The current worldwide recession has led to a reduction in spending and a tightening of budget at all levels. Measures such as cuts in wages, lower pension pay-outs and rising unemployment seem to go hand-in-hand with politically motivated violence and social instability. In recent times, certain areas of Europe have been met with widespread protests, strikes and riots such as the ones in United Kingdom (UK), Spain, and Greece. Events over the last few years in the UK have demonstrated that people are willing to go to extreme lengths for their voice to be heard. Researchers in this area are still unclear about what leads to social instability and violent protests. How can these events be predicted? What tactics can be deployed by law enforcement agencies to manage these events? Social Networks such as Twitter and Facebook have been proven to be useful tools for demonstrators to organise themselves. Instead of limiting access to these services during any future disorders, filtered information fed from these media can be used by law enforcement agencies not only to prevent using them for criminal behaviour, but also to predict these events and develop tactics to manage future protests. This papers reviews the most cited research in this area and proposes a novel theoretical framework based on digital forensics principles combined with Cloud technology, followed by a sample practical implementation for illustration.

No abstract

Semantic and Cloud Computing technologies have become vital elements for developing and deploying solutions across diverse fields in computing. While they are independent of each other, they can be integrated in diverse ways for developing solutions and this has been significantly explored in recent times. With the migration of web-based data and applications to cloud platforms and the evolution of the web itself from a social, web 2.0 to a semantic, web 3.0 comes the convergence of both technologies. While several concepts and implementations have been provided regarding interactions between the two technologies from existing research, without an explicit classification of the modes of interaction, it can be quite challenging to articulate the interaction modes, hence building upon them can be a very daunting task. Hence, this research identifies and describes the modes of interaction between them. Furthermore, a "cloud-driven" interaction mode which focuses on fully maximising cloud computing characteristics and benefits for driving the semantic web is described; providing an approach for evolving the semantic web and delivering automated semantic annotation on a large-scale to web applications.

No abstract

The detection of fake news on social media has become a very active research area. Several approaches and techniques have been proposed and implemented to address the challenge, across diverse technological domains such as NLP (Natural Language Processing) and machine learning. While substantial progress has been made on these, it remains a daunting task due to complexities in its nature. Therefore, it has become pertinent to significantly explore and integrate other technologies to detect fake news on social media. Hence, this research focuses on further exploring and developing native semantic technology solutions for the discourse space. The initial result is a taxonomy classifying socially contextual features for news articles and then Fandet: an OWL ontology for context-based fake news detection by semantically annotating contextual features of news articles and datasets using the ontology. This provides a basis for patterns recognition, analysis, and identification of news articles on social media as either fake or not.