Time-Aware Datasets are Adaptive Knowledgebases for the New Normal

Recent advances in text classification and knowledge capture in language models have relied on availability of large-scale text datasets. However, language models are trained on static snapshots of knowledge and are limited when that knowledge evolves. This is especially critical for misinformation detection, where new types of misinformation continuously appear, replacing old campaigns. We propose time-aware misinformation datasets to capture time-critical phenomena. In this paper, we first present evidence of evolving misinformation and show that incorporating even simple time-awareness significantly improves classifier accuracy. Second, we present COVID-TAD, a large-scale COVID-19 misinformation da-taset spanning 25 months. It is the first large-scale misinformation dataset that contains multiple snapshots of a datastream and is orders of magnitude bigger than related misinformation datasets. We describe the collection and labeling pro-cess, as well as preliminary experiments

Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability.

We discuss the validation of machine learning models, which is standard practice in determining model efficacy and generalizability. We argue that internal validation approaches, such as cross-validation and bootstrap, cannot guarantee the quality of a machine learning model due to potentially biased training data and the complexity of the validation procedure itself. For better evaluating the generalization ability of a learned model, we suggest leveraging on external data sources from elsewhere as validation datasets, namely external validation. Due to the lack of research attractions on external validation, especially a well-structured and comprehensive study, we discuss the necessity for external validation and propose two extensions of the external validation approach that may help reveal the true domain-relevant model from a candidate set. Moreover, we also suggest a procedure to check whether a set of validation datasets is valid and introduce statistical reference points for detecting external data problems

Big Data in Organizations and the Role of Human Resource Management

Big data are changing the way we work. This book conveys a theoretical understanding of big data and the related interactions on a socio-technological level as well as on the organizational level. Big data challenge the human resource department to take a new role. An organization’s new competitive advantage is its employees augmented by big data

Project-based Serious Leisure in Adventure Sports: Diggers not duffers – a case study of cavers aged 65 and over

There are currently in excess of ten million people aged 65+ in the United Kingdom, with the number predicted to almost double to 19 million by 2050 (Cracknell, 2010). This equates to an increase of one-in-four of the UK population aged 65+ compared to one-in-six currently (Rutherford, 2012). There has been much concern about the consequences of an ageing population on health care systems and an emphasis on health and wellbeing to help older adults age well. Research suggests that leisure activities play an important role for older adults and successful ageing (Menec, 2003; Nimrod, 2007; Payne, Mowen & Montoro-Rodriguez, 2006). Boyes (2013) highlighted the multi-dimensional benefits of outdoor adventure activities and successful ageing, in particular, the physical, social and psychological gains that can be afforded by such activities. The aim of this investigation was to identify how caving is perceived by a small group of older adult males, an often marginalised and hard to reach group. The exploratory nature of the work was to determine the value of any deeper research in this direction and its potential worth to both theory and practice. The project used a small convenience and purposive sample (Mason, 2002; Patton, 2002). Questionnaires and semi-structured interviews were conducted with adult male cavers (n=4), aged between 65 – 74, from the North of England. Themes were identified through manual handling data analysis with internal and external checking throughout. Five key themes emerged: Adventure; risk; identity; serious leisure and managing health related adversity and in order to afford the work critical value, only the latter two were selected for inclusion in this work. Whilst this study indicates the need for further research, it also highlights the benefits of caving in active aging, supporting Boyes’ (2013) notion that adventure sports are mentally and physically challenging, enable social interaction and engagement with the natural environment