4,230 research outputs found
Structuring visual exploratory analysis of skill demand
The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on
A framework to maximise the communicative power of knowledge visualisations
Knowledge visualisation, in the field of information systems, is both a process and a product, informed by the closely aligned fields of information visualisation and knowledg management. Knowledge visualisation has untapped potential within the purview of knowledge communication. Even so, knowledge visualisations are infrequently deployed due to a lack of evidence-based guidance. To improve this situation, we carried out a systematic literature review to derive a number of “lenses” that can be used to reveal the essential perspectives to feed into the visualisation production process.We propose a conceptual framework which incorporates these lenses to guide producers of knowledge visualisations. This framework uses the different lenses to reveal critical perspectives that need to be considered during the design process. We conclude by demonstrating how this framework could be used to produce an effective knowledge visualisation
News devices : how digital objects participate in news work and research
News work is increasingly taking place in and through a variety of intersecting digital devices, from websites, to search engines, online platforms, apps, bots, web analytics, data analysis and visualisation tools. These devices are also increasingly used as resources in digital research, and their implications are yet to be fully understood. This thesis examines how digital objects participate in news work and research. To this end, I propose an orientation towards the news device as a research topic and approach. The news device approach calls attention to the ways in which practices and relations are co-produced with digital objects involved in news work. It also attends to how such digital devices may afford modes of studying these practices. To make the case for this approach, I examine the participation of three types of devices in three aspects of news work: (1) the role of the network graph in journalistic storytelling, (2) the role of the online platform in journalism coding, and (3) the role of the web tracker in news audience commodification. In all, the thesis contributes to understanding the digital transformations of news in two ways. First, it develops a rich, nuanced, multidisciplinary, collaborative and reflexive approach to news research with digital methods. Secondly, it provides novel insights into how digital devices shape both news processes and relations with the online advertising and marketing industries, commercial online platforms, digital visual culture, and other digital content producers
Commonshare:a new approach to social reputation for online collaborative communities
Reputation systems are a popular feature of web-based platforms for ensuring that their users abide by platform rules and regulations and are incentivized to demonstrate honest, trustworthy conduct. Accrual of “reputation” in these platforms, most prominently those in the e-commerce domain, is motivated by self-interested goals such as acquiring an advantage over competing platform users. Therefore, in community-oriented platforms, where the goals are to foster collaboration and cooperation among community members, such reputation systems are inappropriate and indeed contrary to the intended ethos of the community and actions of its members. In this article, we argue for a new form of reputation system that encourages cooperation rather than competition, derived from conceptualizing platform communities as a networked assemblage of users and their created content. In doing so, we use techniques from social network analysis to conceive a form of reputation that represents members’ community involvement over a period of time rather than a sum of direct ratings from other members. We describe the design and implementation of our reputation system prototype called “commonshare” and preliminary results of its use within a Digital Social Innovation platform. Further, we discuss its potential to generate insight into other networked communities for their administrators and encourage cooperation between their users
A Novel and Domain-Specific Document Clustering and Topic Aggregation Toolset for a News Organisation
Large collections of documents are becoming increasingly common in the news gathering industry. A review of the literature shows there is a growing interest in datadriven journalism and specifically that the journalism profession needs better tools to understand and develop actionable knowledge from large document sets. On a daily basis, journalists are tasked with searching a diverse range of document sets including news gathering services, emails, freedom of information requests, court records, government reports, press releases and many other types of generally unstructured documents. Document clustering techniques can help address problems of understanding the ever expanding quantities of documents available to journalists by finding patterns within documents. These patterns can be used to develop useful and actionable knowledge which can contribute to journalism. News articles in particular are fertile ground for document clustering principles. Term weighting schemes assign importance to terms within a document and are central to the study of document clustering methods. This study contributes a review of the dominant and most commonly used term frequency weighting functions put forward in research, establishes the merits and limitations of each approach, and proposes modifications to develop a news-centric document clustering and topic aggregation approach. Experimentation was conducted on a large unstructured collection of newspaper articles from the Irish Times to establish if the newly proposed news-centric term weighting and document similarity approach improves document clustering accuracy and topic aggregation capabilities for news articles when compared to the traditional term weighting approach. Whilst the experimentation shows that that the developed approach is promising when compared to the manual document clustering effort undertaken by the three journalist expert users, it also highlights the challenges of natural language processing and document clustering methods in general. The results may suggest that a blended approach of complimenting automated methods with human-level supervision and guidance may yield the best results
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