No abstract

Online social networks (OSNs) are structures that help users to interact, exchange, and propagate new ideas. The identification of the influential users in OSNs is a significant process for accelerating the propagation of information that includes marketing applications or hindering the dissemination of unwanted contents, such as viruses, negative online behaviors, and rumors. This article presents a detailed survey of influential users’ identification algorithms and their performance evaluation approaches in OSNs. The survey covers recent techniques, applications, and open research issues on analysis of OSN connections for identification of influential users.

Purpose-The purpose of this paper is to investigate the information-seeking behaviour of international students in terms of their information needs and to highlight the role of social media. Design/methodology/approach-In this paper, a systematic literature survey was conducted in order to investigate information-seeking trends among international students while using social media. As a result, an exhaustive systematic literature review (SLR) was carried out in order to investigate social media as a source for the observation of the behaviours of international students. For this purpose, 71 articles were selected from various well-known sources after an intensive SLR process of searching, filtering and enforcing the inclusion and exclusion criteria. Findings-As an outcome of this study, the information-seeking behaviour of international students was highlighted with respect to social media as a source of information. In addition, this research identifies the information needs of the international students and categorizes them by the roles played by the social media in fulfilling the information needs. Practical implications-A comparative study that highlighted the dearth of studies which merge the social media and information-seeking behaviour of international students as well as identify the future direction for the researchers and for benefits of international students. Originality/value-A detail SLR which highlights the need of shifting the information seeking behaviour from libraries to social media in regard to the new environment for international students.

No abstract

Association rule mining was first introduced to examine patterns among frequent items. The original motivation for seeking these rules arose from need to examine customer purchasing behaviour in supermarket transaction data. It seeks to identify combinations of items or itemsets, whose presence in a transaction affects the likelihood of the presence of another specific item or itemsets. In recent years, there has been an increasing demand for rare association rule mining. Detecting rare patterns in data is a vital task, with numerous high-impact applications including medical, finance, and security. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for rare pattern mining. We investigate the problems in finding rare rules using traditional association rule mining. As rare association rule mining has not been well explored, there is still specific groundwork that needs to be established. We will discuss some of the major issues in rare association rule mining and also look at current algorithms. As a contribution, we give a general framework for categorizing algorithms: Apriori and Tree based. We highlight the differences between these methods. Finally, we present several real-world application using rare pattern mining in diverse domains. We conclude our survey with a discussion on open and practical challenges in the field.