2,926 research outputs found
COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts
© 2020 The Authors. Published by MIT Press. This is an open access article available under a Creative Commons licence.
The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1162/qss_a_00066The COVID-19 pandemic requires a fast response from researchers to help address biological,
medical and public health issues to minimize its impact. In this rapidly evolving context,
scholars, professionals and the public may need to quickly identify important new studies. In
response, this paper assesses the coverage of scholarly databases and impact indicators
during 21 March to 18 April 2020. The rapidly increasing volume of research, is particularly
accessible through Dimensions, and less through Scopus, the Web of Science, and PubMed.
Google Scholar’s results included many false matches. A few COVID-19 papers from the
21,395 in Dimensions were already highly cited, with substantial news and social media
attention. For this topic, in contrast to previous studies, there seems to be a high degree of
convergence between articles shared in the social web and citation counts, at least in the
short term. In particular, articles that are extensively tweeted on the day first indexed are
likely to be highly read and relatively highly cited three weeks later. Researchers needing wide
scope literature searches (rather than health focused PubMed or medRxiv searches) should
start with Dimensions (or Google Scholar) and can use tweet and Mendeley reader counts as
indicators of likely importance
Capturing stance dynamics in social media: open challenges and research directions
Social media platforms provide a goldmine for mining public opinion on issues
of wide societal interest and impact. Opinion mining is a problem that can be
operationalised by capturing and aggregating the stance of individual social
media posts as supporting, opposing or being neutral towards the issue at hand.
While most prior work in stance detection has investigated datasets that cover
short periods of time, interest in investigating longitudinal datasets has
recently increased. Evolving dynamics in linguistic and behavioural patterns
observed in new data require adapting stance detection systems to deal with the
changes. In this survey paper, we investigate the intersection between
computational linguistics and the temporal evolution of human communication in
digital media. We perform a critical review of emerging research considering
dynamics, exploring different semantic and pragmatic factors that impact
linguistic data in general, and stance in particular. We further discuss
current directions in capturing stance dynamics in social media. We discuss the
challenges encountered when dealing with stance dynamics, identify open
challenges and discuss future directions in three key dimensions: utterance,
context and influence
VetCompass Australia: A National Big Data Collection System for Veterinary Science
VetCompass Australia is veterinary medical records-based research coordinated with the global VetCompass endeavor to maximize its quality and effectiveness for Australian companion animals (cats, dogs, and horses). Bringing together all seven Australian veterinary schools, it is the first nationwide surveillance system collating clinical records on companion-animal diseases and treatments. VetCompass data service collects and aggregates real-time, clinical records for researchers to interrogate, delivering sustainable and cost-effective access to data from hundreds of veterinary practitioners nationwide. Analysis of these clinical records will reveal geographical and temporal trends in the prevalence of inherited and acquired diseases, identify frequently prescribed treatments, revolutionize clinical auditing, help the veterinary profession to rank research priorities, and assure evidence-based companion-animal curricula in veterinary schools. VetCompass Australia will progress in three phases: (1) roll-out of the VetCompass platform to harvest Australian veterinary clinical record data; (2) development and enrichment of the coding (data-presentation) platform; and (3) creation of a world-first, real-time surveillance interface with natural language processing (NLP) technology. The first of these three phases is described in the current article. Advances in the collection and sharing of records from numerous practices will enable veterinary professionals to deliver a vastly improved level of care for companion animals that will improve their quality of life
Big data methods, social media, and the psychology of entrepreneurial regions: capturing cross-county personality traits and their impact on entrepreneurship in the USA
There is increasing interest in the potential of artificial intelligence and Big Data (e.g., generated via social media) to help understand economic outcomes. But can artificial intelligence models based on publicly available Big Data identify geographical differences in entrepreneurial personality or culture? We use a machine learning model based on 1.5 billion tweets by 5.25 million users to estimate the Big Five personality traits and an entrepreneurial personality profile for 1,772 U.S. counties. The Twitter-based personality estimates show substantial relationships to county-level entrepreneurship activity, accounting for 20% (entrepreneurial personality profile) and 32% (Big Five traits) of the variance in local entrepreneurship, even when controlling for other factors that affect entrepreneurship. Whereas more research is clearly needed, our findings have initial implications for research and practice concerned with entrepreneurial regions and eco-systems, and regional economic outcomes interacting with local culture. The results suggest, for example, that social media datasets and artificial intelligence methods have the potential to deliver comparable information on the personality and culture of regions than studies based on millions of questionnaire-based personality tests
Explaining consequences of employment insecurity: The dynamics of scarring in the United Kingdom, Poland and Norway
This deliverable presents three country studies on scarring effects of early employment insecurity in the United Kingdom, Poland and Norway. Traditional analysis of scarring effects has favoured the analysis of the impact of the experience of unemployment on the experience of subsequent unemployment (state dependence) and the monetary costs of previous unemployment in terms of lower subsequent wages (see e.g. Arulampalam, Booth and Taylor 2000; Arulampalam, Gregg and Gregory 2001). The three present country studies go beyond the traditional analysis of scarring effects in order to better understand the trade-offs experienced by young female and male workers when faced with an insecure labour market integration. With national longitudinal data, original methodological designs and research focus, each study contributes in an original way to the research literature. All three studies pay special attention to gender and education as potential moderating variables of scarring effects
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