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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 The approach comprises two analysis phases: the time-aware semantic analysis of users’ historical content incorporating five commonly used machine learning classifiers.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 study is to investigate the determinants that likely influence the intention of using digital payment systems such as the Jordan Mobile Payment (JoMoPay) system among public sector employees in Jordan. To achieve the purpose of the current study, the authors developed a new research model based on the extended unified theory of acceptance and use of technology (UTAUT2), with one of Hofstede’s cross-cultural dimension scales [uncertainty avoidance (UA)] to provide a further understanding of the JoMoPay system acceptance in Jordan.

A partial least squares-structural equation modeling approach was used to analyze the data collected by self-administration from the 270 employees working in the Jordanian public sector located in Amman city, the capital city of Jordan. Because most main public sectors are located in Amman and because of the cost and time considerations, the current study applied a non-probability sampling with the purposive sampling technique.

The empirical results reveal that the evident drivers of behavioral intention to use the JoMoPay system are significantly and positively influenced by social influence, UA, performance expectancy, price value and effort expectancy; therefore, the H1, H2, H3, H5 and H6 were supported. Conversely, the results show no significant relationship between facilitating conditions and the behavioral intention to use the JoMoPay system, and hence, the related hypothesis (H4) was not supported.

The results of this study provide beneficial information to the Central Bank of Jordan and other service providers in Jordan about employee intentions to adopt JoMoPay system and increase decision-makers’ knowledge on factors that have an important impact in UTAUT2 model.

The results of this study enable policymakers to understand the important factors that will enhance savings, investments and living standards, create job opportunities as well as reduce the poverty, the paper money printing cost, risks of money transportation cost and the risk of human errors.

The outcomes obtained will help both practitioners and researchers elucidate and understand the situation of digital payment systems acceptance among Jordanian public sector employees, as well as help them formulate plans to expedite the adoption process of digital payment systems in the case of UA.