Enhancing Trustworthiness of Qualitative Findings: Using Leximancer for Qualitative Data Analysis Triangulation

This paper offers an approach to enhancing trustworthiness of qualitative findings through data analysis triangulation using Leximancer, a text mining software that uses co-occurrence to conduct semantic and relational analyses of text corpuses to identify concepts, themes, and how they relate to one another. This study explores the usefulness of Leximancer for triangulation by examining 309 pages of previously analyzed interview data that resulted in a conceptual model. Findings show Leximancer to be an ideal tool for refining a priori conceptual models. The Leximancer analysis provided missing nuance from the a priori model, depicting the value of and connection between emergent themes. Dependability was also added to the findings by facilitating a better understanding of how participant quotes represent particular themes

A collective intelligence approach for building student's trustworthiness profile in online learning

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Information and communication technologies have been widely adopted in most of educational institutions to support e-Learning through different learning methodologies such as computer supported collaborative learning, which has become one of the most influencing learning paradigms. In this context, e-Learning stakeholders, are increasingly demanding new requirements, among them, information security is considered as a critical factor involved in on-line collaborative processes. Information security determines the accurate development of learning activities, especially when a group of students carries out on-line assessment, which conducts to grades or certificates, in these cases, IS is an essential issue that has to be considered. To date, even most advances security technological solutions have drawbacks that impede the development of overall security e-Learning frameworks. For this reason, this paper suggests enhancing technological security models with functional approaches, namely, we propose a functional security model based on trustworthiness and collective intelligence. Both of these topics are closely related to on-line collaborative learning and on-line assessment models. Therefore, the main goal of this paper is to discover how security can be enhanced with trustworthiness in an on-line collaborative learning scenario through the study of the collective intelligence processes that occur on on-line assessment activities. To this end, a peer-to-peer public student's profile model, based on trustworthiness is proposed, and the main collective intelligence processes involved in the collaborative on-line assessments activities, are presented.Peer ReviewedPostprint (author's final draft

Mining and visualizing uncertain data objects and named data networking traffics by fuzzy self-organizing map

Uncertainty is widely spread in real-world data. Uncertain data-in computer science-is typically found in the area of sensor networks where the sensors sense the environment with certain error. Mining and visualizing uncertain data is one of the new challenges that face uncertain databases. This paper presents a new intelligent hybrid algorithm that applies fuzzy set theory into the context of the Self-Organizing Map to mine and visualize uncertain objects. The algorithm is tested in some benchmark problems and the uncertain traffics in Named Data Networking (NDN). Experimental results indicate that the proposed algorithm is precise and effective in terms of the applied performance criteria.Peer ReviewedPostprint (published version

DC Proposal: Evaluating trustworthiness of web content using semantic web technologies

Trust plays an important part in people's decision processes for using information. This is especially true on the Web, which has less quality control for publishing information. Untrustworthy data may lead users to make wrong decisions or result in the misunderstanding of concepts. Therefore, it is important for users to have a mechanism for assessing the trustworthiness of the information they consume. Prior research focuses on policy-based and reputation-based trust. It does not take the information itself into account. In this PhD research, we focus on evaluating the trustworthiness of Web content based on available and inferred metadata that can be obtained using Semantic Web technologies. This paper discusses the vision of our PhD work and presents an approach to solve that problem

Conducting a Self-Assessment of a Long-Term Archive for Interdisciplinary Scientific Data as a Trustworthy Digital Repository

4th International Conference on Open RepositoriesThis presentation was part of the session : Conference PresentationsDate: 2009-05-19 03:00 PM – 04:30 PMLong-term preservation and stewardship of scientific data and research-related information is paramount to the future of science and scholarship. Disciplinary and interdisciplinary scientific data archives can offer capabilities for managing and preserving data for research, education, and decision-making activities of future communities representing various scientific and scholarly disciplines. However, meeting the requirements for a trusted digital repository presents challenges to ensure that archived collections will be discoverable, accessible, and usable in the future. Assessing whether scientific data archives meet the requirements for trustworthy repositories will help to ensure that todayâ s collections of scientific data will be available in the future. A continuing self-assessment of a long-term archive for interdisciplinary scientific data is being conducted to identify improvements needed to become a trustworthy repository for managing and providing access to interdisciplinary scientific data by future communities of users. Recommendations are offered for archives of scientific data to meet the requirements of a trustworthy repository.NAS

People on Drugs: Credibility of User Statements in Health Communities

Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information

A Wikipedia Literature Review

This paper was originally designed as a literature review for a doctoral dissertation focusing on Wikipedia. This exposition gives the structure of Wikipedia and the latest trends in Wikipedia research