A collection of titles and publishers of articles used to explore historical reports of an E.coli incident 2016-2018

Edwards, Peter and Markovic, Milan and Petrunova, Nikol and Chenghua, Lin and Corsar, David (2018). A collection of titles and publishers of articles used to explore historical reports of an E.coli incident 2016-2018. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-853376

Social media and other forms of online content have enormous potential as a way to understand people's opinions and attitudes, and as a means to observe emerging phenomena - such as disease outbreaks. How might policy makers use such new forms of data to better assess existing policies and help formulate new ones? This one year demonstrator project is a partnership between computer science academics at the University of Aberdeen and officers from Food Standards Scotland which aims to answer this question. Food Standards Scotland is the public-sector food body for Scotland created by the Food (Scotland) Act 2015. It regularly provides policy guidance to ministers in areas such as food hygiene monitoring and reporting, food-related health risks, and food fraud. The project will develop a software tool (the Food Sentiment Observatory) that will be used to explore the role of data from sources such as Twitter, Facebook, and TripAdvisor in three policy areas selected by Food Standards Scotland: - attitudes to the differing food hygiene information systems used in Scotland and the other UK nations; - study of an historical E.coli outbreak to understand effectiveness of monitoring and decision making protocols; - understanding the potential role of social media data in responding to new and emerging forms of food fraud. The Observatory will integrate a number of existing software tools (developed in our recent research) to allow us to mine large volumes of data to identify important textual signals, extract opinions held by individuals or groups, and crucially, to document these data processing operations - to aid transparency of policy decision-making. Given the amount of noise appearing in user-generated online content (such as fake restaurant reviews) it is our intention to investigate methods to extract meaningful and reliable knowledge, to better support policy making.

Data description (abstract)

Data collected from Lexis Nexis database including the result of a search for UK news articles mentioning phrase "errington cheese" for the period between July 2016 and Jan 2018.

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