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What can tweets about contact tracing apps tell us about attitudes towards data sharing for public health? (Part 3)

At the end of my last blog post about Covid-19 apps, I speculated it was unlikely that the UK’s Track and Trace app would gain enough public trust and support to be a success.  Since that blogpost was published, it was announced that UK’s app might not be ready until winter, followed by news that the centralised NHSX app has been abandoned for a decentalised alternative developed by Apple/Google.  

Many people have reacted to this news on Twitter resulting in a spike of tweets about the Track and Trace app (Figure 1). In this blog post I will present findings from sentiment analysis on these tweets to understand people’s reactions to the new decentralised app and discuss the future of data-sharing post-Covid-19.  

Graph showing number of tweets about Covid-19 tracing apps
Figure 1: Daily number of tweets about all ‘Covid-19’/’Coronavirus’ apps from all countries (blue) and only  the UK’s ‘Track/Test and Trace’ app (orange), collected 24 April to 16 June 2020 and 17 June to 25 June 2020 respectively.  

Holly Clarke

Leeds Institute for Data Analytics

Holly Clarke is an Intern at Leeds Institute for Data Analytics, applying data science solutions to solve complex, real-world challenges. She is working for the LifeInfo project with Michelle Morris, researching attitudes towards novel lifestyle and health data linkages and how access to this information could improve public health. 

Read the previous parts of this blog:

Read part 1
Read part 2

The positives and negatives of Covid-19 apps  

This sentiment analysis includes tweets about the UK’s Track and Trace app posted between 17th and 25th June 2020, thereby, focusing in on recent events.  Sentiment analysis matches words within tweets with common positive and negative words categorised in the “Bing” dataset, developed by Bing Liu in order to identify their sentiment. Overall, this analysis tells us there are more commonly used negative words within recent tweets about Track and Trace app than positive, indicating the tweets hold mainly negative content (Figure 2).  

Proportion of sentiment words in tweets that are positive and negative for tweets about all Covid-19 apps, collected 24 April to 16 June 2020, and tweets about the UK’s Track/Test and Trace app, collected 16 June to 25 June 2020.
Figure 2: Proportion of sentiment words in tweets that are positive and negative for tweets about all Covid-19 apps, collected 24 April to 16 June 2020, and tweets about the UK’s Track/Test and Trace app, collected 16 June to 25 June 2020.  

The nature of these positive and negative words is also very telling. The negative words refer predominantly to the management of the app rather than issues about data privacy and the app itself; “failure”, “incompetence”, “fiasco”, “shambles”, “chaos”, “disaster”, “lying” and “debacle” all feature prominently (see Figure 3).  As a comparison, sentiment analysis on all general tweets from 24 April to 16 June 2020 about Covid-19 apps (Figure 4) shows common negative words to be more technology focused and in line with common concerns about data-sharing – “breach”, “risk”, “concerns”, “issues”.  

50 most frequently used positive and negative sentiment words used in tweets about the UK’s Track/Test and Trace app, collected 16 June to 25 June 2020.
Figure 3: 50 most frequently used positive and negative sentiment words used in tweets about the UK’s Track/Test and Trace app, collected 16 June to 25 June 2020.  

The positive words refer to more common topics around data-sharing and technology e.g. “trust”, “protection” and “safe” across both datasets of tweets. This indicates engagement with the topic of data sharing and a significant proportion of the tweet sentiment words are positive across both datasets.  

As part of the sentiment analysis I have controlled for negation, inversing the positive/negative categorisation if a common negator is directly before the word (e.g. “not good” or “don’t trust”). In the figures these are shown with the pre-fix “neg_”.  However, linguistic features such as sarcasm, humour and questioning are not easily picked up through sentiment analysis. Some instances of positive words like ‘wow’ or ‘promises’ may also be used in a critical way.   

Overall, although both datasets of tweets include more negative than positive words, the recent events around the UK’s Track/Test and Trace app seem to have framed the app more negatively than Covid-19 apps more generally due to “waste” and issues around the development of the app.   

50 most frequently used positive and negative sentiment words used in tweets about ‘Covid-19’/’Coronavirus’ apps from all countries, collected 24 April to 16 June 2020.
Figure 4: 50 most frequently used positive and negative sentiment words used in tweets about ‘Covid-19’/’Coronavirus’ apps from all countries, collected 24 April to 16 June 2020. 

What will Track and Trace mean for people’s attitudes to data sharing?  

When I began writing this blog series on Covid-19 apps, countries across the world were rapidly launching contact tracing apps to quelle the spread of coronavirus through technology and the UK was poised to trial their app on the Isle of Wight. Two months later the Track and Trace app journey has certainly not been smooth and the app’s importance has been downgraded from “world beating” to “the cherry on the cake”.  But what does this late-stage Apple/Google switch will mean for public opinion?  

Research on attitudes to data-sharing, as discussed in my last blog post, frequently finds that people’s willingness to share their data is dependent on which actors are involved. People tend to have high trust in the NHS and the lowest trust in private companies. Hence, we might expect the shift from an NHSX app to one involving tech giants Apple and Google to be met with opposition. However, the sentiment analysis indicates conversation about the Track and Trace app mainly focuses on the wastefulness and “shambles” of the switch rather than inherent mistrust in private companies.  

Initial findings from my work with the LifeInfo project, exploring public opinion about linking lifestyle data (e.g. supermarket loyalty card or fitness app data) with health records, may explain this. My analysis highlights that data-sharing and trust in actors is not as straight forward as might be expected.  

Although people generally have high levels of trust in health organisations, respondents repeatedly expressed concerns that their supermarket loyalty card data might be seen by their GP if these data were linked for health research. Many worried their GPs would unfairly judge their diet and lifestyle, and even withhold treatment. Yet, respondents were happy for supermarkets (private companies in which research finds people to have the least trust) to store and use their loyalty card data.  This indicates that attitudes about data sharing are not simply informed by trust in actors but are also influenced by the type of data involved and social norms about how it is currently used.  

In the context of coronavirus apps, this could mean that users are more comfortable with mobile phone providers using data to alert them about exposure to coronavirus than the government or NHS. Many mobile phone users share vast amounts of data with technology companies through everyday use of apps and services which they may be uncomfortable sharing with the government or healthcare providers.  Therefore, a contact tracing system involving Apple and Google, and especially a decentralised one which enables more data privacy, might encourage wider use than the NHSX app.  

The future of data sharing post Covid-19 

The Covid-19 pandemic will undoubtedly create lasting changing across many aspects of our lives including attitudes towards data sharing.  The pandemic had led us to consider sharing unprecedented amounts of data it has also made clear the inadequacies of our medical data sharing systems.   

In the context of the LifeInfo study, access to lifestyle data linked to health records could help researchers better understand and prevent diseases such as diabetes, certain cancers and heart disease, The World Health Organization attributes 30% of yearly global deaths to poor diet and physical inactivity, so it is a substantial challenge. However, for participants to willingly share their data they must trust organisations to safely, responsibly and transparently use it. 

Successful contact tracing apps had the potential to demonstrate that data sharing could help improve health while maintaining personal privacy and data security. Yet, technological failings, privacy concerns, and government mismanagement in the UK could turn public opinion against data sharing initiatives in the same way other high-profile failings such as care.data didAbove all, the Track and Trace app highlights how detailed consideration of peoples attitudes towards data sharing is vital for initiatives to be successful.