Scalable Knowledge Extraction and Visualization for Web Intelligence

Understanding stakeholder perceptions and assessing the impact of campaigns are key questions of communication experts. Web intelligence platforms help to answer such questions, provided that they are scalable enough to analyze and visualize information flows from volatile online sources in real time. This paper presents a distributed architecture for aggregating Web content repositories from Web sites and social media streams, memory-efficient methods to extract factual and affective knowledge, and interactive visualization techniques to explore the extracted knowledge. The presented examples stem from the Media Watch on Climate Change, a public Web portal that aggregates environmental content from a range of online sources

Visualising the Propagation of News on the Web

Abstract When newsworthy events occur, information quickly spreads across the Web, along official news outlets as well as across social media platforms. Information diffusion models can help to uncover the path of an emerging news story across these channels, and thereby shed light on how these channels interact. The presented work enables journalists and other stakeholders to trace back the distribution process of news stories, and to identify their origin as well as central information hubs who have amplified their dissemination

A prescriptive knowledge visualization model for student performance based on Internet Use Behavior (IUB)

The visualization and interpretation of the Internet usage data is one of the key elements to determine students’ Internet use behavior (IUB). The term IUB refers to online activities by the students. However, these processes have significant shortcomings in terms of cognitive and reasoning in prescriptive analytics for determining the impact of IUB on student performance. Therefore, this study aims to propose a Prescriptive Knowledge Visualization (PKV) model by using cognitive and reasoning components. This study employed a design science research methodology comprising four phases; problem definition, suggestion, development and evaluation. Initially, a content analysis was conducted to investigate the components of Knowledge Visualization in an educational context. The proposed model was constructed based on the content analysis results of three main components; Knowledge Visualization, prescriptive analytics and Internet use behavior (IUB). The underlying theory of PKV model was based on Prescriptive Knowledge Visualization Theory, Cognitive Load Theory and Decision-Making Theory. The model was then verified and accepted by domain experts. Subsequently, seven PKV model validation dimensions were put forward; complexity, visibility, flexibility, clarity, manageability, practicality and effectiveness. The model validation was conducted in three phases; expert review, usability testing and knowledge base evaluation. The findings indicate that the PKV model's evaluation results score the highest mean of 9.67 (out of 10) of all dimensions and indicate that the proposed PKV model is producing a knowledge base that can be inserted into the system and relevant in the education sector, particularly in supporting the decision process related to the improvement of student performance. Theoretically, this study contributes in enhancing the visualization and interpretation of data by using cognitive and reasoning components in prescriptive analytics. Besides, the proposed model enables decision makers in the educational domain to make meaningful decisions regarding Internet usage management