{"id":11155,"date":"2018-05-15T12:02:46","date_gmt":"2018-05-15T16:02:46","guid":{"rendered":"https:\/\/engineering.jhu.edu\/magazine-archive\/?p=11155"},"modified":"2018-05-15T12:43:54","modified_gmt":"2018-05-15T16:43:54","slug":"modern-day-miners","status":"publish","type":"post","link":"https:\/\/engineering.jhu.edu\/magazine-archive\/2018\/05\/modern-day-miners\/","title":{"rendered":"Modern Day Miners"},"content":{"rendered":"\"Modern<\/a>\n

There\u2019s treasure in all those tweets and Google searches we send out into cyberspace each day, says Mark Dredze<\/strong><\/a>. He is leading high-tech tracking efforts that could yield important insights on everything from drug overdoses to suicide prevention.<\/em><\/p>\n

The internet went haywire when the news broke about Charlie Sheen on that Tuesday morning in November 2015. In the days after the actor revealed live on television\u2019s Today<\/em> show that he had HIV, the virus that causes AIDS, millions of people ventured online to share the news and see what everybody else had to say about it.<\/p>\n

For most people, mornings like that have become part and parcel of day-to-day life in the 21st century, with its celebrity-driven news cycles and ubiquitous access to social media. Facebook feeding frenzies along the lines of Sheen\u2019s announcement rise up and then fade away with increasing regularity. The phenomenon has grown only more intense now that the country has a president who sends Twitter off the rails every time he twitches his thumbs.<\/p>\n

Mark Dredze\u2019s experience of social media is different. The Whiting School of Engineering<\/a> associate professor has spent most of the past decade working at the leading edge of a fast-developing young field called social monitoring. Here, the goal is to sort through the gigantic haystack of our collective online activity in search of nuggets of scientific insight that can boost our understanding of human behavior and help inform public policy decisions.<\/p>\n

Dredze\u2019s focus is public health, so it was natural that the Sheen news would land on his to-do list. In fact, it presented an opportunity to dispel a key doubt harbored by skeptics of social monitoring. That doubt is this: Do the words people type on their keyboards bear a reliable relationship to the things that happen in their so-called real lives?<\/p>\n

\u201cEveryone in marketing bought into the idea that this connection is real 10 or 15 years ago, right? That\u2019s why we see all those ads when we\u2019re online,\u201d says Dredze, the John C. Malone Associate Professor in Computer Science and visiting professor at the Applied Physics Laboratory<\/a>. \u201cBut for a lot of people in other fields\u2014and especially in public health, it seems\u2014this can still be kind of a new idea.\u201d<\/p>\n

The primary tools of the trade in social monitoring are machine learning and natural language processing. When Dredze put them to work on search engine data from the time around Sheen\u2019s announcement, he found, as expected, that activity on the topics of HIV and AIDS had indeed skyrocketed. Interestingly, he also found that the level of that activity dwarfed numbers that had been drummed up over the years by public awareness campaigns along the lines of World AIDS Day.<\/p>\n

But it was the next step in Dredze\u2019s work that drove the extensive media coverage about his findings. Digging a little deeper into the search engine data, he found a surge of interest in information about getting tested for HIV and, especially, about home testing kits. Then he went to the companies that make those kits and asked for sales data and trend lines.<\/p>\n

\u201cLet\u2019s just say it became very clear that a lot of the people who went searching for information about getting tested for HIV also went ahead and took the next step of buying a kit,\u201d Dredze says. \u201cIt was a pretty powerful confirmation that there really is that relationship between what people do online and what they do offline.\u201d<\/p>\n

\u00a0<\/strong><\/p>\n

coming of age<\/strong><\/h2>\n

Dredze found his way to social monitoring eight years ago during a round of small talk with one of his graduate students, Michael Paul, PhD \u201915<\/strong>. The two were wrapping up a long day at a conference in Boston over dinner when Dredze started musing out loud about Twitter.<\/p>\n

Neither Dredze nor Paul had used Twitter at that point. But they had heard enough to be curious about the platform\u2019s potential as a data pool. Just four years old, Twitter had already drawn more than 30 million users. (Today, that number is more than 300 million.)<\/p>\n

More important than those numbers, however, was the way Twitter operated. On older platforms, such as Facebook and LinkedIn, users post messages that are directed inward, toward a circumscribed, semiprivate network of friends or connections. Tweets don\u2019t work that way. They mostly face outward, toward the general public, leaving pretty much everything that happens on the platform right out there in plain view. Better yet, the company makes it easy for researchers to dig into that public-facing data by offering generous access to an array of application programming interface tools.<\/p>\n

Given his interest in public health, Dredze steered the small talk over dinner in that direction: Do Twitter users talk online about their experiences with illnesses? What sorts of things do they say about their medications? Back in Baltimore a few days later, he sent an email to Paul reporting that he had run a quick query along those lines through a small sample of Twitter data and found \u201clots\u201d of tweets containing the word \u201csick.\u201d<\/p>\n

\u201cBut of course,\u201d that missive concluded, \u201cit\u2019s not such a simple problem.\u201d He urged Paul to tackle that problem in an upcoming class project.<\/p>\n

\"Mark<\/a>
As a field, social monitoring has been growing up a at a breakneck pace\u2014with Dredze at the fore.<\/figcaption><\/figure>\n

The next year, 2011, Dredze and Paul published one of the early papers in social monitoring, mapping out a basic data landscape regarding which ailments are discussed most often on Twitter \u2014the \u201cwinners\u201d included influenza, allergies, and insomnia\u2014and proposing a few ground rules for how researchers should approach them.<\/p>\n

Some other pioneering researchers around the country had started finding their way into this new territory about the same time. The field in those early years had the aura of an old-fashioned gold rush, with scientists racing up various hills of data in willy-nilly fashion and staking claims to this, that, or another vein of potentially valuable information. There was little in the way of coordination and cooperation, especially across disciplinary lines.<\/p>\n

\u201cA lot of work was coming out of the computer science community, and a lot was coming out of the public health community, but there wasn\u2019t a lot of cross-pollination,\u201d Dredze says. \u201cWhat we ended up with were computer science papers on health topics that sounded really interesting to computer scientists, but it turned out that the results weren\u2019t actually very useful in public health.\u201d<\/p>\n

It worked the other way around as well. Public health experts would discover a vein of potentially valuable data, only to find computer scientists greeting their results with skepticism centered on the way they were employing various computational tools.<\/p>\n

Dredze has shaped the course of his career to put himself in position to work effectively across the interdisciplinary boundaries of social monitoring. In addition to his main post in the Whiting School’s Department of Computer Science, he has affiliations at Johns Hopkins with the Malone Center for Engineering in Healthcare<\/a> and the Bloomberg School of Public Health\u2019s Center for Population Health Information Technology<\/a>. He has a joint appointment in the School of Medicine\u2019s Division of Health Sciences Informatics<\/a>. He also did summer-long stints working on projects at Google, Microsoft, and IBM, and spent a recent sabbatical at Bloomberg LP.<\/p>\n

Though still less than a decade old, social monitoring has been growing\u2014and growing up<\/em>\u2014at a breakneck pace. The field\u2019s coming of age is one of two primary themes in Dredze\u2019s latest project, a book titled Social Monitoring for Public Health<\/em><\/a>. Co-written with Paul (who is now an assistant professor of information science at the University of Colorado Boulder), it sorts through and synthesizes the findings of hundreds of social monitoring papers published over the last decade to present a clear picture of the current state of the science social monitoring.<\/p>\n

 <\/p>\n

charting the course<\/strong><\/h2>\n

The second theme in the book is more of a forward-looking affair. When the conversation in his tidy office on the third floor of Malone Hall takes a turn in this direction, Dredze leans in and shifts tone in a clear signal of his heightened interest.<\/p>\n

\u201cCan we make decisions about what social monitoring researchers should be focusing on and investing in over the next few years? I think we can.\u201d<\/p>\n

By a coincidence of timing, this conversation was sandwiched by a few weeks on either side between the publication dates of two papers looking at the recent drop in U.S. life expectancy rates\u2014a December 2017 report by the U.S. Centers for Disease Control and Prevention and a February 2018 paper in the British Medical Journal<\/em>. While still small, the drop is alarming because it comes on the heels of decades of steady, incremental gains\u2014gains that most other developed countries in the world are continuing to see.<\/p>\n

Taken together, the papers point to two main culprits as causes of the decline: fatal drug overdoses and suicides. That checks off two of the three boxes that make up Dredze\u2019s answer to the question of where social monitoring is poised to make a big difference in the coming years. The third is gun violence.<\/p>\n

None of these areas has been particularly well-studied so far by researchers in the field. To date, the health topic that has drawn the most interest is influenza. One reason the flu is such a popular topic is because it\u2019s a convenient subject: There is a wealth of easily accessible online conversation around it, and results can be compared readily with rock-solid CDC data based on statistical monitoring of physician visits.<\/p>\n

\"Mark<\/a>
Drug overdoses and suicides occur mostly outside of doctors’ offices, so both are public health areas where “online data have the potential to be really helpful,” notes Dredze.<\/figcaption><\/figure>\n

In Social Monitoring for Public Health<\/em>, Dredze and Paul run through a lengthy list of the successful flu surveillance models developed to date by researchers looking at Twitter, Google searches, Wikipedia traffic, or various combinations of the three. One study even managed to track flu prevalence\u2014successfully\u2014via cancellation rates on OpenTable, a platform where customers make restaurant reservations.<\/p>\n

All this work on the flu has put social monitoring in a position to help boost public health outcomes in at least three ways. First is a matter of speed. Researchers using web-based data sets can run models on a near real-time schedule, while the CDC\u2019s reporting process, which runs through physicians\u2019 offices, unfolds with a lag of two weeks.<\/p>\n

Second, Dredze and Paul expect social monitoring researchers to continue to fine-tune their ability to zoom in on so-called fine-grained locations, boosting the reliability and detail of flu data at the level of cities, towns, and even neighborhoods so that residents and health professionals alike can get a more accurate sense for when to go on heightened alert.<\/p>\n

Third, Dredze and Paul are hopeful about work under way that aims to move flu surveillance out of the real-time \u201cnowcasting\u201d mode and into accurately forecasting future trajectories.<\/p>\n

\u201cShowing where the flu is hitting today, compared with where it was two weeks ago\u2014that can be a useful thing,\u201d Paul says. \u201cBut if you can also look out there and see where it\u2019s going to be next week or next month, that\u2019s going to be even better.\u201d<\/p>\n

 <\/p>\n

daunting obstacles<\/strong><\/h2>\n

Compared with influenza, the work of social monitoring in those three areas Dredze flags as future priorities is just getting started. Looking out to the horizons of mental health, drug use, and gun violence, he sees a mix of urgent needs, daunting obstacles, and promising possibilities in every direction.<\/p>\n

A key challenge in all three areas is the absence of baseline data. There is no central database in mental health where psychologists and psychiatrists are logging basic data on patient volume and diagnoses that then get toted up and distributed in the manner of CDC flu reports. The fact that many, and perhaps even most, cases of mental illness go undiagnosed, untreated, or both complicates matters even more.<\/p>\n

The state of suicide statistics is illustrative here. The cause of 40,000 deaths a year in the United States, suicide has drawn quite a bit of attention from public health researchers in recent decades, but gaping holes remain in our basic knowledge of the phenomenon. Perhaps the most daunting: Experts can identify a very large group of people who exhibit risk factors, but they currently have no way of narrowing things down to identify who in that group will be among a comparatively small number of actual serious attempts and fatalities.<\/p>\n

Even the best available statistics in suicide seem to come with important caveats. For example, a much-publicized 2015 study that confirmed high suicide rates among recent military veterans was based on data that stretched between the years of 2001 and 2009.<\/p>\n

\u201cHow are you supposed to respond to increasing suicide rates when it takes somewhere around six, 10, or even 15 years just to confirm them in the first place?\u201d Dredze asks.<\/p>\n

Social monitoring researchers are starting the work of filling these holes. Recent studies cited in Social Monitoring for Public Health<\/em> have identified social media data sets that are predictive of national suicide numbers and have developed ways to track the prevalence of known risk factors on Twitter. In China, researchers took a retrospective look at the social media activity of 130 suicide victims in search of measurable shifts in mood and activity in the lead-up to a suicide attempt.<\/p>\n

Dredze teamed up with public health researcher John Ayers<\/strong> of the University of California, San Diego to look at online behavior<\/a> around the 2017 Netflix television drama 13 Reasons Why<\/em>. The series follows a teenager as he hunts through the remnants of a friend\u2019s life in search of the reason she killed herself. Dredze was looking in particular for evidence of the Werther effect, a phenomenon named after an 18th-century European novel that became a pop culture phenomenon in its day and allegedly sparked a \u201ccontagion\u201d of suicides among readers.<\/p>\n

\u201cWe have decades of research that tells us that if you show someone\u2019s suicide, it leads to increased suicide,\u201d Dredze says. \u201cStill, there was a debate back and forth about whether the show was having a positive influence or negative influence\u2014that\u2019s where our research steps in.\u201d<\/p>\n

The work showed sharp increases in searches for suicide topics\u2014a jump of 34 percent on \u201cteen suicide\u201d and, alarmingly, 26 percent on \u201chow to commit suicide.\u201d (Previous social monitoring studies, Dredze notes, have demonstrated a link between increased search activity in these areas and increased suicide rates.) The news was not all bad, however: Searches for \u201csuicide hotline\u201d and \u201csuicide prevention\u201d rose by percentages that were nearly as large, indicating broad awareness about the availability of resources to help potential victims.<\/p>\n

The other primary culprit singled out in those reports on declining U.S. life expectancy is drug overdoses. Fatal opioid overdoses, in particular, have jumped in recent years by annual percentage rates in the double digits.<\/p>\n

\u201cThis is an all-hands-on-deck situation for public health professionals,\u201d Dredze says. \u201cAnd like with suicide, it\u2019s another situation where it\u2019s mostly happening outside of doctors\u2019 offices, so this is also an area where online data have the potential to be really helpful.\u201d<\/p>\n

Paul points toward Reddit as one rich vein of data in this area, as users under cloak of anonymity feel freer to share their drug experiences. Paul is currently tracking Reddit conversations about a new product: highly concentrated marijuana oils. Drug users posting to Reddit have a tradition of informing readers how intoxicated they are by gauging highness on a scale of one to 10 in brackets at the end of posts, so Paul is looking to use that strange bit of social media etiquette to get an idea of the various potency levels of different oils.<\/p>\n

\u201cIf you want to learn about something new going on with drugs, the internet is the place to be,\u201d Paul says. \u201cIt\u2019s where people go to have these conversations.\u201d<\/p>\n

The dearth of reliable data goes even deeper in Dredze\u2019s third priority area, gun violence. Here, the situation is aggravated by a highly charged political environment that leaves politicians and bureaucrats skittish when it comes to funding even the most basic sorts of research. CDC health surveys, for instance, ask a series of questions about mental illness\u2014whether respondents feel depressed, anxious, or suicidal\u2014but nothing about guns.<\/p>\n

\u201cWhen it comes to really obvious questions around guns, we don\u2019t have any answers yet,\u201d Dredze says. \u201cHow many people use gun locks? How many people store their weapons in safes?\u201d<\/p>\n

Social monitoring is the fastest and cheapest way to start closing this data gap, Dredze adds. In recent years, researchers have used online news sources to develop a new database of gun violence incidents, gauged Twitter when it comes to predicting the outcome of polling questions on guns, and examined social media and search engine data to learn about public reactions in the wake of mass shootings.<\/p>\n

\u201cIf I\u2019m a scientist and I want to come up with policies that reduce gun violence, the place I start is the data\u2014what do they say?\u201d Dredze says. \u201cThey don\u2019t say much of anything right now because there are very few data. This is definitely another area where I think we can do a lot of good work.\u201d<\/p>\n

\u00a0<\/strong><\/p>\n

#Ethical Issues<\/strong><\/h2>\n

Social monitoring research involves constant interplay among three different fields: computer science, public health, and social media businesses. Each has developed its own sets of rules and limitations when it comes to conducting research.<\/p>\n

Consider the case of a controversial 2014 study in which Facebook worked in tandem with academic researchers to measure whether the emotional tone of posts in a news feed can affect the mood of users. The project involved the active manipulation of news feed content to see how it altered people\u2019s emotional state.<\/p>\n

In academic medicine, any work along those lines would require approval from an institutional review board, not to mention explicit consent from study participants on a level much more specific than the all encompassing \u201cterms of service\u201d agreements that govern research as social media companies.<\/p>\n

\u201cWe have to be very careful in our interactions here,\u201d Dredze says. \u201cWe have to really think things through and anticipate what might be coming up down the road.\u201d<\/p>\n

It\u2019s one thing if researchers can look over a public Twitter feed and identify someone as a fan of a certain band, he says, but it\u2019s quite another when they can identify more sensitive information, such as someone being at risk for an anxiety disorder.<\/p>\n

\u201cOnce we are doing that type of analysis, people have a right to become very wary about how this technology is being used,\u201d Dredze says. \u201cWe need to be thinking through these ethical issues now and talking about how we\u2019re maintaining data, sharing data, and using data.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"

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