Tell Me How You Feel: Designing Emotion-Aware Voicebots to Ease Pandemic Anxiety In Aging Citizens

The feeling of anxiety and loneliness among aging population has been recently amplified by the COVID-19 related lockdowns. Emotion-aware multimodal bot application combining voice and visual interface was developed to address the problem in the group of older citizens. The application is novel as it combines three main modules: information, emotion selection and psychological intervention, with the aim of improving human well-being. The preliminary study with target group confirmed that multimodality improves usability and that the information module is essential for participating in a psychological intervention. The solution is universal and can also be applied to areas not directly related to COVID-19 pandemic.Comment: 16 page

Confirmation Bias: Roles of Search Engines and Search Contexts

Prior work shows that confirmation bias, defined as the tendency to seek confirming evidence, is prevalent on the Web as well. While this has been attributed to individuals\u27 psychological needs or cognitive limitations, the roles of search engines and search contexts have largely been neglected. The goals of this study are to examine how search contexts may change the composition of search results, and how – if at all – search engines may contribute to confirmation bias. Results of two studies show that search engines may exacerbate confirmation bias by generating results that consist only of confirming evidence for search contexts where disconfirming evidence is identified using different terms or phrases. This induces individuals to make biased decisions. Findings of this study deepen our understanding of the ways in which confirmation bias unfolds on the Web when individuals use search engines

Competition-based rating system for medical website credibility

In this paper, we propose a new approach to the aggregation of monadic ratings (5-step scale) done by crowdsourcing users for the evaluation of medical websites. We compare them pairwise with other evaluations done by the same users for other websites (whether they are higher or lower), and we will use an Elo rating algorithm to calculate website “credibility” values. Results show that this method of crowdsourcing evaluation is highly correlated with expert evaluations. As proposed, a competition-based model uses a 5-step scale as ordinal and only compares which website is rated higher or lower by the same user. This approach can solve many problems associated with a 5-point scale, such as different understanding by users, user bias, and distribution skewness that can be clearly observed in results

Technologies for extracting and analysing the credibility of health-related online content

The evolution of the Web has led to an improvement in information accessibility. This change has allowed access to more varied content at greater speed, but we must also be aware of the dangers involved. The results offered may be unreliable, inadequate, or of poor quality, leading to misinformation. This can have a greater or lesser impact depending on the domain, but is particularly sensitive when it comes to health-related content. In this thesis, we focus in the development of methods to automatically assess credibility. We also studied the reliability of the new Large Language Models (LLMs) to answer health questions. Finally, we also present a set of tools that might help in the massive analysis of web textual content

Informative web content guidelines: A practitioner model for online content effectiveness

With the rise of the world wide web, many organisations publish large knowledge bases as online informative content, enabling access for their current and potential stakeholders, customers, and service users. Providing universal access to information is a key feature of many national laws, ensuring that content is accessible for the intended audience, however there is little focus on its informativeness. Whilst there are many prior academic and industry frameworks for assessing the success of information systems, many of these focus on facets of the system itself or task completion, rather than the quality of the content. Evolutions of the WCAG (Web Content Accessibility Guidelines) have guided practitioners towards accessibility, neglecting the other attributes of information quality.This interpretive study identifies the key attributes that have the greatest impact on information quality, using four action case studies to examine the attributes and identify areas for content improvement. Each action case study employs observations using task scenarios and the concurrent think aloud protocol to elicit user perceptions and cognitive understanding of information within websites and their inherent attributes of quality. The insights discovered from users feed the development of a model for practitioners to refine their content based on a synthesis between existing generalised literature and focused studies within the online space.The Informative Web Content Guidelines (IWCG) is proposed as a new practitioner model for developing and assessing web content by promoting information quality. The guidelines parallel existing industry standards mandated by many national governments to improve online accessibility. Based on results from the case studies, this model combines key attributes from prior literature with three new attributes identified through the case studies: those of fallback, information usability, and interactivity. By combining existing academic information quality frameworks with focused data from the case studies, a specialised selection of attributes for online information quality is proposed.This thesis narrates the study, including the identification of potential information quality attributes from prior literature, the development of a practitioner-focused model based on WCAG principles and validation through a final action case study