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<p>Every day, we leave small traces of ourselves online. And we might not realize what these traces say about us. This week, computational social scientist <a href="https://business.columbia.edu/faculty/people/sandra-matz" rel="noreferrer noopener" target="_blank">Sandra Matz</a> explores how understanding what we <em>actually</em> do online – not just what we <em>think</em> we do – can help us improve our lives. <br/></p>
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<p><em>The transcript below may be for an earlier version of this episode.
Our transcripts are provided by various partners and may contain errors or deviate slightly from the audio.</em></p>
This is Hidden Brain, I'm Shankar Vedantam. I have a question for you: how well do you know yourself? Chances are, you'll tell me you know yourself very well. All of us like to believe this. We feel like we know ourselves better than anyone else does. Every day, we make choices based on this knowledge we have of ourselves. We decide how to spend our money, who to vote for, where to go for dinner -- based on what we know of our predilections and preferences. But our knowledge of ourselves is not always accurate. A host of biases and self-deceptions keep us from seeing ourselves clearly. When you ask people how smart they are, or how ethical they are, or how good-looking they are, for example, majorities say they are above average, which of course is mathematically impossible. But it isn't just about vanity. How many times have you gone to a restaurant you've been to before and ordered the same dish you ordered last time, only to remember after you started eating it that you didn't like it the last time? Or think about your last romantic entanglement that ended in disaster. By the time it ended, did you wonder how your past self could have gotten involved with someone so unsuitable? Over the last few decades, researchers in a variety of disciplines have discovered there is a much better way to understand people than to ask them questions. When you ask people what books they like to read, people will tell you about the novels and biographies they think they ought to like. If you ask them what movies they want to watch, they will tell you about the movies they aspirationally want to watch. But if you look at the books that people actually read, or the movies they actually watch, it usually paints a different picture of their preferences. This week on Hidden Brain, how understanding what we do, instead of listening to what we say, can help us make better financial choices, improve our physical and mental health, and maybe even bridge our political divides. Philosophers tell us the highest wisdom is to know ourselves. They say this precisely because knowing ourselves is difficult, not easy. It requires self-reflection, self-awareness, and a healthy dose of humility. At Columbia University, psychologist Sandra Matz studies how one aspect of our behavior can reveal surprising truths about who we are. Sandra Matz, welcome to Hidden Brain. Sandra Matz: Thank you so much for having me. Shankar Vedantam: Sandra, you grew up in a small village in Germany, which had two restaurants and no shops. Can you paint me a picture of the place where you grew up? Sandra Matz: Yes, happily. It's a village of 500 people in the very southwest corner of Germany. As you said, it has two restaurants, no shops, one church, I should say. It's very important to the people living there. And it was really like a small community. Shankar Vedantam: So one day I understand that your doorbell rang, and it was a neighbor reporting a missing rabbit? Sandra Matz: Yeah. I must have been, I think, eight, nine years old, and I had this pet rabbit or bunny called Schnuffel. And he was living outside. We had built him this house outside, my dad and I. And one day the neighbor comes and says that they found him in their garden, feasting on their vegetables and salad. And so they tried to catch him already, but unsuccessfully. And so they were trying to get more manpower. So now my entire family is up, their entire family is up. We're trying to get him. And I don't know if you've ever had a bunny or let alone try to catch one. They're really fast. So they zigzag around and it's almost impossible to catch them. So we must have looked like clowns running around. And that certainly didn't go unnoticed. So very soon into our hunt, I think the entire street really was involved. So we had someone was managing the traffic because the bunny would just kind of run from one side of the street to the next. And then we had like, it really felt like a command center. So people were strategizing about whether we should set up a trap for the rabbit or whether we should just try to lure him in with a treat. Eventually we caught him. But it was certainly an adventure for the entire neighborhood. Shankar Vedantam: How was the rabbit eventually recaptured? Was it a dramatic moment? Sandra Matz: It was a dramatic moment. So it was actually one of my neighbors who leaped and caught him on the, on the back leg. And I've just remembered that the rabbit's screaming. I never, I didn't even know that rabbits could scream that loudly. And then all the kids were crying because the rabbit was screaming. And so I think the adults were just happy that we got him. But it was certainly a dramatic capture. Shankar Vedantam: I'm getting a sense that this was a village where everyone knew everyone's business. Sandra Matz: Very much so. Very much so. Shankar Vedantam: So when you were 15, Sandra, you loved riding around the village with your boyfriend on a motorcycle. What was this bike like and where would you go? Sandra Matz: Yeah, so it was, I still remember it. It was like a red Suzuki Bandit. And I thought it was beautiful. So I was 15, he was a bit older, and we would just take it from one village to the next, through the hills and up and down the serpentines. And I loved riding it, but I was usually in the back. So at some point, I think I got really tired of being in the back. And I knew that I would have to wait for another three years, because in Germany, you get your license at 18 at the earliest. So I kind of tried to sneakily convince him to let me just try. I found this abandoned airfield, and I just told him, 'Let me just kind of ride for a few meters. You're going to get the bike back. It's all going to be good.' He was a bit skeptical at first, but then agreed to let me try. So we start, and I don't know exactly what happened. I think we must have kind of moved to the grass, and I was trying to pull the bike back. But suddenly I think I just turn on the gas, and the bike rises. My boyfriend falls off the back, and I just speed away. So I have no idea what I'm doing. Now suddenly I'm alone on the bike without any sense of like how to handle it. I'm trying kind of going left and right and left and right, and at some point I essentially crash on the side. Luckily, I was still going slowly, but there was my first experience riding the bike myself. Shankar Vedantam: Sandra and her boyfriend weren't hurt, but Sandra had to spend a year's worth of tutoring money to get the bike prepared. Sandra Matz: But in a way, that wasn't the worst part. For me, the worst part was that, I would say the minute we dropped the bike at the shop, everybody knew. So people knew about me asking my boyfriend to drive the bike, me crashing the bike, which was even worse. Otherwise, it could have potentially been a cool story, but certainly wasn't. So everybody knew what had happened. And just I was punished for weeks after with people asking me about it. Shankar Vedantam: When you say you were punished, how so? What was the reaction of your neighbors and friends? Sandra Matz: Very, I mean, very different. So some neighbors actually just went back to their own childhood, and they were like, 'Oh, that's like such a brave and fun thing to do.' And they just recounted their own childhood offenses. Others were like, 'How could you ever do this? We thought that you were actually one of the good kids on the block.' And so but it was just one conversation after the other that was all about me crashing the bike. Shankar Vedantam: Was there anything good that came from all the surveillance, Sandra? Sandra Matz: Certainly. I mean, I think that maybe not the bike surveillance. I think this one was a hard one. But generally speaking, the fact that my neighbors knew everything about my business also meant that there was a community that I felt safe in. So it was a community of people who knew me, who tried to help when I was looking for advice. And I've never quite experienced anything like it ever since. Shankar Vedantam: Was there a time when you in fact got very useful advice from these people because in fact they knew you quite well? Sandra Matz: I think so. I mean, some of them were just trying to interfere with my life. But some of the advice I got was incredibly helpful. So one of the ones that I still remember is that when I was finishing high school, I was thinking about doing a gap year. It sounded like a dream to me. I was like, well, you can travel the world. You can take a year off. Don't rush into university. But I remember being quite torn because not many of my friends were considering it. And I was very ambitious, I would say, at the time. So I was like, well, maybe it's just a waste of my time to spend a year traveling. I could start university, get a job. And luckily, a lot of my neighbors told me, like, 'Look, you seem like someone who's been always craving to leave this village, to see some of the world. Why don't you do it? Like, you can work for many, many years after. You should really consider it.' They helped me even find a job to scrape together the money. So I think that was one of the times that I felt very much supported. Shankar Vedantam: And in some ways, it sounds like they knew you almost better than you knew yourself. Sandra Matz: In some ways, I would say so, because I think it was a slightly less biased and maybe self-critical version of myself. Shankar Vedantam: Sandra's experience with the nosy neighbors in her village is what life has been like for most humans through most of human history. We've typically lived in small groups, and people in those groups have known everything there is to know about us. Today, many of us live in a different kind of village. It's a global village where anonymous entities, rather than our actual neighbors, have eyes on us. Not all of them have our best interests at heart. When we come back, what our digital footprints reveal about us and how this information can be used both to help us and to harm us. You're listening to Hidden Brain. I'm Shankar Vedantam. This is Hidden Brain, I'm Shankar Vedantam. Every day, as we go about our lives, we reveal aspects of ourselves to the world. If you visit a local bakery a lot, it's probably because you like pastries and baked goods. If you spend time in parks, it's because you value nature and recreation. Someone who rarely ventures outside their home except to go to work might be introverted. At Columbia University, computational social scientist Sandra Matz studies how the things we say and do reveal things about our thoughts, preferences, and personalities. Sandra, I want to talk about the clues we unintentionally leave behind us as we go about our lives. Let's start in the physical world. Many years ago, you were on a date, and things were going well, and you ended up at your date's apartment. Tell me what you did as soon as you got to his place. Sandra Matz: Yeah, it's a funny story, because I remember entering the apartment, and you meet someone for the first time, you have no idea who they are. And I entered the apartment, and it's pristine. It's first of all, it has this huge library, which I loved, and had books in Hebrew and English and French. So I was like, oh man, this is a bookworm. I love it already. Walk to the kitchen, it's sparkling clean, which I cannot say of my own kitchen. So I was highly impressed in the sense that everything had its place, like the knives were perfectly organized. I got some glasses for us and they were perfect, like no marks, no water marks anywhere. And I just kind of started building this image of who the person living in this apartment, the guy I was dating at the time, who he was. And it just felt like he was this curious, book-loving person with almost like an OCD sense of order. Shankar Vedantam: So you weren't wearing a hat and carrying around a magnifying glass, but it feels vaguely Sherlock Holmes-ian to me, what you were doing in that apartment. Sandra Matz: It does feel like Sherlock Holmes, and I think we do this all the time, right? We meet someone new, we look for clues of who that person is, could be their apartment, could be what they're wearing, could be what they're saying. It's really we're kind of trying to piece the puzzle pieces together in a way. Shankar Vedantam: How did things go with this date? Sandra Matz: Well, it worked out really nicely. He's now my husband and we have a 10-month-old. Shankar Vedantam: I'm wondering whether your impressions of your husband, your first impressions of him when you were dating, did they turn out to be accurate, Sandra? Sandra Matz: Very accurate. I would say he's probably the most curious person I know. He loves podcasts, loves reading, loves to learn everything about the world. And he also, I have to say, is a little bit OCD, in a good way. Shankar Vedantam: So the psychologist Sam Gosling has shown that people, in fact, are remarkably accurate at judging the personality of strangers when given the chance to snoop around their offices or bedrooms. Tell me about this work, Sandra. Sandra Matz: Yeah, so this is actually work that inspired all of my research in the digital space. So Sam Gosling was one of the first people to try and figure out, like, how good are strangers at judging our personality if they just take a look at our bedrooms and our offices? And he distinguished between these two types of cues that you can find. So he said, well, some of the cues that you find in someone's office or bedroom are the intentional identity claims that we put there, right? So we put up a poster of Lady Gaga, because that's the signal that we want to send to the world of, well, we're into music and this is the type of music that we like. But then there's also all of these other cues that we don't really think about, right? So the socks are disorganized, the bed isn't made. It's the opposite of my husband. It's just like probably a little bit more disorganized. So what Sam Gosling really showed is that if you combine all of these things, you get a pretty good sense of who the person living in these places is. Shankar Vedantam: So you say there are parallels between what happened in your village or your behavior when you visited your date's place and what happens to us online. It's as if your village neighbors now have access to your Facebook messages and credit card purchases? Sandra Matz: Yeah. So in a way, right? So I could take a look at your office or your bedroom, or I could see what my neighbors are doing. But on some level, I think we all now live in this, what I think of as a digital village. And so we all leave these traces, these digital traces all the time. That could be anything from the stuff that you post on social media. So again, relatively explicit identity claims to the data that is captured by your smartphone. So GPS records. Where do you go? Your credit card. What do you buy? And the same way that we could put the pieces together from someone's bedroom, we can also do that in someone's digital space. Shankar Vedantam: You say it takes shockingly little information to get an extremely granular picture about people, even in a big town like New York City. Now, there are millions of transactions that take place every day in New York. Finding any one person might seem like you're looking for a needle in a haystack. Sandra Matz: Yeah, this is actually one of my favorite studies that was coming out of MIT. And what they showed is that it's very easy to identify someone based on your spending records or your GPS records. So you can imagine, as you said, like there's millions of people in New York. And even if we say got access to all of their credit card spending, anonymize it so we don't have names, we don't have any personal identifiers, it's very easy to reverse engineer their data. You can imagine that, let's say, you go and get a matcha latte at Starbucks on 72nd Street in New York at 7:20 a.m., then you have lunch in a certain place, and maybe you take a cab downtown at night, there's at some point only so many people who have exactly that same signature. So you can almost think of it as a fingerprint that is made up of your data. Shankar Vedantam: I want to talk about some of the ways you and others have found that our digital footprints can reveal deep truths about our lives. In 2019, you ran a study that predicted people's income based on an extremely unlikely source. Tell me what you found, Sandra. Sandra Matz: Yeah, so that's in a way the most interesting part of this entire field of research. It's like, yeah, we can identify you as a person, we can know that it's Shankar based on your data. But for me, the more interesting part is actually that we can dive into your psychology. So we can take a look at what's going on inside your mind. And so the study that we did when we tried to predict someone's income was essentially relying on their Facebook data. So what is it that people talk about and post on social media? And I think there were some really interesting, sometimes quite uncomfortable truth that we discovered. But overall, the bottom line was that just by looking at what you talk about on Facebook, we can have a pretty good sense of your socioeconomic status. Shankar Vedantam: I'm puzzled by how that would be the case. I mean, what does my posting about a movie that I've watched or a vacation that I've taken, how do you tell what my income is based on those postings, Sandra? Sandra Matz: Yeah. So when you start opening the black box, what you see is some of the cues are relatively obvious. So you can imagine that people with a lot of money, they talk about the vacations that they're going to take, they talk about expensive luxury brands. A lot more often than people who are struggling to make ends meet. But there's also these more subtle cues that I found even more interesting, which is, for example, that lower income people, they talk more about themselves and they talk more about the present than higher income people. And in the beginning, you might be wondering like, why might this be the case? And I think it's just that it's really damn hard to think of anything else other than how you make the present work, if you're struggling to make enough money to put food on the table. So those are all these little, I think, like secrets about what's going on inside our mind that we can uncover in the data. Shankar Vedantam: What's fascinating about that, of course, is that most of us are not thinking, are my posts describing something that's happening in the present or something that is about the future, for example, but that difference, in fact, can reveal something about us. Sandra Matz: Yeah, and I think that's the distinction between identity claims and behavioral residue that I think is so interesting, right? So again, you might post about this luxury vacation, and it's a very clear signal to the world that you're having a great time, and you can afford going on this vacation. But then all of these more subtle ones where you talk about yourself, you're more focused on the present, that's certainly something that we don't necessarily intend to reveal. Shankar Vedantam: You used an interesting phrase just now, behavioral residue. What do you mean by that, Sandra? Sandra Matz: Yeah, so behavioral residue are all of the traces that we essentially inadvertently leave as we go about our life. In the offline context, you could imagine, again, that's like the bin overflowing, that's your socks not being organized, that's the bed not being made. And in the digital world, it's all of the traces that we generate without really thinking about it. So that could be your smartphone, for example, captures your GPS records pretty much continuously, 24-7. And you're not intentionally sitting down to create a record of where you went and what you did there, but still those traces exist. Shankar Vedantam: Let's look at some of the ways in which these behavioral residues can tell us important things about our lives and the lives of other people. The researcher Yo-Yo Wu once looked at what you could learn about a person from their Facebook likes. Sandra Matz: Yeah. And that was really, so the research by Yo-Yo Wu, I would say was one of the pivotal studies in this field, because it showed just how accurate the predictions that we can make about someone's psychology really are based on relatively little data. And so she was studying the Facebook pages that people follow. So let's say CNN has a Facebook page, you can like it. And what she showed is that just by looking at your Facebook pages, an algorithm can actually predict your personality more accurately than our coworkers could, than our friends could, than our family members could. And mind you, those are people who know you pretty well, right? Those are your parents, those are your siblings, those are your kids. They've spent a substantial amount of time with you. And it was slightly inferior to the judgments and the predictions of your significant other. Now, this was a study that was done in 2015. It was only based on Facebook likes. So you could imagine that if we get access to all of your digital traces and apply slightly more sophisticated machine learning, that we could probably outperform even your significant other. Shankar Vedantam: So the study found that after observing just 10 likes from someone's Facebook profile, the model was able to judge a user's personality better than their work colleagues. After 65 likes, it knew users better than someone's friends, and after 120 likes, better than family members. I mean, that's astonishing, Sandra. Sandra Matz: Yeah. And I think what is astonishing to me, and I think a point that is important, those models aren't perfect. Right? So I think any prediction always has a certain amount of error. And what we're talking about are averages. On averages, these models are really accurate, as you just said, with a comparison. However, we still make mistakes at the individual level. So one of the things, when we kind of make these comparisons and predictions that I want to highlight is that don't take it as a truth. Right? It's a prediction, it's a probability. It's pretty damn accurate on average, but we're still going to make mistakes at the individual level. Shankar Vedantam: I'm also assuming that when you have intersecting lines of evidence, so this study was looking at Facebook likes, but if you were able to combine that, for example, with people's credit card purchases, if you were able to combine that with their Twitter feeds, if you were able to combine that with what they're saying about themselves, you're gradually producing a more and more accurate profile of who the person is. Sandra Matz: Yeah, I think of it as like this puzzle that we're putting together of a person. So you get a piece here that's their social media, and then you get another piece that's their credit card spending, and another piece that's their smartphone sensing data. And gradually, you kind of see this person behind the data emerge. And what I think is fascinating about this combining data sources is essentially that a lot of people always say when I talk about social media that, well, isn't it just like this curated identity of who we are? It's just like who we want to be. We all like machilates and amazing vacations. We're never sad. So it's just like the self-idealized version of who we really are. That's true for some of these identity claims, right? Social media. But if you wanted to, let's say you wanted to pretend that you're more organized and conscientious than you really are, maybe you can do this on Facebook for a couple of weeks. It's really, really difficult to do this across all data sources and across months and months and months. Shankar Vedantam: I'm wondering if you can talk a moment about how these, sort of in some ways, mindless algorithms are painting a picture of us that's more accurate than our friends and neighbors and coworkers. And some of that is because our friends and neighbors and coworkers are bringing, you know, their own perceptions and their own biases to the equation as they're evaluating us. Sandra Matz: Yeah, so I think part of it might be bias, right? One of the things that we're limited by as humans is we have only a sliver of experience and we have our own perspective on the world, and that's influencing every judgment that we make about other people. Now, we also have a lot less data to work with, right? If you look at the prediction of models that we build, they are looking at millions and millions and millions of data points, and all integrating them at the same time. There's just no way that we have access to millions of millions of friendships that allow us to then judge someone's personality based on their behavior. Shankar Vedantam: Yeah, I mean, in some ways, this is like Sherlock Holmes on steroids, is what these machines are doing, right? Because they're actually picking up huge amounts of data, far more than most of us are actually able to observe in the physical world. Sandra Matz: Yeah, let's imagine it's like Sherlock Holmes with a million Watsons. So even our search history, what we're looking for online, can say a lot about us. Shankar Vedantam: Talk about this, that in some ways, what we search for online can paint a very powerful picture of who we are. Sandra Matz: Yeah, so Google searches, if you think about that, Google is probably a more, like the closest confidant that we have, right? We ask Google questions that we don't even dare to ask our closest friends, our partners. So on some level, it's not surprising that whatever we search for on Google actually reveals a lot of what's going on inside. And that could be anything from mental health to truth about society that we might not want to see. So one of a close friend, Seth Stevens Davidovitz, and what he did is he looked at search data. So all of the searches that people make, and he was trying to uncover some of the relationships between what we search for and really kind of truths about society. So that could be anything from what do people search for when they search for sex? Do people look for abortions more often than we actually see in the official data? Do people search for racist jokes more often than people would admit in public? So I think Google is really the source that captures what's going on inside our mind, stuff that we don't want to share with anyone else. Shankar Vedantam: So we had Seth Stevens Davidovitz on Hidden Brain some years ago, and one of the things he mentioned was that there was this negative correlation between racist searches on the internet and the likelihood that people would vote for Barack Obama. So in both the 2008 and 2012 presidential elections, places with higher rates of Google searches using racist terms were less likely to vote for Barack Obama. Sandra Matz: Yeah, that's right. And I think again, if you look at the official polls, nobody wants to admit that. So those are correlations that you don't necessarily see up showing up in survey data, but you do see them show up in these more hidden cues. Shankar Vedantam: In another study, Sandra, you looked at the relationship between social media updates and voting, but you were not looking at explicit data, like people saying they were going to vote for a particular politician. What were you looking for and what did you find? Sandra Matz: Yeah, so this was a study that we did where we looked at what's driving populist voting. And what we were particularly interested in is affect. So to what extent is this negative affect, and not just like the more aggressive negative affect, like anger, which you oftentimes see talked about in the media, but also the more subtle ones like sadness and depression. To what extent are those emotions as they show up on social media linked to people voting for populist candidates? So one of the elections that we looked into was Brexit in the UK. So people voting to leave the European Union or the 2016 US presidential election. And what you find consistently is that in areas where there's a lot of this negative affect showing up on social media, people are also more likely to vote for these populist candidates and causes. And again, what's interesting here is that it's like the mismatched socks in the drawer, right? It's not a signal that people are actually thinking will say something about their political preferences. If I'm feeling upset or sad or my affect in general is negative, I don't think it's going to reveal something about my political preferences, but in fact, it does. Shankar Vedantam: Yeah. Sandra Matz: And you have all of these predictive models, right? So you have all of these predictive models trying to project what is the outcome of an election. None of them really consider tone or emotional valence based on social media. Shankar Vedantam: You know, I'm reminded of that analysis that found in the 2016 presidential election that Donald Trump won three quarters of all counties that had a cracker barrel restaurant, but only 22% of counties that had a Whole Foods store. Now, most people are not thinking about politics when they're shopping for groceries or dining out, but it turns out that our shopping and dining habits can reveal powerful things about us. Sandra Matz: Yeah. So sometimes I think it's like the behaviors that you show, right? Shopping and Whole Foods is probably a proxy for some of the more psychological variables. That could be anything from openness, which we know is associated with being liberal, could be associated with socioeconomic status. And the same way that negative emotions, for example, is associated oftentimes with a desire for change. And in that sense, it's not necessarily surprising that those people who feel currently bad about themselves want to vote for a candidate that promises change. Shankar Vedantam: Most of us spend a great deal of time every day in front of various devices. We scroll and tap and like and listen. We search for answers to our most personal questions and post updates to our social media feeds. When we come back, how all this data can help us improve our lives. You're listening to Hidden Brain. I'm Shankar Vedantam. This is Hidden Brain, I'm Shankar Vedantam. You wake up in the morning and reach for your phone. You open Instagram and leave a comment on a friend's vacation pictures. You sneeze and run a Google search about allergies. On the way to work, you buy a muffin at a local cafe using your credit card. Every day, we leave dozens of tiny traces of ourselves in the digital world. At Columbia University, Sandra Matz calls the accumulation of these traces our digital footprints. She is the author of Mind Masters, the data-driven science of predicting and changing human behavior. Sandra, you say that the traces we leave online not only paint a picture of who we are, they show marketers and political campaigns how to influence us. Now, we've all heard a lot about the problems of digital surveillance, but fewer people know how these tools can be used for good. Let's start with the work you've done showing how psychological targeting can help people save more money. Sandra Matz: So the idea here was that if we could make saving more appealing to people, to make it more personally relevant, could we help them put that money, the extra money to the side? So we teamed up with Save a Life, which is a fintech company in the US. They are trying to help low-income families save for a rainy day. So the people that we work with were people with very low levels of savings, so less than $100. And our goal was to get them to add an additional $100 to their savings account over the course of four weeks. So we teamed up with the creative team of Save a Life, and we essentially asked them, well, come up with saving messages that try to encourage, say, people who are very agreeable, so people who care about other people who care about their social relationships, and maybe tell them that if you manage to put some money to the side right now, this is a great way of making sure that your loved ones are protected. Now, if you're talking to someone who is much more competitive and critical, which is the other side of the same personality trait, maybe you want to highlight how just putting this money to the side gets them ahead of the game. So we kind of came up with this different type of messaging for all of the big five personality traits, and then we just sent out the messages over the course of four weeks, and we looked at how many people eventually managed to save an additional $100. Shankar Vedantam: What did you find? Sandra Matz: So what we find is that essentially if we target people with the messages that were tailored to their personality, about 11% of the entire sample managed to put $100 to the side. Now that's certainly far from perfect, but ideally we'd want this to be closer to 100%. But if you think about it, it means that someone is doubling their savings over the course of four weeks. And what's more important is that it was also much better than the existing messaging that Save A Life had been using up to this point. So they had been trying to perfect their messaging over a couple of years, and we were still 60% better than the gold standard that they were using at the time. Shankar Vedantam: So just to underscore the principle here, what you're doing is you're basically saying, we can tell what people's personalities are by the digital footprints they're leaving behind. And if we can tailor messages in some ways to match people's personalities, those messages are far more likely to break through. Sandra Matz: Exactly. And you can think of it as, essentially, this is what we do all the time, in our offline relationships. Pretty much any type of conversation that you have is, to some extent, tailored. You don't talk about the same things or in the same way to a friend or to your kid or to your boss. So we're trying to replicate this at scale and just say, okay, what is it that you might care about and how can we make saving more appealing to you? Shankar Vedantam: Our digital footprints can also reveal insights about our mental health. You and a colleague have studied whether there's a connection between depression and a person's location data. Sandra Matz: Yeah, so this is essentially research that we did with GPS records. So again, your phone tracks your GPS records pretty much 24-7. And what we were interested in is whether we could tell whether someone might be suffering from depression or not just based on these GPS records alone. Now if you look at the content of some of these traces that we observed, they actually make a lot of sense. So what we found, for example, is that if you don't leave your house as much anymore as you typically did, or there's much less physical activity, you don't travel to as many places as you used to, those are all small indicators that there might be something going on. But it's certainly not a diagnostic tool. But it means that maybe we could be raising a red flag and say, hey, it might be nothing, but why don't you check in with some support? Shankar Vedantam: And I suppose there's always going to be noise in the data. So someone may have lost their phone inside their sofa cushions. And so the phone basically sits at home for three weeks. It doesn't mean that they are depressed and they haven't left their home in three weeks. It just means that the phone was lost. But I think what you're really saying, Sandra, is that in aggregate, this data, in fact, are telling us valuable things. And at a minimum, they're basically raising a flag that warrants further investigation. Sandra Matz: Yeah. So I don't think it's a deterministic diagnostic tool, but it could be incredibly helpful for people, for example, who know already that they're suffering from depression, right? So it's like one of these mental health challenges that just pop up time and again. And it's really difficult to find your way out of the valley. So once you enter the full-fledged depression, it's really hard to come back. And so if we can get these early indicators of, well, maybe it's nothing, but here's like a warning system that might alert you to, well, again, there's like these changes in your behavior. You're deviating from your typical routine. Why don't you reach out to someone and see if there's something to it? Shankar Vedantam: I mean, this is really no different than basically saying, let me measure your resting heart rate or your cholesterol levels. And over time, if I have enough data, it might paint me a picture of saying, you know, you're heading down a bad path. You might want to change your lifestyle. Sandra Matz: Yeah. And you can do this in real time. And technically, what you could also do, if you're really thinking about this as a support system for the person is not just alert the user, but maybe I can give you the opportunity to name two people, my loved ones, someone that you want to know that you're having a hard time, even if you're not in a position to tell them. Shankar Vedantam: So our digital footprints not only reveal things about our past, they can also predict things we might do in the future. You once tried to predict dropout rates among college students by studying their digital footprints. How did you do this, Sandra? Sandra Matz: Yeah, so this is actually one of the projects that I personally care a lot about, because there's still so many students dropping out with enormous debt that they never recover. So what we were trying to do is to see if we could predict early on, once people joined university in the first semester, whether we could see if they might be struggling integrating into the system. Maybe they're not finding the information that they should be finding. Maybe they're not embedded in the cohort as much as other people, and they're somewhat on the fringes, not really connecting to the community as much. So we kind of, again, teamed up with a company called Radio Education. They had a sense of what are the activities that students attending? Are they talking to other students? Are they part of groups? Are they sending messages? Are they receiving messages? So we looked at all of these data traces, and again, once you combine all of them, you actually have a relatively decent sense of whether someone might be struggling and whether they might drop out at the end of the semester. Shankar Vedantam: And of course, when you put it this way, it seems to make sense now. If I know, for example, that a student doesn't have many friends and is not exchanging messages and in fact is a little bit isolated and is not spending time hanging out with other students, it's not unreasonable now to say maybe the student doesn't feel like he or she belongs at the university and is at higher risk of dropping out. Sandra Matz: Yeah. And for me, what I love most about this is essentially, it creates a path to help students. And at the very bare minimum, what it allows administrators to do is identify at risk students. So if you see that there's some students who have a higher likelihood of dropping out, maybe you allocate more resources to helping them. Now, for me, the even more interesting part is that we also get a sense of what is predicting dropout for each individual student. So it could be that I, for example, when I started university as a first-generation student, my problem was that I simply didn't know where all of the information was sitting. I didn't know how to get the literature. I didn't know where to search for information. And so for me, if that was the prediction that the algorithm had made, administrators could have gone in and said, here's the information that you need. You can pop it up on my app. You can send it in my email. Just make sure that I see what I need to see. Now, there could be other people who know exactly. I know that most of my friends, when I started, knew exactly what they were looking for. But some of them probably had a harder time integrating with the community and finding the friends and making these connections. So for those people, if we see that that's what's happening based on the algorithm, it's a totally different intervention. So then we're trying to see if we can get you involved in events more. Is there a way to ask other people to connect? So the moment that you understand why someone is predicted to be a dropout, you can also adjust the approaches that you use to help them. Shankar Vedantam: In other words, instead of a one size fits all approach, now you can actually say the individual person gets his or her own approach. Sandra Matz: Exactly. It's the same as targeted advertising, right? So we kind of try and figure out what each person needs at a given point in time. Same for student dropout. Shankar Vedantam: Sandra, you say that these digital tracking tools are increasingly being used not just to identify health issues, but to actually intervene. How so? Sandra Matz: Yeah, so there's really two things that the data has to offer, and I think of it as tracking and treating. So on some level, just all of the data that we generate says a lot about our physical activity, our physical health, but also about our mental health, right? Again, we talked about GPS records that say something about whether you might be suffering from depression. There's a lot that we can learn about your mental health from what you post on social media. So this is the tracking part. But then what I think is really interesting, and it's currently being developed, so I think we're really early stages, is more of the treatment part. So can I use your footprints to not only surface, let's say, the most relevant interventions to you, the same way that Amazon recommends products and the same way that Netflix recommends movies? Can actually an algorithm who knows you based on your data recommend the best treatment for you suffering from depression? Shankar Vedantam: You tell the story of a woman named Chakura Ali, who was in a car accident that left her severely injured. She spiraled into depression. Tell me her story and what happened to her. Sandra Matz: Yeah, so this is a really tragic story of a woman who got into an accident, got severely injured, lost the bakery that she was running, shows she was self-employed, which also meant that she couldn't afford a car anymore, couldn't really provide for her family. And you can imagine that all of this takes a pretty big toll on someone's mental health. Now, with no car, no money, there's no way that you can either find a therapist, let alone drive to a therapist for like your weekly session. So what she did is she started using an app that's called Wisa, which is really trying to interact with you, give you advice, ask questions about how you're feeling, gives these little prompts and little challenges. Maybe you go out to nature and maybe you try and meditate for a little bit. And I think the way that she tells the story is that it was very weird in the beginning talking to a bot about your mental health struggles, but at some point you're just right and you get used to it. And from using it once in a while, I think she started using it multiple times a day. Shankar Vedantam: What was the effect of using this bot on her mental health, Sandra? Sandra Matz: So in her case, I think it significantly improved her mental health. It certainly didn't fix all of the problems, right? And there's still a lot of effort that you have to put in as a human being, but it felt like there was a support system that she otherwise couldn't have afforded. Shankar Vedantam: And again, I don't think you're necessarily suggesting that a bot is necessarily an ideal replacement for a human therapist. But you're saying in a situation like this, where in fact the person cannot afford or cannot get to a human therapist, this would be a potential solution. Sandra Matz: Yeah. So I think if you have access to a human being, blood and flesh, who can be your therapist, that's probably preferable. However, there's this huge gap in terms of how many therapists there are and how many people are seeking therapy. So then there is a really huge need for people to get at least some support in cases where they can't get hold of a human being. Shankar Vedantam: Many people are worried that digital tracking has increased polarization. The moment you click on one video with a political theme, the algorithms quickly paint a picture of you as liberal or conservative and start feeding you more and more of the same content. In other words, digital tracking and psychological targeting can quickly leave you inside an echo chamber. You say it's at least theoretically possible to use the same tools to reduce polarization? Sandra Matz: Oh yeah, it's one of my favorite applications. But the idea here is that it actually offers this, what I think of as like a magical echo chamber swap machine. Because it's really difficult for me to figure out, well, what is the reality of, let's say, a 50-year-old guy in the middle of Ohio? I just don't have direct access, right? It's really difficult for me to step into their shoes and see what does their day-to-day look like. Same for, let's say, a single mom in the suburbs of Chicago. But Google knows, right? Google knows exactly what those people see every day when they search for something specific. Facebook knows exactly what their news feed looks like every day. So instead of keeping me in my own echo chamber and just feeding more of the stuff that I already know, they could actually allow me to hop into the echo chambers of other people. Shankar Vedantam: In other words, if I know that you are basically self-selecting into one echo chamber, you're saying, what if these platforms in some ways can encourage us to basically visit other echo chambers and in some ways broaden our worldviews? Sandra Matz: Yeah, so it could be an explorer mode, right? And the explorer mode at the very basic level could be, well, just do an echo chamber swap with someone. So maybe someone is happy to let you access their Facebook feed and you give them access to yours. At the more sophisticated level, they could build an engine that allows you to specify exactly which echo chamber you want to hop into, right? I can say, here's the demographics of the person, here's the preferences, here's the age, gender, whatever you want to see, and then you can hop into the echo chamber. Now, I don't think we're going to use it all too often, right? The argument by Google is nobody would use it because it's so comfortable in our own echo chamber. And I think that is largely true. Most of the time, we probably love to not have to go to page two of Google because we find what we want to see on page one. But I at least want to have the option, see, well, what is the search result for like immigration that someone with a totally different political ideology than me and a totally different part of the country sees that I would never otherwise get to see? Shankar Vedantam: When I'm thinking about the concerns that major platforms might have in serving up this kind of information, I'm struck by the fact that in some ways, I think Sandra, what you're talking about is the difference between the information we want and the information that we need. So the information that I want might be information that basically confirms that my pre-existing views are correct. The information that I need might in fact tell me, hey, take a look at what's happening on the other side. Sandra Matz: I think that's absolutely true. And to, in all fairness, some of it is human nature. So the reason for why these algorithms work and the reason for why companies craft them in their effort to make profits is because we love to see stuff that we believe in anyway. It's very comforting. It's very reassuring to see stuff that is aligned with our worldview. So that's why I feel like this explorer mode is just one option that allows us to at least get some collective oversight. So even if we're not using it as much, it still means that we have an option to see what's happening on the other side. Shankar Vedantam: In our companion episode on Hidden Brain+, we look at the downsides of digital surveillance. We take a closer look at the harms of tracking technologies and why the most popular intervention to protect people, giving them control over whether they attract online and whether their children attract online, may not be the best approach. Sandra Matz: It feels much more of a burden and a responsibility that we're not really equipped to take on. Shankar Vedantam: To listen, please look for the episode titled, How to Protect Yourself Online, on Hidden Brain Plus. If you're not yet signed up, please visit support.hiddenbrain.org. If you're using an Apple device, please go to apple.co-slash hiddenbrain. Sandra Matz is the author of Mind Masters, the data-driven science of predicting and changing human behavior. Sandra, thank you so much for joining me today on Hidden Brain. Sandra Matz: Thank you so much. Shankar Vedantam: Hidden Brain is produced by Hidden Brain Media. Our audio production team includes Annie Murphy-Paul, Kristen Wong, Laura Kwerel, Ryan Katz, Autumn Barnes, Andrew Chadwick, and Nick Woodbury. Tara Boyle is our executive producer. I'm Hidden Brain's executive editor. We end today with a story from our sister show, My Unsung Hero. This My Unsung Hero segment is brought to you by T-Mobile for Business. Today's story comes from Stephanie Cole. When Stephanie was a teenager, she got her very first job. It was around the winter holidays at a department store in Los Angeles. Stephanie Cole: There I was in my black skirt, my white blouse, and ready to go the first day. And I had been trained, but very, very quickly. And as is true in a department store during Christmas, it was just bustling. You know how it is at Christmas when everybody's out shopping and everybody's in a hurry, and all these people around, this woman comes up to me with, I think, a Christmas tree ornament she wanted to buy. And I freeze. I just freeze. All of a sudden, I can't remember anything. I can't remember how to run the cash register. I can't remember anything about the transactions. I am just absolutely frozen, and probably very close to tears. Just, I so wanted this to go right, and it was going so wrong. She looked at me and paused, and with such a kind expression on her face said, It's all right. Take your time. I'm not in a hurry. And that was the release. All of a sudden, I could breathe. I could wait till somebody else could help me. It was going to be okay. It made such an impression that all these years later, not only do I still remember it, but I find myself, those words coming out of my mouth on numerous, many, many occasions over the years. You know, you encounter somebody whose first day on the job, or they're just having a bad day, and things are really... You can tell they're in a bad place, and you can say, it's okay, I'm not in a hurry, take your time. And it always makes the situation better. Always, always. And so this woman, I can't really remember her face, and certainly she's probably dead by now, given how old I was and how old she was. But she gave me that gift, and without knowing she gave me that gift, and it's lasted all these years. Shankar Vedantam: Stephanie Cole is from Bainbridge Island, Washington. This segment of My Unsung Hero was brought to you by T-Mobile for Business. You can find more stories like this on the My Unsung Hero podcast or on our website, hiddenbrain.org. I'm Shankar Vedantam. See you soon.
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