Drs Luisa Zuccolo and Cheryl McQuire, Department of Population Health Sciences, Bristol Medical School, University of Bristol.

The problem

Soon after the World Health Organisation (WHO) declared COVID-19 a pandemic on March 11th 2020, the UN declared the start of an infodemic, highlighting the danger posed by the fast spreading of unchecked misinformation. Defined as an overabundance of information, including deliberate efforts to disseminate incorrect information, the COVID-19 infodemic has exacerbated public mistrust and jeopardised public health.

Social media platforms remain a leading contributor to the rapid spread of COVID-19 misinformation. Despite urgent calls from the WHO to combat this, public health responses have been severely limited. In this project, we took steps to begin to understand and address this problem.

We believe that it is imperative that public health researchers evolve and develop the skills and collaborations necessary to combat misinformation in the social media landscape. For this reason, in Autumn 2020 we extended our interest in public health messaging, usually around promoting healthy behaviours during pregnancy, to study COVID-19 misinformation on social media.

We wanted to know:

What is the nature, extent and reach of misinformation about face masks on Twitter during the COVID-19 pandemic?

To answer this question we aimed to:

Upskill public health researchers in the data capture and analysis methods required for social media data research;
Work collaboratively with Research IT and Research Software Engineer colleagues to conduct a pilot study harnessing social media data to explore misinformation.

The team

Dr Cheryl McQuire got the project funded and off the ground. Dr Luisa Zuccolo led it through to completion. Dr Maria Sobczyk checked the data and analysed our preliminary data. Research IT colleagues, led by Mr Mike Jones, helped to develop the search strategy and built a data pipeline to retrieve and store Twitter data using customised application programming interfaces (APIs) accessed through an academic Twitter account. Research Software Engineering colleagues, led by Dr Christopher Woods, provided consultancy services and advised on the analysis plan and technical execution of the project.

Cheryl McQuire, Luisa Zuccolo, Maria Sobcyzk, Mike Jones, Christopher Woods. (Left to Right)

Too much information?!

Initial testing of the Twitter API showed that keywords, such as ‘mask’ and ‘masks’, returned an unmanageable amount of data, and our queries would often crash due to an overload of Twitter servers (503-type errors). To address this, we sought to reduce the number of results, while maintaining a broad coverage of the first year of the pandemic (March 2020-April 2021).

Specifically, we:

I) Searched for hashtags rather than keywords, restricting to English language.

II) Requested original tweets only, omitting replies and retweets.

III) Broke each month down into its individual days in our search queries to minimise the risk of overload.

IV) Developed Python scripts to query the Twitter API and process the results into a series of CSV files containing anonymised tweets, metadata and metrics about the tweets (no. of likes, retweets etc.), and details and metrics about the author (no. of followers etc.).

V) Merged data into a single CSV file with all the tweets for each calendar month after removing duplicates.

What did we find?

Our search strategy delivered over three million tweets. Just under half of these were filtered out by removing commercial URLs and undesired keywords, the remaining 1.7m tweets by ~700k users were analysed using standard and customized R scripts.

First, we used unsupervised methods to describe any and all Twitter activity picked up by our broad searches (whether classified as misinformation or not). The timeline of this activity revealed clear peaks around the UK-enforced mask mandates in June and September 2020.

We further described the entire corpus of tweets on face masks by mapping the network of its most common bigrams and performing sentiment analysis.

We then quantified the nature and extent of misinformation through topic modelling, and used simple counts of likes to estimate the reach of misinformation. We used semi-supervised methods including manual keyword searches to look for established types of misinformation such as face masks restricting oxygen supply. These revealed that the risk of bacterial/fungal infection was the most common type of misinformation, followed by restriction of oxygen supply, although the extent of misinformation on the risks of infection decreased as the pandemic unfolded.

Extent of misinformation (no tweets), according to its nature: 1- gas exchange/oxygen deprivation, 2- risk of bacterial/fungal infection, 3- ineffectiveness in reducing transmission, 4- poor learning outcomes in schools.

Relative to the volume of tweets including the hashtags relevant to face masks (~1.7m), our searches uncovered less than 3.5% unique tweets containing one of the four types of misinformation against mask usage.

A summary of the nature, extent and reach of misinformation on face masks on Twitter – results from manual keywords search (semi-supervised topic modelling).

A more in-depth analysis of the results attributed to the 4 main misinformation topics by the semi-supervised method revealed a number of potentially spurious topics. Refinements of these methods including iterative fine-tuning were beyond the scope of this pilot analysis.

Our initial exploration of Twitter data for public health messaging also revealed common pitfalls of mining Twitter data, including the need for a selective search strategy when using academic Twitter accounts, hashtag ‘hijacking’ meaning most tweets were irrelevant, imperfect Twitter language filters and ads often exploiting user mentions.

Next steps

We hope to secure further funding to follow-up this pilot project. By expanding our collaboration network, we aim to improve the way we tackle misinformation in the public health domain, ultimately increasing the impact of this work. If you’re interested in health messaging, misinformation and social media, we would love to hear from you – @Luisa_Zu and @cheryl_mcquire.

Note:

This blog post was original written for the Jean Golding Institute blog

Luisa Zuccolo , MRC Integrative Epidemiology Unit and Department of Population Health Sciences, Bristol Medical School, University of Bristol

Cheryl McQuire, School for Public Health Research/Centre for Public Health and Department of Population Health Sciences, Bristol Medical School, University of Bristol

Follow Luisa and Cheryl on Twitter

What’s the issue?

Drinking alcohol during pregnancy continues to stir passionate and polarised reactions. The issue has once again come into sharp focus. In England, the National Institute for Health and Care Excellence (NICE) is proposing to measure all alcohol use in pregnancy and transfer this information to a child’s health records, without explicit agreement from the mother. The aim of the proposal is to ensure better diagnosis and support for those with lifelong conditions caused by drinking in pregnancy that can include problems with learning, behaviour and physical abnormalities, known as Fetal Alcohol Spectrum Disorders (FASDs). However, the NICE proposal has been met with strong opposition from some organisations, which say that this breaches pregnant women’s right to medical privacy.

A balancing act

Benefits of introducing the proposed FASD NICE Quality Standards

It’s important to remind ourselves why measuring and sharing information on drinking in pregnancy could be worthwhile, and who will benefit.

Information – the key to understanding

Official guidance recommends that it is safest not to drink at all during pregnancy, or when trying for a baby. But a quarter of people in the UK are not aware of this guidance and the UK has the fourth highest rate of drinking in pregnancy in the world. The new NICE quality standards propose to rectify this by making it compulsory for midwives (or other health care professionals) to have conversations about alcohol with pregnant women. The idea is to help women to make informed choices about drinking in pregnancy.

Information is in everyone’s interest. We still don’t know enough about the effects of different levels of alcohol use in pregnancy. This is a tricky area to study, in large part because we don’t have enough information. If we don’t measure, we can’t fully understand the effects of alcohol in pregnancy (not even to confirm whether small amounts are safe). This has been our area of research for a number of years, and it is important both because many pregnant women drink alcohol, and because they should have the right to be better informed. Current abstinence guidelines are largely (but not solely) based on the precautionary principle. Our research has provided some evidence that even low levels of use (two drinks a week or fewer) can have negative effects, including smaller babies and preterm birth. We need more information to find out about the full extent of these risks – including whether the risks are genuinely there – to ensure that women can make informed decisions based on the best possible evidence.

Diagnosing fetal alcohol spectrum disorders (FASD)

Fetal alcohol spectrum disorders are severely underdiagnosed. They are characterised by lifelong problems with learning, behaviour and, in some cases, physical abnormalities. Contrary to what many think, these are common disorders. Our recent research suggests that between 6 and 17% of children in the UK could have symptoms consistent with FASD. Without good information on exposure to alcohol in pregnancy, many of these children remain ‘invisible’ to services and do not get the support that they need.

Concerns about the Quality Standards

Despite the potential benefits of these proposals, there are several unresolved issues that need tackling urgently.

Stigma and trust

If women feel stigmatised, they might lie about their drinking, invalidating any data collection. If they can’t trust their healthcare providers, then we can’t trust the data – so what would be the point of collecting it?

Tradeoffs

What would women be offered, in exchange for volunteering this information? What’s in it for them? It would be unbalanced and probably unethical to request information about drinking in pregnancy, at the risk of stoking maternal anxiety, without explaining the reasons, or offering support if so desired. The treatment of pregnant women who smoke provides an appropriate model – information is recorded on antenatal notes, and support to quit is offered at the same time. We need to guarantee a non-judgmental and supportive approach to listening when it comes to alcohol too.

Confidentiality

Women should be able to opt out. For those opting in, it should be made clear that the same high levels of confidentiality will apply as are already in place for current information from maternity notes and child health records. These new data on alcohol use should be no different and must be covered by existing guarantees.

Finding the right balance

So, where is the balance between the benefits and risks of the proposed changes? We often talk about burdening pregnant women with anxieties, but we neglect to talk about the lifelong consequences of emotional and behavioural problems arising from exposure to alcohol in pregnancy – these pose real everyday challenges for families, for many years to come. If on the one hand, maternal health is child health, then on the other child health is maternal health.

As we hear in these COVID-19 times, health is a marathon not a sprint

We need to continue shifting the focus from ‘healthy pregnancies’ to ‘healthy families’. The former can be met with resistance by those evoking the dangers of the surveillance state and policing women’s ‘baby-making’ bodies. The latter reminds us of the many individuals involved, all equally important, all of whom need support for the long term beyond those initial nine months. We believe that NICE should listen to the plurality of women and families’ voices. The debate on recording alcohol in pregnancy will lead to constructive health gains that will benefit all.

Tag: Luisa Zuccolo

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