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

Edwards, Peter and Markovic, Milan and Petrunova, Nikol and Lin, Chenghua and Corsar, David (2018). Tweets used to explore causes of self-reported foodborne illnesses on social media 2017. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-853375

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 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.

Data creators:
Creator NameEmailAffiliationORCID (as URL)
Edwards, Peterp.edwards@abdn.ac.ukUniversity of AberdeenUnspecified
Markovic, Milanmilan.markovic@abdn.ac.ukUniversity of AberdeenUnspecified
Petrunova, NikolUnspecifiedUniversity of AberdeenUnspecified
Lin, Chenghuachenghua.lin@abdn.ac.ukUniversity of AberdeenUnspecified
Corsar, DavidUnspecifiedUniversity of AberdeenUnspecified
Sponsors: Economic and Social Research Council
Grant reference: ES/P011004/1
Topic classification: Health
Keywords: policy making, social media, food, twitter
Project title: The Food Sentiment Observatory: Exploiting New Forms of Data to Help Inform Policy on Food Safety and Food Crime Risks
Grant holders: Peter Edwards, Bryan Campbell, Jacqui Mcelhiney, Chenghua Lin, Susan Pryde, Tigan Daspan, Ross Clark, Robin White
Project dates:
FromTo
14 February 201731 July 2018
Date published: 16 Nov 2018 12:18
Last modified: 16 Nov 2018 12:18

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