During an investigation into the existence of Gauss-type quadrature formulae for the numerical solution of Fredholm integral equations with weakly singular kernels an intermediate result was found which is of independent interest.
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ABSTRACTMonitoring rates of alcohol consumption across the UK is a timely problem due to ever-increasing drinking levels [36]. This has led to calls from public services (e.g. police and health services) to assess the e↵ect it is having on people and society. Current research methods that are utilised to assess consumption patterns are costly, time consuming, and do not supply su ciently detailed results. This is because they look at snapshots of individuals' drinking patterns, which rely on generalised usage patterns, and post consumption recall. In this paper we look into the use of social media such as Twitter (a popular micro blogging site) to monitor the rate of alcohol consumption in regions across the UK by introducing the Social Media Alcohol Index (SMAI). By looking at the variation in term usage, and treating the social network as a spatio-temporal self-reporting sense-network, we aim to discover variation in drinking patterns on both local and national levels within the UK. This study used 31.6 million tweets collected over a 6 week period, and used the Health & Social Care Information Centre (HSCIC) weekly alcohol consumption pattern as a ground truth. High correlations between the ground truth and the computed SMAI (Social Media Alcohol Index) were found on a national and local level, along with the ability to detect variation in consumption on National holidays and celebrations at both local and national levels.
Language change and innovation is constant in online and offline communication, and has led to new words entering people's lexicon and even entering modern day dictionaries, with recent additions of 'e-cig' and 'vape'. However the manual work required to identify these 'innovations' is both time consuming and subjective. In this work we demonstrate how such innovations in language can be identified across two different OSN's (Online Social Networks) through the operationalisation of known language acceptance models that incorporate relatively simple statistical tests. From grounding our work in language theory, we identified three statistical tests that can be applied -variation in; frequency, form and meaning. Each show different success rates across the two networks (Geo-bound Twitter sample and a sample of Reddit). These tests were also applied to different community levels within the two networks allowing for different innovations to be identified across different community structures over the two networks, for instance: identifying regional variation across Twitter, and variation across groupings of Subreddits, where identified example innovations included 'casualidad' and 'cym'.
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