Overview of the 'The Food Sentiment Observatory' pilot project (funded by the UK's Economic & Social Research Council) which explored the role of social media analytics in the context of food policy.
What Are The Drone Anti-jamming Systems Technology?
The Food Sentiment Observatory: Exploiting New Forms of Data to Help Inform Policy on Food Safety & Food Crime Risks
1. The Food Sentiment Observatory: Exploiting
New Forms of Data to Help Inform Policy on
Food Safety & Food Crime Risks
Prof. Peter Edwards
University of Aberdeen
p.edwards@abdn.ac.uk
ES/P011004/1
Dr. Susan Pryde
Head of Science Strategy &
Information Analysis, FSS
Susan.Pryde@fss.scot
2. Introduction
• Keen to demonstrate the potential
value of using new forms of data
from sources such as social media
to inform policy.
• Data of interest in their own right
and when brought together with
other data that FSS collects or has
access to - for example, within local
authorities.
THE PUBLIC-SECTOR FOOD BODY FOR
SCOTLAND CREATED BY THE FOOD
(SCOTLAND) ACT 2015.
4. Project Objectives
• Develop a software platform to collect new forms of data from a range
of sources (i.e., social media channels, blogs and review sites) and
collate this with existing Food Standards Scotland data assets and
intelligence.
• Employ the Observatory to explore, via three policy-relevant use cases,
the role of new forms of data in assessing and shaping aspects of food
policy.
• To evaluate the utility of new forms of data in the policy context through
a series of policy sprints with Food Standards Scotland staff.
5. Project Objectives
• Develop a software platform to collect new forms of data from a range
of sources (i.e., social media channels, blogs and review sites) and
collate this with existing Food Standards Scotland data assets and
intelligence.
• Employ the Observatory to explore, via three policy-relevant use cases,
the role of new forms of data in assessing and shaping aspects of food
policy.
• To evaluate the utility of new forms of data in the policy context through
a series of policy sprints with Food Standards Scotland staff.
Use Case #1:
• Consumer and business attitudes to the differing food
hygiene information systems used in Scotland and the
other UK nations.
Use Case #2:
• Study of an historical E.coli outbreak to understand
effectiveness of existing monitoring and decision making
protocols.
Use Case #3:
• Understanding how social media data can be used to
respond to new and emerging forms of food fraud.
6. The Data Conundrum
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7. The Data Conundrum
Prohibited to copy any
content by any means for
any purpose without our
permission…
You will not engage in
Automated Data
Collection without
Facebook's express
written permission…
You may not reproduce,
download, or reuse the
materials…
You will not modify, derive
work from, use, or store
any Content outside of
Google Maps…
8. Agile Methodology
1 day policy jam
(review, identify)
Acquire
data
Review preliminary
results
Adapt
analyses
1/2 day policy jam
(outcomes)
Software tool – development sprints
9. Policy Sprint 1: Food Hygiene Information
Scheme (FHIS) Review
• Context:
– Ongoing review of food hygiene
information scheme in Scotland.
• Review activities include focus groups,
consumer attitudes survey, local
authority input …
GOAL
To have a food hygiene information scheme that is
well understood and used by consumers and that, as a
consequence, influences consumer purchase
behaviour. As well as informing consumers, the
scheme will act as a disincentive to non-compliance
and reward sustainably compliant businesses through
more customers choosing to visit them.
18. Data Inspiration & Discovery
• Adapted Open Policy Making
Toolkit Data Discovery cards:
19. Policy Sprint 1: Food Hygiene Information
Scheme (FHIS) Review
• Questions
– How is food hygiene being discussed on
social media?
– How do the Scottish (FHIS) and rUK (FHRS)
food hygiene schemes compare in terms of
their online presence/ recognition?
• Experiment 1: Investigate food hygiene
discourse on social media in Scotland
and England.
• Experiment 2: Compare mentions of
the FHIS (Scotland) and FHRS
(England/Wales) schemes on social
media.
20. FHIS Review: Eggs & Brexit
Keyword Set England Scotland
General 417,924 41,426
FHIS 130,359 13,008
FHRS 41,294 3,285
Data Collection
5 - 28 August 2017
Total Tweets collected: 2,907,577
Tweets used for analysis: 640,314
21. FHIS Review: Eggs & Brexit
Keyword Set England Scotland
General 417,924 41,426
FHIS 130,359 13,008
FHRS 41,294 3,285
Data Collection
5 - 28 August 2017
Total Tweets collected: 2,907,577
Tweets used for analysis: 640,314
22. FHIS Review: Eggs & Brexit
Keyword Set England Scotland
General 417,924 41,426
FHIS 130,359 13,008
FHRS 41,294 3,285
Data Collection
5 - 28 August 2017
Total Tweets collected: 2,907,577
Tweets used for analysis: 640,314
23. FHIS Review: FHIS vs FHRS
Keyword Set England Scotland
General 417,924 41,426
FHIS 130,359 13,008
FHRS 41,294 3,285
Data Collection
5 - 28 August 2017
Total Tweets collected: 2,907,577
Tweets used for analysis: 640,314
26. FHIS Review: Outcomes
• FHIS
– Not actively promoted by FSS for over 3
years so they expected that the number of
tweets on the scheme would be small -
which it was.
– “fhis” occurs frequently in tweets – but
often as a typo of “this”.
– One one Scottish LA actively using Twitter
to communicate food hygiene results.
• FHRS
– Consumers do refer to terms such as
rating, 5 star, etc .
– In the main, higher ratings were the
subject of tweets.
• Tweets were identified highlighting a
connection between Salmonella and
papaya in the USA.
– A potential emerging risk which FSS could
track in future .
• Tweets were identified which
suggested a possible food poisoning
incident in a particular geographic
location in Scotland.
Results are being incorporated into FHIS
Review, together with other evidence.
27. Policy Sprint 2: E.coli Risk & Illness
• Questions
– What opinions are expressed on social media in
Scotland towards E.coli in the context of high-risk
foods?
– Do consumers in Scotland associate certain food
types/food behaviours when self-reporting illness
on social media?
• Experiment 1: Attitudes expressed on social
media towards E.coli in the context of
unpasteurised dairy products.
• Experiment 2: Attitudes expressed on social
media towards E.coli in the context of gourmet
burgers.
• Experiment 3: Associations between self-
reported illness and certain food types/food
behaviours.
28. E.coli Risk & Illness: High Risk Foods
• Experiment 1
(E.coli & Unpasteurised Dairy)
– Investigation of discussions related to
food poisoning and E.coli in the
context of the Errington Cheese
outbreak.
• Experiment 2
(E.coli & Gourmet Burgers)
– Initial data exploration studies
concluded that there was insufficient
data.
Data Collection
July 2016 – Jan 2018
Unique articles collected: 87
29. E.coli Risk & Illness: Illness Reporting
174 keywords/phrases associated with
gastric illness symptoms
boak, gastro geyser, puke, spew,
trots, upchuck, vomit …
827 keywords associated with food and food
behaviours used to guide further analysis.
Data Collection
16 October - 18 December 2017
Scotland
Total Tweets collected: 12,269
Tweets used for analysis: 6,651
30. E.coli Risk & Illness: Food Behaviours
Chicken
boak feel ill food poisoning
spew threw up toilet
Fish
boak puke toilet vomit was sick
Chocolate
boak diarrhoea feel sick
feeling sick felt sick food poisoning
puking toilet
174 keywords/phrases associated with
gastric illness symptoms
boak, gastro geyser, puke, spew,
trots, upchuck, vomit …
Data Collection
16 October - 18 December 2017
Scotland
Total Tweets collected: 12,269
Tweets used for analysis: 6,651
31. E.coli Risk & Illness: Food Behaviours
Chicken
boak feel ill food poisoning
spew threw up toilet
Fish
boak puke toilet vomit was sick
Chocolate
boak diarrhoea feel sick
feeling sick felt sick food poisoning
puking toilet
174 keywords/phrases associated with
gastric illness symptoms
boak, gastro geyser, puke, spew,
trots, upchuck, vomit …
Data Collection
16 October - 18 December 2017
Scotland
Total Tweets collected: 12,269
Tweets used for analysis: 6,651
32. E.coli Risk & Illness: Outcomes
• Errington Cheese Incident
– News article analysis informing FSS
Comms/Media strategy for future
incidents.
• Social media influencers
• Illness Reporting
– Weakness of simple keyword searches
– Even with keyword enrichment, still a lot
of noise in data.
– #sicknessbug !!
• Food Behaviours
– Reporting of overeating/binging.
– Feed into future FSS diet/healthy eating
messages.
33.
34. Summary
• Data access
– More challenging than we
imagined!
• FSS now better understand social
media data, and its potential for
policy making.
– Open Policy Making Toolkit.
• Some of the issues FSS wanted to
explore don’t feature in social
media discourse.
• Importance of vocabulary
expansion and language context.
• Capturing the processes of data
gathering, selection,
manipulation …
35. Thanks…
• University of Aberdeen
– Milan Markovic
– Nikol Petrunova
– David Corsar
– Chenghua Lin
• Food Standards Scotland
– Susan Pryde
– Sam McKeown
– Jacqui McElhiney
– Ross Clark
Economic & Social Research Council
Editor's Notes
Introduction to the project including the link to policy-making
SECONDARY OBJS:
To engage the public in debate and discussion about the role of new forms of data in relation to policy. (This strand of activity will explore the views of the public on this issue through activities at food and science festivals).
To enhance the capacity of Food Standards Scotland to incorporate new forms of data into their future policy toolset.
SECONDARY OBJS:
To engage the public in debate and discussion about the role of new forms of data in relation to policy. (This strand of activity will explore the views of the public on this issue through activities at food and science festivals).
To enhance the capacity of Food Standards Scotland to incorporate new forms of data into their future policy toolset.
policy ‘sprints’
3 month blocks of effort during which academic researchers and FSS staff work together on a particular policy use case.
Inspired by the obvious alignment between agile approaches to software development and the agile approach to policy favoured by Open Policy Making.
PolicyJam to commence sprint1: APRIL 27th 2017
PolicyJam to commence sprint1: APRIL 27th 2017
PolicyJam to commence sprint2: September 28, 2017
Experiment 1 – Attitudes expressed on social media towards E.coli in the context of unpasteurised dairy products
Focus of the experiment: To explore and analyse social media discussions related to artisan cheese in connection to food poisoning and E.coli in the context of the Errington Cheese outbreak.
01 Jan 2016 – 31 Oct 2017
Experiment 2 - Attitudes expressed on social media towards E.coli in the context of gourmet burgers
Focus of the experiment: To explore and analyse social media discussions related to burgers in connection to food poisoning and E.coli.
Jan 2013 - Dec 2016
Experiment 3 - Associations between illness reporting and food
Focus of the experiment: To explore and analyse social media discussions containing mentions of self-reported illnesses related to food. The analysis should help to understand the associations between illness reporting and specific types of food and food related behaviours (i.e. eating out at restaurants or cooking certain types of food in the home, or eating at friends or family).
Originally intended : April 2014 – April 2016 , currently working with Live Data
