An Approach for Time-aware Domain-based Analysis of Users Trustworthiness in Big Social Data

In Online Social Networks (OSNs) there is a need for better understanding of social trust in order to improve the analysis process and mining credibility of social media data. Given the open environment and fewer restrictions associated with OSNs, the medium allows legitimate users as well as spammers to publish their content. Hence, it is essential to measure users’ credibility in various domains and accordingly define influential users in a particular domain(s). Most of the existing approaches of trustworthiness evaluation of users in OSNs are generic-based approaches. There is a lack of domain-based trustworthiness evaluation mechanisms. In OSNs, discovering users’ influence in a specific domain has been motivated by its significance in a broad range of applications such as personalized recommendation systems and expertise retrieval. The aim of this paper is to present an approach to analysing domain-based user’s trustworthiness in OSNs. We provide a novel distinguishing measurement for users in a set of knowledge domains. Domains are extracted from the user’s content using semantic analysis. In order to obtain the level of trustworthiness, a metric incorporating a number of attributes extracted from content analysis and user analysis is consolidated and formulated by considering temporal factor. We show the accuracy of the proposed algorithm by providing a fine-grained trustworthiness analysis of users and their domains of interest in the OSNs using big data Infrastructure

A distributed environment for effective Internet search using intelligent personal agent and distributed knowledge base

With the rapid increase in the available information over the Internet, it is important to develop an efficient search and retrieval mechanism. In particular, the use of distributed search will be beneficial to many users. This paper presents two proposals to implement such an approach. The first one is based on an intelligent personal agent to assist in finding pages relevant to the behavior and characteristics of the user. Secondly, a distributed knowledge base situated across the network will lighten the query-processing loads. Distributed knowledge bases on separate issues located at different servers can be treated as integrated domain knowledge. The integrated knowledge is then used in an inference process at the main server. In this paper, the technical issues on the above model are addressed

Ontology based Intelligent System for Online Employer Demand Analysis

Identifying employer demand is crucial for a nation to ensure it develops accurate and reliable education, workforce development and immigration policies. Skills shortages globally pose a real and urgent need for proper investigation and workforce development planning into the future. Analysing workforce development and employer demand needs through online job market allows much deeper and wider research into skill shortages. Current methods do not provide the level of depth required to address such important economic implications. In this chapter, the authors present an intelligent system aiming to gather and analyse current employer demand information from online job advertisements. An Employer Demand Ontology has been developed and to further the ontology functionality, the Employer Demand Identification Tool has been developed as a semi-automated means to gather and analyse current employer demand information on a regular basis

Big Data Challenges for the Internet of Things (IoT) Paradigm

Millions of devices equipped with sensors are connected together to communicate with each other in order to collect and exchange data. The phenomenon of daily life objects that are interconnected through a worldwide network is known as the Internet of Things (IoT) or Internet of Objects. These sensors from a large number of devices or objects simultaneously and continuingly generate a huge amount of data, often referred to as Big Data. Handling this vast volume, and different varieties, of data imposes significant challenges when time, resources, and processing capabilities are constrained. Hence, Big Data analytics become even more challenging for data collected via the IoT. In this chapter, we discuss the challenges pertaining to Big Data in IoT; these challenges are associated with data management, data processing, unstructured data analytics, data visualization, interoperability, data semantics, scalability, data fusion, data integration, data quality, and data discovery. We present these challenges along with relevant solutions

Tree-based classification to users’ trustworthiness in OSNs

© 2018 Association for Computing Machinery. In the light of the information revolution, and the propagation of big social data, the dissemination of misleading information is certainly difficult to control. This is due to the rapid and intensive flow of information through unconfirmed sources under the propaganda and tendentious rumors. This causes confusion, loss of trust between individuals and groups and even between governments and their citizens. This necessitates a consolidation of efforts to stop penetrating of false information through developing theoretical and practical methodologies aim to measure the credibility of users of these virtual platforms. This paper presents an approach to domain-based prediction to user’s trustworthiness of Online Social Networks (OSNs). Through incorporating three machine learning algorithms, the experimental results verify the applicability of the proposed approach to classify and predict domain-based trustworthy users of OSNs