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

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.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.</p

Tweets used to explore the potential role of social media data in responding to new and emerging forms of food fraud 2018

Data collected from Twitter social media platform (6 May 2018 - 16 May 2018) to explore the potential role of social media data in responding to new and emerging forms of food fraud reported on social media from posts originating in the UK. The dataset contains Tweet IDs and keywords used to search for Tweets using a programatic access via the public Twitter API. Keywords used in this search were generated using a machine learning tool and consisted of a combinations of keywords describing terms related to food and outrage.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.</p

Tweets used to explore general food hygiene discourse and attitudes to the differing food hygiene information systems used in the UK 2017

Data collected from Twitter social media platform (5 Aug 2017 - 28 Aug 2017) to explore general food hygiene discourse and attitudes to the differing food hygiene information systems used in Scotland and the other UK nations such as England and Wales (i.e. Food hygiene information scheme vs Food hygiene rating scheme).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.</p

Tweets used to explore causes of self-reported foodborne illnesses on social media 2017

Data collected from Twitter social media platform (10 Nov 2017 - 18 Dec 2017) to explore causes of self-reported foodborne illnesses on social media from posts originating in Scotland, UK. The dataset contains Tweet IDs and keywords used to search for Tweets using a programatic access via the public Twitter API. In addition, this archive also includes keywords that were used to cluster retrieved Tweets into smaller groups of messages containing mentions of specific keywords. This includes lists of keywords describing ingredients, foods and drinks, cooking techniques, and domestic implements. Additional keywords relating to food and places associated with food (e.g. restaurants) were generated using an automated machine learning tool based on a set of seed keywords. Finally, the last set of keywords used to cluster retrieved Tweets includes a list of names of food businesses located in Glasgow, UK. 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.</p

Tweets used to study reports of food fraud related to fish products 2018

Data collected from Twitter social media platform (8 June 2018 - 22 June 2018) to study reports of food fraud related to fish products on social media from posts originating in the UK. The dataset contains Tweet IDs and keywords used to search for Tweets using a programatic access via the public Twitter API. Keywords used in this search were generated using a machine learning tool and consisted of combinations of keywords describing terms related to fish and fake.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.</p