In recent years, knowledge graphs (KGs) have been widely applied in various domains for different purposes. The semantic model of KGs can represent knowledge through a hierarchical structure based on classes of entities, their properties, and their relationships. The construction of large KGs can enable the integration of heterogeneous information sources and help Artificial Intelligence (AI) systems be more explainable and interpretable. This systematic review examines a selection of recent publications to understand how KGs are currently being used in eXplainable AI systems. To achieve this goal, we design a framework and divide the use of KGs into four categories: extracting features, extracting relationships, constructing KGs, and KG reasoning. We also identify where KGs are mostly used in eXplainable AI systems (pre-model, in-model, and post-model) according to the aforementioned categories. Based on our analysis, KGs have been mainly used in pre-model XAI for feature and relation extraction. They were also utilised for inference and reasoning in post-model XAI. We found several studies that leveraged KGs to explain the XAI models in the healthcare domain.

Algorithm audits have increased in recent years due to a growing need to independently assess the performance of automatically curated services that process, filter and rank the large and dynamic amount of information available on the Internet. Among several methodologies to perform such audits, virtual agents stand out because they offer the ability to perform systematic experiments, simulating human behaviour without the associated costs of recruiting participants. Motivated by the importance of research transparency and replicability of results, this article focuses on the challenges of such an approach. It provides methodological details, recommendations, lessons learned and limitations based on our experience of setting up experiments for eight search engines (including main, news, image and video sections) with hundreds of virtual agents placed in different regions. We demonstrate the successful performance of our research infrastructure across multiple data collections, with diverse experimental designs, and point to different changes and strategies that improve the quality of the method. We conclude that virtual agents are a promising venue for monitoring the performance of algorithms across long periods of time, and we hope that this article can serve as a basis for further research in this area.

Studies of parents' online safety concerns typically centre on information privacy and on worries over unknown third parties preying on children, whereas investigations into youth perspectives on online safety have found young people to focus on threats to safety or reputation by known individuals. The case of youth who are themselves parents raises questions regarding how these differing perspectives are negotiated by individuals who are in dual roles as youth and parents. Using interview and ethnographic observation data from the longitudinal Young Parent Study in British Columbia, Canada, this analysis investigates social media and online safety practices of 113 young parents. Online safety concerns of young parents in this study focused on personal safety, their children's online privacy and image management. These concerns reflect their dual roles, integrating youth image and information management concerns with parental concerns over the safety and information privacy of their own children.

This study examines how the credibility of the content of mis- or disinformation, as well as the believability of authors creating such information is assessed in online discussion. More specifically, the investigation was focused on the credibility of mis- or disinformation about COVID-19 vaccines. To this end, a sample of 1887 messages posted to a Reddit discussion group was scrutinised by means of qualitative content analysis. The findings indicate that in the assessment of the author's credibility, the most important criteria are his or her reputation, expertise and honesty in argumentation. In the judgement of the credibility of the content of mis/disinformation, objectivity of information and plausibility of arguments are highly important. The findings highlight that in the assessment of the credibility of mis/disinformation, the author's qualities such as poor reputation, incompetency and dishonesty are particularly significant because they trigger expectancies about how the information content created by the author is judged.

The purpose of this study is to develop a text clustering-based analysis of COVID-19 research articles. Owing to the proliferation of published COVID-19 research articles, researchers need a method for reducing the number of articles they have to search through to find material relevant to their expertise. The study analyzes 83,264 abstracts from research articles related to COVID-19. The textual data are analysed using singular value decomposition (SVD) and the expectation-maximisation (EM) algorithm. Results suggest that text clustering can both reveal hidden research themes in the published literature related to COVID-19, and reduce the number of articles that researchers need to search through to find material relevant to their field of interest.

COVID-19 has created unprecedented organisational challenges, yet no study has examined the impact on information search. A case study in a knowledge-intensive organisation was undertaken on 2.5 million search queries during the pandemic. A surge of unique users and COVID-19 search queries in March 2020 may equate to 'peak uncertainty and activity', demonstrating the importance of corporate search engines in times of crisis. Search volumes dropped 24% after lockdowns; an 'L-shaped' recovery may be a surrogate for business activity. COVID-19 search queries transitioned from awareness, to impact, strategy, response and ways of working that may influence future search design. Low click through rates imply some information needs were not met and searches on mental health increased. In extreme situations (i.e. a pandemic), companies may need to move faster, monitoring and exploiting their enterprise search logs in real time as these reflect uncertainty and anxiety that may exist in the enterprise.

The unprecedented outbreak of COVID-19 is one of the most serious global threats to public health in this century. During this crisis, specialists in information science could play key roles to support the efforts of scientists in the health and medical community for combatting COVID-19. In this article, we demonstrate that information specialists can support health and medical community by applying text mining technique with latent Dirichlet allocation procedure to perform an overview of a mass of coronavirus literature. This overview presents the generic research themes of the coronavirus diseases: COVID-19, MERS and SARS, reveals the representative literature per main research theme and displays a network visualisation to explore the overlapping, similarity and difference among these themes. The overview can help the health and medical communities to extract useful information and interrelationships from coronavirus-related studies.

The use of online information sources in most professions is widespread, and well researched. Less understood is how the use of these sources vary across the strata within a single profession, and how question context affects search behaviour. Using the dental profession as a case of a highly stratified discipline, we examine search preferences for sources by professional strata among dentists in a practice-based network. Results show that variation exists in information search behaviour across professional strata of dental clinicians. This study highlights the importance of addressing information literacy across different levels of a profession. Findings also underscore that search behaviour and source preference vary with perceived question relevance.