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This paper aims to obtain the domain of the textual content generated by users of online social network (OSN) platforms. Understanding a users’ domain (s) of interest is a significant step towards addressing their domain-based trustworthiness through an accurate understanding of their content in their OSNs.

This study uses a Twitter mining approach for domain-based classification of users and their textual content. The proposed approach incorporates machine learning modules. The approach comprises two analysis phases: the time-aware semantic analysis of users’ historical content incorporating five commonly used machine learning classifiers. This framework classifies users into two main categories: politics-related and non-politics-related categories. In the second stage, the likelihood predictions obtained in the first phase will be used to predict the domain of future users’ tweets.

Experiments have been conducted to validate the mechanism proposed in the study framework, further supported by the excellent performance of the harnessed evaluation metrics. The experiments conducted verify the applicability of the framework to an effective domain-based classification for Twitter users and their content, as evident in the outstanding results of several performance evaluation metrics.

This study is limited to an on/off domain classification for content of OSNs. Hence, we have selected a politics domain because of Twitter’s popularity as an opulent source of political deliberations. Such data abundance facilitates data aggregation and improves the results of the data analysis. Furthermore, the currently implemented machine learning approaches assume that uncertainty and incompleteness do not affect the accuracy of the Twitter classification. In fact, data uncertainty and incompleteness may exist. In the future, the authors will formulate the data uncertainty and incompleteness into fuzzy numbers which can be used to address imprecise, uncertain and vague data.

This study proposes a practical framework comprising significant implications for a variety of business-related applications, such as the voice of customer/voice of market, recommendation systems, the discovery of domain-based influencers and opinion mining through tracking and simulation. In particular, the factual grasp of the domains of interest extracted at the user level or post level enhances the customer-to-business engagement. This contributes to an accurate analysis of customer reviews and opinions to improve brand loyalty, customer service, etc.

This paper fills a gap in the existing literature by presenting a consolidated framework for Twitter mining that aims to uncover the deficiency of the current state-of-the-art approaches to topic distillation and domain discovery. The overall approach is promising in the fortification of Twitter mining towards a better understanding of users’ domains of interest.

The purpose of this paper is to develop and empirically test a process model (comprising of seven dimensions), for identifying online customer engagement patterns leading to recommendation. These seven dimensions are communication, interaction, experience, satisfaction, continued involvement, bonding, and recommendation.

The authors used a non-participant form of netnography for analyzing 849 comments from Australian banks Facebook pages. High levels of inter-coder reliability strengthen the study’s empirical validity and ensure minimum researcher bias and maximum reliability and replicability.

The authors identified 22 unique pattern of customer engagement, out of which nine patterns resulted in recommendation/advocacy. Engagement pattern communication-interaction-recommendation was the fastest route to recommendation, observed in nine instances (or 2 percent). In comparison, C-I-E-S-CI-B-R was the longest route to recommendation observed in ninety-six instances (or 18 percent). Of the eight patterns that resulted in recommendation, five patterns (or 62.5 percent) showed bonding happening before recommendation.

The authors limited the data collection to Facebook pages of major banks in Australia. The authors did not assess customer demography and did not share the findings with the banks.

The findings will guide e-marketers on how to best engage with customers to enhance brand loyalty and continuously be in touch with their clients.

Most models are conceptual and assume that customers typically journey through all the stages in the model. The work is interesting because the empirical study found that customers travel in multiple different ways through this process. It is significant because it changes the way the authors understand patterns of online customer engagement.

There is an increasing interest in using information and communication technologies to support health services. But the adoption and development of even basic ICT communications services in many health services is limited, leaving enormous gaps in the broad understanding of its role in health care delivery. The purpose of this paper is to address a specific (intercultural) area of healthcare communications consumer disadvantage; and it examines the potential for ICT exploitation through the lens of a conceptual framework. The opportunity to pursue a new solutions pathway has been amplified in recent times through the development of computer-based ontologies and the resultant knowledge from ontologist activity and consequential research publishing.

A specific intercultural area of patient disadvantage arises from variations in meaning and understanding of patient and clinician words, phrases and non-verbal expression. Collection and localization of data concepts, their attributes and individual instances were gathered from an Aboriginal trainee nurse focus group and from a qualitative gap analysis (QGA) of 130 criteria-selected sources of literature. These concepts, their relationships and semantic interpretations populate the computer ontology. The ontology mapping involves two domains, namely, Aboriginal English (AE) and Type II diabetes care guidelines. This is preparatory to development of the Patient Practitioner Assistive Communications (PPAC) system for Aboriginal rural and remote patient primary care.

The combined QGA and focus group output reported has served to illustrate the call for three important drivers of change. First, there is no evidence to contradict the hypothesis that patient-practitioner interview encounters for many Australian Aboriginal patients and wellbeing outcomes are unsatisfactory at best. Second, there is a potent need for cultural competence knowledge and practice uptake on the part of health care providers; and third, the key contributory component to determine success or failures within healthcare for ethnic minorities is communication. Communication, however, can only be of value in health care if in practice it supports shared cognition; and mutual cognition is rarely achievable when biopsychosocial and other cultural worldview differences go unchallenged.

There has been no direct engagement with remote Aboriginal communities in this work to date. The authors have initially been able to rely upon a cohort of both Indigenous and non-Indigenous people with relevant cultural expertise and extended family relationships. Among these advisers are health care practitioners, academics, trainers, Aboriginal education researchers and workshop attendees. It must therefore be acknowledged that as is the case with the QGA, the majority of the concept data is from third parties. The authors have also discovered that urban influences and cultural sensitivities tend to reduce the extent of, and opportunity to, witness AE usage, thereby limiting the ability to capture more examples of code-switching. Although the PPAC system concept is qualitatively well developed, pending future work planned for rural and remote community engagement the authors presently regard the work as mostly allied to a hypothesis on ontology-driven communications. The concept data population of the AE home talk/health talk ontology has not yet reached a quantitative critical mass to justify application design model engineering and real-world testing.

Computer ontologies avail us of the opportunity to use assistive communications technology applications as a dynamic support system to elevate the pragmatic experience of health care consultations for both patients and practitioners. The human-machine interactive development and use of such applications is required just to keep pace with increasing demand for healthcare and the growing health knowledge transfer environment. In an age when the worldwide web, communications devices and social media avail us of opportunities to confront the barriers described the authors have begun the first construction of a merged schema for two domains that already have a seemingly intractable negative connection. Through the ontology discipline of building syntactically and semantically robust and accessible concepts; explicit conceptual relationships; and annotative context-oriented guidance; the authors are working towards addressing health literacy and wellbeing outcome deficiencies of benefit to the broader communities of disadvantage patients.