Could social media help us prepare for future disruptions to food systems? Thanks to the help of over a thousand volunteers, a SFN-backed project is now close to finding out
The past few years have demonstrated just how vulnerable our food systems are to large-scale disruptions, from pandemic lockdowns and social distancing to labour issues and extreme weather events. For the consumer, these can result in product shortages and empty supermarket shelves. Such uncertainty looks set to continue, as food producers start to feel the effects of climate change, and global political systems remain fragile. Consequently, a key part of the STFC Food Network (SFN)’s work is to research ways we can build resilience into food supply chains, so that they can keep functioning even when situations (and people’s shopping behaviour) change quickly.
Potentially, this could include harnessing the power of information contained in social media posts. One of the winning projects in the 2020 SFN Sandpit competition is investigating whether it could be possible to apply deep learning techniques to build a computer model that can forecast specific food-system disruptions (such as flour running out of stock) on the basis of the text and images that individuals post online (e.g. comments about flour).
Project lead Dr Laura Wilkinson (Senior Lecturer in Psychology at Swansea University) explains the rationale: “The COVID-19 pandemic saw an unprecedented level of conversation around food on social media, with many people experiencing shortages of key staples such as flour for the first time. We suggest that understanding how people reacted during the pandemic may help us to understand what might happen to the food system if other events occur that lead to uncertain times.”
The project’s ultimate aim is to train a machine learning programme to automatically detect patterns in people’s social media activity that indicate changes to food systems, and how consumers react to these. Training such a model, however, requires a detailed, annotated dataset, so that it can ‘learn’ which information is relevant to spot trends. To compile this, Laura and her colleagues created a database of just over a million anonymised Twitter posts, using searches with food-related keywords.
The trouble is that some of these posts may not actually be about food at all, as Laura explains: “Many food words can also refer to other things. For example, the word ‘coconut’ could have been used to describe someone wanting to be able to eat a coconut or could have been used to describe their favourite shampoo scent. On the other hand, a tweet might be referring to food but not actually mention a food word like 'banana'. For instance, it might be referring to home deliveries and mentioning supermarkets. Unfortunately, computers are not very good at interpreting these situations.”
This means that, in order to advance the project to the next stage, each tweet in the database first needs to be manually checked and classified as either being about food or not. As a small project team, this would have taken them an unfeasibly long amount of time… so they decided to call in reinforcements, by launching the project on Zooniverse!
Founded in 2009, Zooniverse is perhaps the best-known citizen science platform, and gives any member of the public the opportunity to contribute valuable data for research projects. These typically involve categorising data that computers would struggle to do automatically, such as picking out animals in camera trap footage, transcribing historical documents, or identifying cell features from microscope images. For this project, participants were asked to read through the Twitter posts and decide whether they were about food or not.
Since launching on Zooniverse in July this year, the response has been phenomenal. By the beginning of August, over 209,000 Tweets had been classified by 1,200 volunteers, reaching a high of over 36,500 classifications in a single day. But besides the raw data, Laura has been impressed by how the platform encourages dialogue between contributors and the research teams. “What makes citizen science projects distinct from many other research projects is that there is a high degree of knowledge sharing: the Zooniverse contributors are not passive participants” she says. “On the discussion boards for the project, participants have often reflected on what particular tweets mean to them and how they relate to their own food experiences during the pandemic. This really adds value to the study, giving us a much greater breadth of lived experiences.”
The discussion boards also revealed how social and cultural differences can impact the understanding of food-related content, which could help inform the design of future studies. As Laura explains: “Our database is restricted to UK-located Tweets, but Zooniverse contributors are based all around the world. This has caused some questions about certain terms, for instance about the names of some UK supermarkets that aren’t widely known abroad, and whether Easter eggs in the UK are edible. The two-way discussion boards are a great feature, as they enable us to respond rapidly to these issues, and share information with the whole community of contributors.’
The team hope to start training the first machine learning model in September 2022. Once the workflow is established, Laura hopes it can be adapted to process information on an international scale and from different social media platforms.
“I’m talking about Zooniverse to anyone who will listen – I think it’s an absolutely brilliant platform, which I definitely hope to use more of in the future” she says. “For instance, it would be very interesting to train an algorithm to classify photographs of meals that people post on social media for specific properties, such as the number of food components and portion size.”
“As recent events have shown, we live in uncertain times and we can’t take our food systems for granted, presuming that we can always get exactly what we want from the supermarket, whenever we want” she added. “It is imperative that when global disruptive events occur, we have the information and the means to ensure that the most vulnerable people in our societies who may be food insecure aren’t hit the hardest.”
September 2022 - Caroline Wood, Freelance Science Writer