured through these portals.

Originality/value

This systematic review has not only reported the research trends of EDM but also discussed open issues to direct future research. Finally, it is concluded that the state-of-art of EDM research is far from the ideal of artificial intelligence and the automatic support part for teaching and learning in EDM may need improvement in the future work.

Details

Information Discovery and Delivery, vol. 48 no. 4
Type: Research Article
ISSN: 2398-6247

Keywords

Book part
Publication date: 4 August 2017

Colin Dingler, Alina A. von Davier and Jiangang Hao

Increased interest in team dynamics has resulted in new methods for measuring teamwork over time. The primary purpose of this chapter is to provide a survey of recent…

Abstract

Purpose

Increased interest in team dynamics has resulted in new methods for measuring teamwork over time. The primary purpose of this chapter is to provide a survey of recent developments in teamwork/collaboration measurement in an educational context. Key topics include conceptual frameworks, large-scale assessments, and innovative measurement techniques.

Methodology/approach

A range of methods for collecting and analyzing teamwork data are discussed, and five frameworks for measuring collaborative problem solving (CPS) over time are compared. Frameworks from Programme for International Student Assessment (PISA), Assessment and Teaching of 21st Century Skills (ATC21S) project, Educational Testing Service (ETS), ACT, and von Davier and Halpin (2013) are discussed. Results of assessments developed from these frameworks are also considered.

Social/practical implications

New techniques for measuring team dynamics over time have great potential to improve education and work outcomes. Preliminary results of the assessments developed from these frameworks show that important advances in teamwork measurement have been enabled by innovative task designs, data-mining techniques, and novel applications of stochastic models.

Originality/value

This novel overview and comparison of interdisciplinary approaches will help to indicate where progress has been made and what challenges are ahead.

Book part
Publication date: 12 November 2018

Nicole Brown and Jennifer Leigh

Due to the diversity of academics engaging with research into higher education, there is no single methodological approach or method that would embody higher education…

Abstract

Due to the diversity of academics engaging with research into higher education, there is no single methodological approach or method that would embody higher education research. In this chapter, we put forward the case that this is a good thing and argue that higher education research can benefit from fusing existing methodological and theoretical paradigms with more creative, playful and artistic approaches, more commonly associated with sociological or anthropological research and performance-based disciplines. In order to frame this attitude of creativity, playfulness and openness, we start by providing a brief delineation of the research field and methods of higher education research. In this context we introduce the Deleuzoguattarian concept of rhizomes and assemblages to provide the grounding for what we mean by creativity and playfulness, which leads to our proposal of a renewed approach to research into higher education. We draw upon our own work on embodied academic identity and trainee teachers’ perceptions of their placement experiences in order to critically explore the benefits and potential pitfalls of incorporating this creativity and playfulness into higher education research.

Details

Theory and Method in Higher Education Research
Type: Book
ISBN: 978-1-78769-277-0

Keywords

Abstract

Details

Break the Wall: Why and How to Democratize Digital in Your Business
Type: Book
ISBN: 978-1-80382-188-7

Article
Publication date: 9 January 2020

Joona Keränen and Daniel D. Prior

This paper highlights the suitability, application and fruitful opportunities for ethnographic methodologies in contemporary B2B service research.

Abstract

Purpose

This paper highlights the suitability, application and fruitful opportunities for ethnographic methodologies in contemporary B2B service research.

Design/methodology/approach

This paper is based on a literature review and conceptual analysis of ethnographic research methodology and B2B service literatures.

Findings

This paper discusses the central features of ethnographic research methodologies, their key differences to other qualitative methodologies, key trends in contemporary B2B service research and opportunities for ethnographic research methodologies in selected priority areas.

Research limitations/implications

This paper highlights the opportunities, unique strengths and specific advantages of ethnographic research methodologies to advance B2B service research and theory development.

Practical implications

This paper encourages B2B firms to undertake ethnographic field projects to better understand customers’ roles, experiences and usage processes that relate to B2B services.

Originality/value

Ethnographic research approaches have been largely overlooked or neglected in B2B service research. This paper highlights their potential, suggests areas for application and encourages B2B service researchers to adopt ethnographic approaches to delve deeper into the social and cultural aspects of B2B services

Article
Publication date: 8 February 2021

Gianluca Solazzo, Gianluca Elia and Giuseppina Passiante

This study aims to investigate the Big Social Data (BSD) paradigm, which still lacks a clear and shared definition, and causes a lack of clarity and understanding about…

Abstract

Purpose

This study aims to investigate the Big Social Data (BSD) paradigm, which still lacks a clear and shared definition, and causes a lack of clarity and understanding about its beneficial opportunities for practitioners. In the knowledge management (KM) domain, a clear characterization of the BSD paradigm can lead to more effective and efficient KM strategies, processes and systems that leverage a huge amount of structured and unstructured data sources.

Design/methodology/approach

The study adopts a systematic literature review (SLR) methodology based on a mixed analysis approach (unsupervised machine learning and human-based) applied to 199 research articles on BSD topics extracted from Scopus and Web of Science. In particular, machine learning processing has been implemented by using topic extraction and hierarchical clustering techniques.

Findings

The paper provides a threefold contribution: a conceptualization and a consensual definition of the BSD paradigm through the identification of four key conceptual pillars (i.e. sources, properties, technology and value exploitation); a characterization of the taxonomy of BSD data type that extends previous works on this topic; a research agenda for future research studies on BSD and its applications along with a KM perspective.

Research limitations/implications

The main limits of the research rely on the list of articles considered for the literature review that could be enlarged by considering further sources (in addition to Scopus and Web of Science) and/or further languages (in addition to English) and/or further years (the review considers papers published until 2018). Research implications concern the development of a research agenda organized along with five thematic issues, which can feed future research to deepen the paradigm of BSD and explore linkages with the KM field.

Practical implications

Practical implications concern the usage of the proposed definition of BSD to purposefully design applications and services based on BSD in knowledge-intensive domains to generate value for citizens, individuals, companies and territories.

Originality/value

The original contribution concerns the definition of the big data social paradigm built through an SLR the combines machine learning processing and human-based processing. Moreover, the research agenda deriving from the study contributes to investigate the BSD paradigm in the wider domain of KM.

Details

Journal of Knowledge Management, vol. 25 no. 7
Type: Research Article
ISSN: 1367-3270

Keywords

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