Are Health Videos from Hospitals, Health Organizations, and Active Users Available to Health Consumers? An Analysis of Diabetes Health Video Ranking in YouTube

Health consumers are increasingly using the Internet to search for health information. The existence of overloaded, inaccurate, obsolete, or simply incorrect health information available on the Internet is a serious obstacle for finding relevant and good-quality data that actually helps patients. Search engines of multimedia Internet platforms are thought to help users to find relevant information according to their search. But, is the information recovered by those search engines from quality sources? Is the health information uploaded from reliable sources, such as hospitals and health organizations, easily available to patients? The availability of videos is directly related to the ranking position in YouTube search. The higher the ranking of the information is, the more accessible it is. The aim of this study is to analyze the ranking evolution of diabetes health videos on YouTube in order to discover how videos from reliable channels, such as hospitals and health organizations, are evolving in the ranking. The analysis was done by tracking the ranking of 2372 videos on a daily basis during a 30-day period using 20 diabetes-related queries. Our conclusions are that the current YouTube algorithm favors the presence of reliable videos in upper rank positions in diabetes-related searches.Fernández Llatas, C.; Traver Salcedo, V.; Borrás Morell, JE.; Martinez-Millana, A.; Karlsen, R. (2017). Are Health Videos from Hospitals, Health Organizations, and Active Users Available to Health Consumers? An Analysis of Diabetes Health Video Ranking in YouTube. Computational and Mathematical Methods in Medicine. 2017:1-9. doi:10.1155/2017/8194940S19201

Digital storytelling: engaging young people to communicate for digital media literacy

Digital stories are powerful forces in the lives of young people as they shape opinions, assumptions, and biases about the knowledge of everyday lives. This paper presents the findings of an exploratory project that saw secondary school students participating in a digital storytelling project. Underpinning this project was an interest in cultivating digital media literacy among young people. Data analysed included a self-assessment questionnaire, focus group discussions with young people and the production of short 1-3 minute digital stories on various issues related to online cultures. The first part of the article looks at the digital competences of young people. The findings of a self-assessment revealed that the respondents felt generally capable when working with information, and moderately capable of communication and safety but had difficulty with content creation and problem-solving skills.The findings of the second part of the study revealed that young people get much enjoyment and feel smart and knowledgeable as they scroll quickly through an online search on information, images, news, and stories. They are content consumers and content creators who enjoy dramatic engagements and can produce stories as communication texts. However, it was also found that the students confronted difficulties in evaluating the relevance and usefulness of information as well as in expressing their ideas through different modes of visual communication. By way of conclusion, this paper calls for the creation of a state-based advisory committee composed of educators, researchers and media practitioners who will work towards building digital media literacy

Using linked data for integrating educational medical web databases based on bioMedical ontologies

Open data are playing a vital role in different communities, including governments, businesses, and education. This revolution has had a high impact on the education field. Recently, Linked Data are being adopted for publishing and connecting data on the web by exposing and connecting data which were not previously linked. In the context of education, applying Linked Data to the growing amount of open data used for learning is potentially highly beneficial. This paper proposes a system that tackles the challenges of data acquisition and integration from distributed web data sources into one linked dataset. The application domain of this work is medical education, and the focus is on integrating educational content in the form of articles published in online educational libraries and Web 2.0 content that can be used for education. The process of integrating a collection of heterogeneous resources is to create links that connect the resources collected from distributed web data sources based on their semantics. The proposed system harvests metadata from distributed web sources and enriches it with concepts from biomedical ontologies, such as SNOMED CT, that enable its linking. The final result of building this system is a linked dataset of more than 10,000 resources collected from PubMed Library, YouTube channels, and Blogging platforms. The final linked dataset is evaluated by developing information retrieval methods that exploit the SNOMED CT hierarchical relations for accessing and querying the dataset. Ontology-based browsing method has been developed for exploring the dataset, and the browsing results have been clustered to evaluate its linkages. Furthermore, ontology-based query searching method has been developed and tested to enhance the discoverability of the data. The results were promising and had shown that using SNOMED CT for integrating distributed resources on the web is beneficial

The influence of personal knowledge management on individual health care decision-making : an action learning approach : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management at Massey University, Albany, Auckland, New Zealand

Appendix B and Appendix K were removed at the author's request.Background: Making effective health care decisions is important. Despite the large volumes of information available, individuals often face personal limitations evaluating this information and making optimal decisions. Personal knowledge management has been suggested as a method of addressing information barriers and improving decision-making. Personal knowledge management has, however, been mostly applied within an education context, in order to improve individuals’ learning performance. From the available literature in this area, very limited research or significant conceptual development has been undertaken on personal knowledge management and its influence on decision-making, particularly in the health care context. Aims and Significance: This study examines an effective personal knowledge management strategy for older adults (aged between 46 and 75) with limited computer/technological skills by answering the following questions: How do older adults access and evaluate information and knowledge for health care decision-making? How can personal knowledge management help older adults with limited computer/technological abilities manage their information and knowledge for health care decision-making? How effective is an action learning training program in supporting older adults with limited computer/technological abilities for health care decision-making? The aim of this study is to provide an understanding of the use of action learning and personal knowledge management pertaining to older adults’ health care decision-making. Examples of relevant health care concerns include, diabetes and obesity or other issues of this nature, but are exclusive of severe health issues, such as cancer. The findings will offer educators and researchers an understanding of ways to help these individuals to navigate the world of information regarding critical personal decision-making, with specific reference to health care. Method: To investigate this issue, a qualitative study was conducted using action learning with thematic and grounded theory coding techniques. New Zealand patient health care support groups and churches provided a source of older adults with health-related issues as volunteers. Participants were asked to practice personal knowledge management strategies, focusing on their personal health-related issues after each learning session. In the following session, the issues or experiences that the participants encountered whilst conducting their self-practice exercises, within their groups were discussed. Findings: This study found that the older adult participants in this study used Google, Facebook closed groups, YouTube, online videos, health care support groups, family and medical professionals as information sources before embarking upon this training program. To evaluate alternative treatment options, these participants rely predominantly on family, friends, medical professionals and their personal life experience for decisions. This study found that major factors that negatively impacted older adults’ effective information interpretation and decision-making include: barriers to accessing accurate and relevant health care information and knowledge, barriers to computer-based technology use, and humanistic barriers. The findings suggest that a four-stage personal knowledge management strategy could help older adults (with limited computer/technological skills) to overcome the barriers to effective information interpretation, and making informed health care decisions. Finally, this study suggests some practical training/learning techniques for older adults. For instance, major individual health-related issues of the older adults within the pre-training program need to be confirmed, followed by a warm welcome prior to the commencement of the training program. I learned that it is important to pre-diagnose participants’ abilities in learning and computer-based technology before designing the training program. This can help to develop an appropriate training program for a specific cohort. Conclusions: The findings of this study contribute to the development of an academic understanding of personal knowledge management conceptualisation in the consumer decision-making field, with the aim of improving older adults’ information and knowledge management processes. This study serves as a vantage point for further empirical research in personal knowledge management and older adult education and training

Social media mining under the COVID-19 context: Progress, challenges, and opportunities

Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and repro�ducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies

A treatise on Web 2.0 with a case study from the financial markets

There has been much hype in vocational and academic circles surrounding the emergence of web 2.0 or social media; however, relatively little work was dedicated to substantiating the actual concept of web 2.0. Many have dismissed it as not deserving of this new title, since the term web 2.0 assumes a certain interpretation of web history, including enough progress in certain direction to trigger a succession [i.e. web 1.0 → web 2.0]. Others provided arguments in support of this development, and there has been a considerable amount of enthusiasm in the literature. Much research has been busy evaluating current use of web 2.0, and analysis of the user generated content, but an objective and thorough assessment of what web 2.0 really stands for has been to a large extent overlooked. More recently the idea of collective intelligence facilitated via web 2.0, and its potential applications have raised interest with researchers, yet a more unified approach and work in the area of collective intelligence is needed. This thesis identifies and critically evaluates a wider context for the web 2.0 environment, and what caused it to emerge; providing a rich literature review on the topic, a review of existing taxonomies, a quantitative and qualitative evaluation of the concept itself, an investigation of the collective intelligence potential that emerges from application usage. Finally, a framework for harnessing collective intelligence in a more systematic manner is proposed. In addition to the presented results, novel methodologies are also introduced throughout this work. In order to provide interesting insight but also to illustrate analysis, a case study of the recent financial crisis is considered. Some interesting results relating to the crisis are revealed within user generated content data, and relevant issues are discussed where appropriate

Supporting Cancer Knowledge Needs Using Online Information

Information is exploding at an exponential rate. Because there is a flood of medical information on the Internet, it can be difficult to wade through the many resources to determine what information is best to use in practice. The intent of this chapter in Cancer Concepts: A Guidebook for the Non-Oncologist is to help the health care provider find reliable online cancer information. To help inform clinical decision making, health science librarians continue to address this rapidly growing body of literature by analyzing resources and identifying the highest quality information available on the Internet. The concept of Evidence-Based Medicine (EBM) is important to understand, as well as the process needed to find literature supporting EBM. Why EBM? EBM is the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. Making evidence-based clinical decisions is not about intuition, but finding reliable, up-to-date literature and using it in combination with clinical expertise and patient choice. Once a source for free online quality literature is located, a health care provider can consider the best current evidence to thoroughly answer clinical questions.https://escholarship.umassmed.edu/cancer_concepts/1026/thumbnail.jp

"Mango Mango, How to Let The Lettuce Dry Without A Spinner?'': Exploring User Perceptions of Using An LLM-Based Conversational Assistant Toward Cooking Partner

The rapid advancement of the Large Language Model (LLM) has created numerous potentials for integration with conversational assistants (CAs) assisting people in their daily tasks, particularly due to their extensive flexibility. However, users' real-world experiences interacting with these assistants remain unexplored. In this research, we chose cooking, a complex daily task, as a scenario to investigate people's successful and unsatisfactory experiences while receiving assistance from an LLM-based CA, Mango Mango. We discovered that participants value the system's ability to provide extensive information beyond the recipe, offer customized instructions based on context, and assist them in dynamically planning the task. However, they expect the system to be more adaptive to oral conversation and provide more suggestive responses to keep users actively involved. Recognizing that users began treating our LLM-CA as a personal assistant or even a partner rather than just a recipe-reading tool, we propose several design considerations for future development.Comment: Under submission to CHI202

Application of Common Sense Computing for the Development of a Novel Knowledge-Based Opinion Mining Engine

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience