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The purpose of this paper is to tackle the problem of technology addiction by investigating the differences between the antecedences of addictive (problematic technology usage) and high-engagement behavior (non-problematic technology usage). The case of social networking site usage (SNS, e.g. Facebook, Instagram or Twitter) is taken as the case out of the reason of prevalent user population.

It is revealed that people tend to use SNS not only for building a relationship, but also for communicating. In other words, there are inner needs of adopting the SNS technology. However, no clear definitions can be followed for determining the problematic SNS usage, addictive behavior and the high-engagement behavior. Therefore, this study adopts the notion of uses and gratification theory (U&G theory) for investigating the SNS usage behavior. Also, the social anxiety is also first introduced to integrate into the research for an empirical study.

Results reveal that gratification sought and relationship maintenance are associated with the addictive behavior, whereas the relationship maintenance is significantly related to high-engagement behavior.

First, the selected data represents a sample of SNW users in the Asian Pacific region and mainly from the group of young college users. Therefore, caution must be taken when generalizing the findings to other SNW users or groups. Second, the time aspect related to social media dependence may need to be considered in future studies. Third, the authors found marginal support for the influence of intentions of high engagement¸ and future studies may consider applying other theories that could better explain these types of behavior.

The results of this study provide strong evidence that inner anxiety perceived by users should not be neglected while tackling the problematic internet use due to SNW addiction because it can strengthen the force for depending on SNW for seeking social support. Apart from the value of perceived enjoyment as asserted in previous studies, this study opens up a new opportunity to tackle SNW dependence.

The key implication of this research is that the impact of the mental health of users on SNW problematic should not be overlooked . The higher the level of anxiety perceived, the more likely is the SNW dependence. Therefore, the online behavior depending on psychological health should be addressed because it may be a critical point for assisting users to adopt SNW wisely.

This study confirms that social anxiety people experience in real (offline) life has impacts on online behavior of SNS usage (online). It suggests that the difference between users as the perceived level of social anxiety can trigger different levels of SNS usage. Second, U&G theory is proven valid in understanding SNS addiction. Third, relationship maintenance through the use of SNS reveals its dissimilar effects on SNS addiction and high engagement.

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.