Why the inequalities in our information diets matter (Part 1 of 2)

Part I: Visiting the United States in November 2016

As I write this, I am on a train travelling from Boston to New York City, at a time when people in the United States are still coming to terms with what they thought they knew about their country. The trip has been rushed because I need to be back in Sydney to catch up on work, which meant I didn’t have time to catch up with all the excellent people on the east coast. Here is a picture of approximately where I am right now.

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Between Boston and New York City, about an hour before New Haven.

Without the benefit of hindsight, it feels like the disintegration of knowledge continues to accelerate into the post-truth era; that societies around the world are struggling to hold onto their values across a populous that outsources its opinions and beliefs to some nebulous idea of whoever is both influential and accessible. While information overload is not at all a new idea, we do not yet entirely understand how information overload may actually restrict rather than open up what we hear, what we click on, what we soak up into our worldview; and how that can reinforce our echo chambers to create polarisation and conflict.

While it is not something we usually like to admit, the information to which we are exposed – our information diets – are an excellent predictor of our attitudes and opinions. This is not necessarily because our information diets causally affect what we believe and how we act, but because we surround ourselves with people who agree with us and rarely seek out opinions that are contrary to our own. And even when different opinions are put right there in front of us, we disregard them.

For researchers who like to observe the world while pretending not to be part of it, right now looks less like a post-truth era and more like a golden era for understanding the impact of news media and social structure on human decision-making and behaviour.

There has never been a better time to take advantage of massive streams of data about information search and exposure to measure their impact on opinion, attitudes, decisions, and behaviours. In my team, we already use social connections on Twitter to train machine learning classifiers that can predict whether you are likely to have negative opinions about vaccines. We are now close to finalising work showing that a population’s (relevant) information diet can explain the variance in vaccine coverage of individual states in the United States.

img_2087More of Boston walking from Back Bay to Boston Children’s Hospital.

Part II: Some news
While I am still looking at the bright orange colours of the United States, and before I head back to the purple jacarandas in Australia, I will stop here. But I will share some related news soon.

 

Twitter users with anti-vaccine opinions are relatively easy to spot if we can measure their misinformation exposure

So…I have been systematically collecting tweets about human papillomavirus (HPV) vaccines since October 2013. We now have over two hundred thousand tweets that included keywords related to HPV vaccines, and the first of two pieces of research we have undertaken using these data has just been published in the Journal of Medical Internet Research. It covers 6 months, 83,551 tweets from 30,621 users connected to each other through 957,865 social connections. The study question is a relatively simple one – we wanted to find out about how many people are tweeting “anti-vaccine” opinions about HPV vaccines, the diversity of their concerns, and how the misinformation exposure is distributed throughout the Twitter communities.

What we found was in some ways surprising – around 24% of the tweets about HPV vaccines were classified as “negative” (more on this later). To me, this seems like a very large proportion given that only around 2% of adults are actually refusing vaccinations for their children. In other ways, I’m less surprised because of how many people have so many other unusual beliefs, and the number of surveys that suggest that 20% to 30% of adults believe that vaccines cause autism.

Looking at how people follow each other within the group of 30,621 users, we found that around 29% of everyone who tweeted about HPV vaccines were exposed to a majority of these “negative” tweets because of who they follow.

To classify the tweets as either “negative” or “neutral/positive”, we used supervised machine learning classifiers that were slightly different to the normal kinds of classifiers that just use information about the text to examine the sentiment of a tweet. I’ll be talking about these machine learning classifiers at the MEDINFO conference in Sao Paulo this August.

What we really wanted to know was how many Twitter users were being exposed to this negative kind of information – usually anecdotes about harm, conspiracy theories, complete fabrications, or some strange amalgamation of all of them – whether these users mostly grouped together, and how far their information reached across communities that might be making decisions about HPV vaccines for themselves or their children.

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A network of 30,621 Twitter users who posted tweets about HPV vaccines in a six month period. Users in orange were exposed mostly to negative opinions. Circles are users, larger ones have more followers within this group of users. Users more closely connected are generally positioned closer to each other in the picture.

We also wanted to know a bit more about the reach of the actual science and clinical evidence that is being published in the area. As researchers, we know that there are now studies showing that the HPV vaccine is safe and that there is early evidence of effectiveness in the prevention of cervical cancer, but we don’t really know who might be “exposed” to that kind of information.

Perhaps unsurprisingly, the people producing the science of HPV vaccines were located pretty much as far away as they could possibly be from the people exposed mostly to negative opinions. Most of the tweets linked directly to peer-reviewed articles came from the people in the very top left section of the network illustration above.

The main contribution of our study was to determine how much more likely it is that a user who was previously exposed to negative opinions would be to then tweet a negative opinion. The answer was: “a lot more”.

But to address the reasons why users’ opinions were relatively easy to predict if we know about the information they were exposed to in the past, we have to do a lot more work…

It could be that the opinions were “contagious” and spread through the community. It might also be that people end up forming “homophilous” connections with other users who express the same negative opinions about HPV vaccines. The much more likely explanation is that people who share opinions about all kinds of other things besides HPV vaccines (like guns, religion, politics, conspiracies, organic vegetables, crystals, and magical healing water) are more likely to be connected to each other, and their opinions about HPV vaccines are due to the breadth of misinformation that spreads to them from influential news organisations, celebrities, friends, and magical water practitioners.

It is important that we are careful to explain that the study only demonstrates an association between what people are exposed to in the past, and the direction of their expressed opinions after that. It does not show causation, and it does not tell us how those people came to believe what they do.

The study does tell us something important about how we might be able to estimate risks of poor vaccination decision-making within particular communities in space and time. One of the things we would like to be able to do is to examine where the concentrations of misinformation exposure are distributed geographically in a couple of countries (US and Australia – because that is where we know best), as a way of helping public health organisations better understand who might be vaccine anxious (or at risk of becoming vaccine anxious), and the specific concerns they might have. Because remember, only 2% of adults are conscientiously refusing to vaccinate their children, but an awful lot more of them might be forming their opinions based on the awful misinformation that spreads through the communities they inhabit.