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Using machine learning to track the pandemic’s impact on mental health
Textual analysis of social media posts finds users' anxiety and suicide-risk levels are rising, among other negative trends.
A team of MIT and Harvard University researchers has shown that they can measure those effects by analyzing the language that people use to express their anxiety online.
Using machine learning to analyze the text of more than 800,000 Reddit posts, the researchers were able to identify changes in the tone and content of language that people used as the first wave of the Covid-19 pandemic progressed, from January to April of 2020. Their analysis revealed several key changes in conversations about mental health, including an overall increase in discussion about anxiety and suicide.
"We found that there were these natural clusters that emerged related to suicidality and loneliness, and the amount of posts in these clusters more than doubled during the pandemic as compared to the same months of the preceding year, which is a grave concern," says Daniel Low, a graduate student in the Program in Speech and Hearing Bioscience and Technology at Harvard and MIT and the lead author of the study.
The analysis also revealed varying impacts on people who already suffer from different types of mental illness. The findings could help psychiatrists, or potentially moderators of the Reddit forums that were studied, to better identify and help people whose mental health is suffering, the researchers say.
"When the mental health needs of so many in our society are inadequately met, even at baseline, we wanted to bring attention to the ways that many people are suffering during this time, in order to amplify and inform the allocation of resources to support them," says Laurie Rumker, a graduate student in the Bioinformatics and Integrative Genomics PhD Program at Harvard and one of the authors of the study.
Satrajit Ghosh, a principal research scientist at MIT's McGovern Institute for Brain Research, is the senior author of the study, which appears in the Journal of Medical Internet Research. Other authors of the paper include Tanya Talkar, a graduate student in the Program in Speech and Hearing Bioscience and Technology at Harvard and MIT; John Torous, director of the digital psychiatry division at Beth Israel Deaconess Medical Center; and Guillermo Cecchi, a principal research staff member at the IBM Thomas J. Watson Research Center.
A wave of anxiety
The new study grew out of the MIT class 6.897/HST.956 (Machine Learning for Healthcare), in MIT's Department of Electrical Engineering and Computer Science. Low, Rumker, and Talkar, who were all taking the course last spring, had done some previous research on using machine learning to detect mental health disorders based on how people speak and what they say. After the Covid-19 pandemic began, they decided to focus their class project on analyzing Reddit forums devoted to different types of mental illness.
"When Covid hit, we were all curious whether it was affecting certain communities more than others," Low says. "Reddit gives us the opportunity to look at all these subreddits that are specialized support groups. It's a really unique opportunity to see how these different communities were affected differently as the wave was happening, in real-time."
The researchers analyzed posts from 15 subreddit groups devoted to a variety of mental illnesses, including schizophrenia, depression, and bipolar disorder. They also included a handful of groups devoted to topics not specifically related to mental health, such as personal finance, fitness, and parenting.
Using several types of natural language processing algorithms, the researchers measured the frequency of words associated with topics such as anxiety, death, isolation, and substance abuse, and grouped posts together based on similarities in the language used. These approaches allowed the researchers to identify similarities between each group's posts after the onset of the pandemic, as well as distinctive differences between groups.
The researchers found that while people in most of the support groups began posting about Covid-19 in March, the group devoted to health anxiety started much earlier, in January. However, as the pandemic progressed, the other mental health groups began to closely resemble the health anxiety group, in terms of the language that was most often used. At the same time, the group devoted to personal finance showed the most negative semantic change from January to April 2020, and significantly increased the use of words related to economic stress and negative sentiment.
They also discovered that the mental health groups affected the most negatively early in the pandemic were those related to ADHD and eating disorders. The researchers hypothesize that without their usual social support systems in place, due to lockdowns, people suffering from those disorders found it much more difficult to manage their conditions. In those groups, the researchers found posts about hyperfocusing on the news and relapsing back into anorexia-type behaviors since meals were not being monitored by others due to quarantine.
Using another algorithm, the researchers grouped posts into clusters such as loneliness or substance use, and then tracked how those groups changed as the pandemic progressed. Posts related to suicide more than doubled from pre-pandemic levels, and the groups that became significantly associated with the suicidality cluster during the pandemic were the support groups for borderline personality disorder and post-traumatic stress disorder.
The researchers also found the introduction of new topics specifically seeking mental health help or social interaction. "The topics within these subreddit support groups were shifting a bit, as people were trying to adapt to a new life and focus on how they can go about getting more help if needed," Talkar says.
While the authors emphasize that they cannot implicate the pandemic as the sole cause of the observed linguistic changes, they note that there was much more significant change during the period from January to April in 2020 than in the same months in 2019 and 2018, indicating the changes cannot be explained by normal annual trends.
Mental health resources
This type of analysis could help mental health care providers identify segments of the population that are most vulnerable to declines in mental health caused by not only the Covid-19 pandemic but other mental health stressors such as controversial elections or natural disasters, the researchers say.
Additionally, if applied to Reddit or other social media posts in real-time, this analysis could be used to offer users additional resources, such as guidance to a different support group, information on how to find mental health treatment, or the number for a suicide hotline.
"Reddit is a very valuable source of support for a lot of people who are suffering from mental health challenges, many of whom may not have formal access to other kinds of mental health support, so there are implications of this work for ways that support within Reddit could be provided," Rumker says.
The researchers now plan to apply this approach to study whether posts on Reddit and other social media sites can be used to detect mental health disorders. One current project involves screening posts in a social media site for veterans for suicide risk and post-traumatic stress disorder.
The research was funded by the National Institutes of Health and the McGovern Institute.
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Inventions with revolutionary potential made by a mysterious aerospace engineer for the U.S. Navy come to light.
- U.S. Navy holds patents for enigmatic inventions by aerospace engineer Dr. Salvatore Pais.
- Pais came up with technology that can "engineer" reality, devising an ultrafast craft, a fusion reactor, and more.
- While mostly theoretical at this point, the inventions could transform energy, space, and military sectors.
The U.S. Navy controls patents for some futuristic and outlandish technologies, some of which, dubbed "the UFO patents," came to life recently. Of particular note are inventions by the somewhat mysterious Dr. Salvatore Cezar Pais, whose tech claims to be able to "engineer reality." His slate of highly-ambitious, borderline sci-fi designs meant for use by the U.S. government range from gravitational wave generators and compact fusion reactors to next-gen hybrid aerospace-underwater crafts with revolutionary propulsion systems, and beyond.
Of course, the existence of patents does not mean these technologies have actually been created, but there is evidence that some demonstrations of operability have been successfully carried out. As investigated and reported by The War Zone, a possible reason why some of the patents may have been taken on by the Navy is that the Chinese military may also be developing similar advanced gadgets.
Among Dr. Pais's patents are designs, approved in 2018, for an aerospace-underwater craft of incredible speed and maneuverability. This cone-shaped vehicle can potentially fly just as well anywhere it may be, whether air, water or space, without leaving any heat signatures. It can achieve this by creating a quantum vacuum around itself with a very dense polarized energy field. This vacuum would allow it to repel any molecule the craft comes in contact with, no matter the medium. Manipulating "quantum field fluctuations in the local vacuum energy state," would help reduce the craft's inertia. The polarized vacuum would dramatically decrease any elemental resistance and lead to "extreme speeds," claims the paper.
Not only that, if the vacuum-creating technology can be engineered, we'd also be able to "engineer the fabric of our reality at the most fundamental level," states the patent. This would lead to major advancements in aerospace propulsion and generating power. Not to mention other reality-changing outcomes that come to mind.
Among Pais's other patents are inventions that stem from similar thinking, outlining pieces of technology necessary to make his creations come to fruition. His paper presented in 2019, titled "Room Temperature Superconducting System for Use on a Hybrid Aerospace Undersea Craft," proposes a system that can achieve superconductivity at room temperatures. This would become "a highly disruptive technology, capable of a total paradigm change in Science and Technology," conveys Pais.
High frequency gravitational wave generator.
Credit: Dr. Salvatore Pais
Another invention devised by Pais is an electromagnetic field generator that could generate "an impenetrable defensive shield to sea and land as well as space-based military and civilian assets." This shield could protect from threats like anti-ship ballistic missiles, cruise missiles that evade radar, coronal mass ejections, military satellites, and even asteroids.
Dr. Pais's ideas center around the phenomenon he dubbed "The Pais Effect". He referred to it in his writings as the "controlled motion of electrically charged matter (from solid to plasma) via accelerated spin and/or accelerated vibration under rapid (yet smooth) acceleration-deceleration-acceleration transients." In less jargon-heavy terms, Pais claims to have figured out how to spin electromagnetic fields in order to contain a fusion reaction – an accomplishment that would lead to a tremendous change in power consumption and an abundance of energy.
According to his bio in a recently published paper on a new Plasma Compression Fusion Device, which could transform energy production, Dr. Pais is a mechanical and aerospace engineer working at the Naval Air Warfare Center Aircraft Division (NAWCAD), which is headquartered in Patuxent River, Maryland. Holding a Ph.D. from Case Western Reserve University in Cleveland, Ohio, Pais was a NASA Research Fellow and worked with Northrop Grumman Aerospace Systems. His current Department of Defense work involves his "advanced knowledge of theory, analysis, and modern experimental and computational methods in aerodynamics, along with an understanding of air-vehicle and missile design, especially in the domain of hypersonic power plant and vehicle design." He also has expert knowledge of electrooptics, emerging quantum technologies (laser power generation in particular), high-energy electromagnetic field generation, and the "breakthrough field of room temperature superconductivity, as related to advanced field propulsion."
Suffice it to say, with such a list of research credentials that would make Nikola Tesla proud, Dr. Pais seems well-positioned to carry out groundbreaking work.
A craft using an inertial mass reduction device.
Credit: Salvatore Pais
The patents won't necessarily lead to these technologies ever seeing the light of day. The research has its share of detractors and nonbelievers among other scientists, who think the amount of energy required for the fields described by Pais and his ideas on electromagnetic propulsions are well beyond the scope of current tech and are nearly impossible. Yet investigators at The War Zone found comments from Navy officials that indicate the inventions are being looked at seriously enough, and some tests are taking place.
If you'd like to read through Pais's patents yourself, check them out here.
Laser Augmented Turbojet Propulsion System
Credit: Dr. Salvatore Pais
- The history of AI shows boom periods (AI summers) followed by busts (AI winters).
- The cyclical nature of AI funding is due to hype and promises not fulfilling expectations.
- This time, we might enter something resembling an AI autumn rather than an AI winter, but fundamental questions remain if true AI is even possible.
The dream of building a machine that can think like a human stretches back to the origins of electronic computers. But ever since research into artificial intelligence (AI) began in earnest after World War II, the field has gone through a series of boom and bust cycles called "AI summers" and "AI winters."
Each cycle begins with optimistic claims that a fully, generally intelligent machine is just a decade or so away. Funding pours in and progress seems swift. Then, a decade or so later, progress stalls and funding dries up. Over the last ten years, we've clearly been in an AI summer as vast improvements in computing power and new techniques like deep learning have led to remarkable advances. But now, as we enter the third decade of the 21st century, some who follow AI feel the cold winds at their back leading them to ask, "Is Winter Coming?" If so, what went wrong this time?
How to build an A.I. brain that can conceive of itself | Joscha Bach | Big Think www.youtube.com
A brief history of AI
To see if the winds of winter are really coming for AI, it is useful to look at the field's history. The first real summer can be pegged to 1956 and the famous Dartmouth University Workshop where one of the field's pioneers, John McCarthy, coined the term "artificial intelligence." The conference was attended by scientists like Marvin Minsky and H. A. Simon, whose names would go on to become synonymous with the field. For those researchers, the task ahead was clear: capture the processes of human reasoning through the manipulation of symbolic systems (i.e., computer programs).
Unless we are talking about very specific tasks, any 6-year-old is infinitely more flexible and general in his or her intelligence than the "smartest" Amazon robot.
Throughout the 1960s, progress seemed to come swiftly as researchers developed computer systems that could play chess, deduce mathematical theorems, and even engage in simple discussions with a person. Government funding flowed generously. Optimism was so high that, in 1970, Minsky famously proclaimed, "In three to eight years we will have a machine with the general intelligence of a human being."
By the mid 1970s, however, it was clear that Minsky's optimism was unwarranted. Progress stalled as many of the innovations of the previous decade proved too narrow in their applicability, seeming more like toys than steps toward a general version of artificial intelligence. Funding dried up so completely that researchers soon took pains not to refer to their work as AI, as the term carried a stink that killed proposals.
The cycle repeated itself in the 1980s with the rise of expert systems and the renewed interest in what we now call neural networks (i.e., programs based on connectivity architectures that mimic neurons in the brain). Once again, there was wild optimism and big increases in funding. What was novel in this cycle was the addition of significant private funding as more companies began to rely on computers as essential components of their business. But, once again, the big promises were never realized, and funding dried up again.
AI: Hype vs. reality
The AI summer we're currently experiencing began sometime in the first decade of the new millennium. Vast increases in both computing speed and storage ushered in the era of deep learning and big data. Deep learning methods use stacked layers of neural networks that pass information to each other to solve complex problems like facial recognition. Big data provides these systems with vast oceans of examples (like images of faces) to train on. The applications of this progress are all around us: Google Maps give you near-perfect directions; you can talk with Siri anytime you want; IBM's Deep Think computer beat Jeopardy's greatest human champions.
In response, the hype rose again. True AI, we were told, must be just around the corner. In 2015, for example, The Guardian reported that self-driving cars, the killer app of modern AI, was close at hand. Readers were told, "By 2020 you will become a permanent backseat driver." And just two years ago, Elon Musk claimed that by 2020 "we'd have over a million cars with full self-driving software."
The general intelligence — i.e., the understanding — we humans exhibit may be inseparable from our experiencing. If that's true, then our physical embodiment, enmeshed in a context-rich world, may be difficult if not impossible to capture in symbolic processing systems.
By now, it's obvious that a world of fully self-driving cars is still years away. Likewise, in spite of the remarkable progress we've made in machine learning, we're still far from creating systems that possess general intelligence. The emphasis is on the term general because that's what AI really has been promising all these years: a machine that's flexible in dealing with any situation as it comes up. Instead, what researchers have found is that, despite all their remarkable progress, the systems they've built remain brittle, which is a technical term meaning "they do very wrong things when given unexpected inputs." Try asking Siri to find "restaurants that aren't McDonald's." You won't like the results.
Unless we are talking about very specific tasks, any 6-year-old is infinitely more flexible and general in his or her intelligence than the "smartest" Amazon robot.
Even more important is the sense that, as remarkable as they are, none of the systems we've built understand anything about what they are doing. As philosopher Alva Noe said of Deep Think's famous Jeopardy! victory, "Watson answered no questions. It participated in no competition. It didn't do anything. All the doing was on our side. We played Jeapordy! with Watson." Considering this fact, some researchers claim that the general intelligence — i.e., the understanding — we humans exhibit may be inseparable from our experiencing. If that's true, then our physical embodiment, enmeshed in a context-rich world, may be difficult if not impossible to capture in symbolic processing systems.
Not the (AI) winter of our discontent
Thus, talk a of a new AI winter is popping up again. Given the importance of deep learning and big data in technology, it's hard to imagine funding for these domains drying up any time soon. What we may be seeing, however, is a kind of AI autumn when researchers wisely recalibrate their expectations and perhaps rethink their perspectives.
A new study explores how investors' behavior is affected by participating in online communities, like Reddit's WallStreetBets.
- The study found evidence that "hype" over assets is psychologically contagious among investors in online communities.
- This hype is self-perpetuating: A small group of investors hypes an asset, bringing in new investors, until growth becomes unsteady and a price crash ensues.
- The researchers suggested that these new kinds of self-organized, social media-driven investment behaviors are unlikely to disappear anytime soon.
Social media has reshaped human behavior in ways we're only starting to understand. The proliferation of online communities has helped spawn novel strategies for promoting political causes, conducting business, finding sex and love, and transforming culture.
Could online communities also transform behavior in the financial world?
That's one of the key questions explored in a new study published on the preprint server arXiv. Titled "Reddit's self-organised bull runs: Social contagion and asset prices," the study used discussion data from the subreddit WallStreetBets to analyze relationships between the price of stocks and "hype" among online retail investors.
Hype is nothing new in the investing world. But the researchers noted that there seems to be something novel about the short squeeze of GameStop's stock in January, when the price of the stock rose tenfold, thanks largely to self-organized retail investors from WallStreetBets.
"As academics and regulators alike grapple with the implications, many wonder whether large-scale coordination among retail investors is the new 'modus operandi,' or a one-off fluke," the researchers wrote. "We argue that this is a new manifestation of a well-established global phenomenon."
To better understand how online hype is associated with stock prices, the researchers focused on two social components of hype: contagion and consensus. Contagion refers to investors spreading interest in an asset among each other, while consensus refers to their ability to agree on whether to buy or sell an asset.
The analysis found empirical evidence that both contagion and consensus emerge in online communities like WallStreetBets. In other words, investors spread sentiments about future stock performance to other investors, and then they cohere around investment strategies.
Popularity over fundamentals
The findings suggest that an asset's popularity, not its fundamentals, is paramount to many investors.
"Our results consistently show that investors become interested in discussing an asset, not because of fundamentals, but because other users discuss it," the researchers wrote. "Subsequently, this paper tests whether an individual's sentiment about future asset performance [is] affected by those of others. We find that this is the case: people look to their peers to form an opinion about an asset's potential."
To find evidence for social contagion among online investors, the researchers compiled a large dataset of posts and comments submitted to WallStreetBets. The goal was to analyze whether investors' past comments or posts about a given stock, such as Tesla, had a predictable effect on future discussions of that asset within WallStreetBets.
After conducting a regression analysis, the results suggest that hype is socially contagious and cyclical. The cycle usually plays out like this: A small group of investors hypes an asset. This attracts a larger group of investors who join the discussions.
But eventually, too many investors have joined the discussion, and fewer new investors are buying into the hype. As investors lose interest, they spend less time discussing (or "spreading") the asset on the forum, and they turn to new opportunities. The process is similar to a virus: As enough people become infected, they reach herd immunity, and the virus (hype) dies out.
So, does this process affect the stock price, and if so, how? The researchers said it was difficult to establish causality between hype and actual market activity. After all, they didn't have access to the trading records of subscribers to WallStreetBets.
But their model did show that activity on WallStreetBets was able to explain "significant variance" in trading volumes for the most-discussed assets on the forum. This suggests that when social contagion is strong for a given asset, consensus is strong too.
On the stock chart, consensus may start off bullish (or positively): As hype spreads, there's a slow, steady run-up in price. But the growth eventually becomes unstable and is followed by a crash and a period of volatility.
"The price crash stems from panic selling, as investors turn nervous in the face of volatility," the researchers wrote.
Bad news spreads faster than good news
Interestingly, the analysis found that bearish (or negative) sentiments were significantly more contagious on WallStreetBets.
"The data demonstrates that authors who previously commented on a bearish post are 47.7% more likely to express bearish over neutral sentiments, and 18.1% less likely to express bullish sentiments over neutral sentiments. Similarly, but less markedly, authors who previously commented on at least one bullish submission are 9.4% more likely to write a bullish submission, yet 11.3% less likely to write a bearish one."
The researchers said that the changing investing climate and widely available online data offers "promising opportunities for future research."
"As social media galvanizes a larger pool of retail investors with the potential for exciting stock market gambles, it is crucial to understand how social dynamics can impact asset prices," the researchers wrote. "With the first publicly acclaimed victory of Main Street over Wall Street, in the form of the GameStop short squeeze, it is unlikely that socially-driven asset volatility will simply disappear."
A 19th-century surveying mistake kept lumberjacks away from what is now Minnesota's largest patch of old-growth trees.