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.

The search for relevant data content was performed using a custom built data collection module within the Observatory platform (https://sites.google.com/view/foobs/the-observatory). A public API provided by Twitter was utilised to gather all social media messages (Tweets) matching a specific set of keywords. Three types of searches were performed, each with diverse lists of search keywords. These searches were grouped by the relevant theme, namely general food hygiene discourse, food hygiene information scheme (FHIS), and food hygiene rating scheme (FHRS). Each line in the keywords files contains a search keyword/phrase used to retrieve matching Tweets, which had to include at least one of the search keywords/phrases. Therefore, the search string used by the API was constructed as follows: keyword1 OR keyword2 OR keyword3 OR ... Two datasets were generated for each theme, one containing data from Scotland and the second containing data from the rest of the UK. The Twitter API allows historical searches to be restricted to Tweets associated with a specific location, however, this can be only specified as a specific radius from a given latitude and longitude geo-point. We used Twitter's geo-resticted search by defining a Lat/Long point and radius (in kilometres). In order to cover major areas in the UK we used the following four geo-restrictions: England ( Latitude =53.157312 Longitude=-1.362305 Radius = 177.312 km; Latitude =52.104256 Longitude=-0.516357 Radius = 177.321 km); Wales ( Latitude =50.700517 Longitude=-3.99353 Radius = 50 km); Scotland ( Latitude =56.496467 Longitude=--3.80127 Radius = 177.312 km)