scientificamerican--2020-11-19--Information Overload Helps Fake News Spread, and Social Media Knows It

Consider Andy , who is worried about contracting COVID-19 .
Unable to read all the articles he sees on it , he relies on trusted friends for tips .
When one opines on Facebook that pandemic fears are overblown , Andy dismisses the idea at first .
But then the hotel where he works closes its doors , and with his job at risk , Andy starts wondering how serious the threat from the new virus really is .
No one he knows has died , after all .
A colleague posts an article about the COVID “ scare ” @ @ @ @ @ @ @ collusion with corrupt politicians , which jibes with Andy 's distrust of government .
His Web search quickly takes him to articles claiming that COVID-19 is no worse than the flu .
Andy joins an online group of people who have been or fear being laid off and soon finds himself asking , like many of them , “ What pandemic ?
” When he learns that several of his new friends are planning to attend a rally demanding an end to lockdowns , he decides to join them .
Almost no one @ @ @ @ @ @ @ , wears a mask .
When his sister asks about the rally , Andy shares the conviction that has now become part of his identity : COVID is a hoax .
This example illustrates a minefield of cognitive biases .
We prefer information from people we trust , our in-group .
We pay attention to and are more likely to share information about risks—for Andy , the risk of losing his job .
We search for and remember things that fit well with what we already know and understand .
These biases are @ @ @ @ @ @ @ for tens of thousands of years , they served us well .
People who behaved in accordance with them—for example , by staying away from the overgrown pond bank where someone said there was a viper—were more likely to survive than those who did not .
Modern technologies are amplifying these biases in harmful ways , however .
Search engines direct Andy to sites that inflame his suspicions , and social media connects him with like-minded people , feeding his fears .
Making matters worse , bots—automated social media accounts that impersonate humans—enable @ @ @ @ @ @ @ of his vulnerabilities .
Compounding the problem is the proliferation of online information .
Viewing and producing blogs , videos , tweets and other units of information called memes has become so cheap and easy that the information marketplace is inundated .
Unable to process all this material , we let our cognitive biases decide what we should pay attention to .
These mental shortcuts influence which information we search for , comprehend , remember and repeat to a harmful extent .
The need to understand these cognitive vulnerabilities and how algorithms use @ @ @ @ @ @ @ At the University of Warwick in England and at Indiana University Bloomington 's Observatory on Social Media ( OSoMe , pronounced “ awesome ” ) , our teams are using cognitive experiments , simulations , data mining and artificial intelligence to comprehend the cognitive vulnerabilities of social media users .
Insights from psychological studies on the evolution of information conducted at Warwick inform the computer models developed at Indiana , and vice versa .
We are also developing analytical and machine-learning aids to fight social media manipulation .
Some of these tools are @ @ @ @ @ @ @ organizations and individuals to detect inauthentic actors , map the spread of false narratives and foster news literacy .
The glut of information has generated intense competition for people 's attention .
As Nobel Prize–winning economist and psychologist Herbert A. Simon noted , “ What information consumes is rather obvious : it consumes the attention of its recipients.
” One of the first consequences of the so-called attention economy is the loss of high-quality information .
The OSoMe team demonstrated this result with a set of simple simulations .
It represented users of @ @ @ @ @ @ @ agents , as nodes in a network of online acquaintances .
At each time step in the simulation , an agent may either create a meme or reshare one that he or she sees in a news feed .
To mimic limited attention , agents are allowed to view only a certain number of items near the top of their news feeds .
Running this simulation over many time steps , Lilian Weng of OSoMe found that as agents ' attention became increasingly limited , the propagation of memes came to reflect the @ @ @ @ @ @ @ the probability that a meme would be shared a given number of times was roughly an inverse power of that number .
For example , the likelihood of a meme being shared three times was approximately nine times less than that of its being shared once .
This winner-take-all popularity pattern of memes , in which most are barely noticed while a few spread widely , could not be explained by some of them being more catchy or somehow more valuable : the memes in this simulated world had no intrinsic quality .
@ @ @ @ @ @ @ of information proliferation in a social network of agents with limited attention .
Even when agents preferentially shared memes of higher quality , researcher Xiaoyan Qiu , then at OSoMe , observed little improvement in the overall quality of those shared the most .
Our models revealed that even when we want to see and share high-quality information , our inability to view everything in our news feeds inevitably leads us to share things that are partly or completely untrue .
Cognitive biases greatly worsen the problem .
In a set of groundbreaking @ @ @ @ @ @ @ told volunteers a Native American legend about a young man who hears war cries and , pursuing them , enters a dreamlike battle that eventually leads to his real death .
Bartlett asked the volunteers , who were non-Native , to recall the rather confusing story at increasing intervals , from minutes to years later .
He found that as time passed , the rememberers tended to distort the tale 's culturally unfamiliar parts such that they were either lost to memory or transformed into more familiar things .
We now know that @ @ @ @ @ @ @ : they adjust our understanding of new information so that it fits in with what we already know .
One consequence of this so-called confirmation bias is that people often seek out , recall and understand information that best confirms what they already believe .
This tendency is extremely difficult to correct .
Experiments consistently show that even when people encounter balanced information containing views from differing perspectives , they tend to find supporting evidence for what they already believe .
And when people with divergent beliefs about emotionally charged issues such as @ @ @ @ @ @ @ on these topics , they become even more committed to their original positions .
Making matters worse , search engines and social media platforms provide personalized recommendations based on the vast amounts of data they have about users ' past preferences .
They prioritize information in our feeds that we are most likely to agree with—no matter how fringe—and shield us from information that might change our minds .
This makes us easy targets for polarization .
Nir Grinberg and his co-workers at Northeastern University recently showed that conservatives in the U.S. are @ @ @ @ @ @ @ own analysis of consumption of low-quality information on Twitter shows that the vulnerability applies to both sides of the political spectrum , and no one can fully avoid it .
Even our ability to detect online manipulation is affected by our political bias , though not symmetrically : Republican users are more likely to mistake bots promoting conservative ideas for humans , whereas Democrats are more likely to mistake conservative human users for bots .
In New York City in August 2019 , people began running away from what sounded like gunshots .
@ @ @ @ @ @ @ Shooter !
” Only later did they learn that the blasts came from a backfiring motorcycle .
In such a situation , it may pay to run first and ask questions later .
In the absence of clear signals , our brains use information about the crowd to infer appropriate actions , similar to the behavior of schooling fish and flocking birds .
Such social conformity is pervasive .
In a fascinating 2006 study involving 14,000 Web-based volunteers , Matthew Salganik , then at Columbia University , and his colleagues found that when @ @ @ @ @ @ @ downloading , they end up downloading similar songs .
Moreover , when people were isolated into “ social ” groups , in which they could see the preferences of others in their circle but had no information about outsiders , the choices of individual groups rapidly diverged .
But the preferences of “ nonsocial ” groups , where no one knew about others ' choices , stayed relatively stable .
In other words , social groups create a pressure toward conformity so powerful that it can overcome individual preferences , and by amplifying @ @ @ @ @ @ @ segregated groups to diverge to extremes .
Social media follows a similar dynamic .
We confuse popularity with quality and end up copying the behavior we observe .
Experiments on Twitter by Bjarke Mønsted and his colleagues at the Technical University of Denmark and the University of Southern California indicate that information is transmitted via “ complex contagion ” : when we are repeatedly exposed to an idea , typically from many sources , we are more likely to adopt and reshare it .
This social bias is further amplified by what psychologists @ @ @ @ @ @ @ : when people are repeatedly exposed to the same stimuli , such as certain faces , they grow to like those stimuli more than those they have encountered less often .
Such biases translate into an irresistible urge to pay attention to information that is going viral—if everybody else is talking about it , it must be important .
In addition to showing us items that conform with our views , social media platforms such as Facebook , Twitter , YouTube and Instagram place popular content at the top of our screens and @ @ @ @ @ @ @ and shared something .
Few of us realize that these cues do not provide independent assessments of quality .
In fact , programmers who design the algorithms for ranking memes on social media assume that the “ wisdom of crowds ” will quickly identify high-quality items ; they use popularity as a proxy for quality .
Our analysis of vast amounts of anonymous data about clicks shows that all platforms—social media , search engines and news sites—preferentially serve up information from a narrow subset of popular sources .
To understand why , we @ @ @ @ @ @ @ and popularity in their rankings .
In this model , agents with limited attention—those who see only a given number of items at the top of their news feeds—are also more likely to click on memes ranked higher by the platform .
Each item has intrinsic quality , as well as a level of popularity determined by how many times it has been clicked on .
Another variable tracks the extent to which the ranking relies on popularity rather than quality .
Simulations of this model reveal that such algorithmic bias typically suppresses @ @ @ @ @ @ @ absence of human bias .
Even when we want to share the best information , the algorithms end up misleading us .
Most of us do not believe we follow the herd .
But our confirmation bias leads us to follow others who are like us , a dynamic that is sometimes referred to as homophily—a tendency for like-minded people to connect with one another .
Social media amplifies homophily by allowing users to alter their social network structures through following , unfriending , and so on .
The result is that people @ @ @ @ @ @ @ increasingly misinformed communities commonly described as echo chambers .
At OSoMe , we explored the emergence of online echo chambers through another simulation , EchoDemo .
In this model , each agent has a political opinion represented by a number ranging from −1 ( say , liberal ) to +1 ( conservative ) .
These inclinations are reflected in agents ' posts .
Agents are also influenced by the opinions they see in their news feeds , and they can unfollow users with dissimilar opinions .
Starting with random initial networks and opinions @ @ @ @ @ @ @ social influence and unfollowing greatly accelerates the formation of polarized and segregated communities .
Indeed , the political echo chambers on Twitter are so extreme that individual users ' political leanings can be predicted with high accuracy : you have the same opinions as the majority of your connections .
This chambered structure efficiently spreads information within a community while insulating that community from other groups .
In 2014 our research group was targeted by a disinformation campaign claiming that we were part of a politically motivated effort to suppress free speech .
@ @ @ @ @ @ @ the conservative echo chamber , whereas debunking articles by fact-checkers were found mainly in the liberal community .
Sadly , such segregation of fake news items from their fact-check reports is the norm .
Social media can also increase our negativity .
In a recent laboratory study , Robert Jagiello , also at Warwick , found that socially shared information not only bolsters our biases but also becomes more resilient to correction .
He investigated how information is passed from person to person in a so-called social diffusion chain .
In the experiment @ @ @ @ @ @ @ read a set of articles about either nuclear power or food additives .
The articles were designed to be balanced , containing as much positive information ( for example , about less carbon pollution or longer-lasting food ) as negative information ( such as risk of meltdown or possible harm to health ) .
The first person in the social diffusion chain told the next person about the articles , the second told the third , and so on .
We observed an overall increase in the amount of negative information as it @ @ @ @ @ @ @ amplification of risk .
Moreover , work by Danielle J. Navarro and her colleagues at the University of New South Wales in Australia found that information in social diffusion chains is most susceptible to distortion by individuals with the most extreme biases .
Even worse , social diffusion also makes negative information more “ sticky.
” When Jagiello subsequently exposed people in the social diffusion chains to the original , balanced information—that is , the news that the first person in the chain had seen—the balanced information did little to reduce individuals ' @ @ @ @ @ @ @ passed through people not only had become more negative but also was more resistant to updating .
A 2015 study by OSoMe researchers Emilio Ferrara and Zeyao Yang analyzed empirical data about such “ emotional contagion ” on Twitter and found that people overexposed to negative content tend to then share negative posts , whereas those overexposed to positive content tend to share more positive posts .
Because negative content spreads faster than positive content , it is easy to manipulate emotions by creating narratives that trigger negative responses such as fear and @ @ @ @ @ @ @ University of Southern California , and his colleagues at the Bruno Kessler Foundation in Italy have shown that during Spain 's 2017 referendum on Catalan independence , social bots were leveraged to retweet violent and inflammatory narratives , increasing their exposure and exacerbating social conflict .
Information quality is further impaired by social bots , which can exploit all our cognitive loopholes .
Bots are easy to create .
Social media platforms provide so-called application programming interfaces that make it fairly trivial for a single actor to set up and control thousands of @ @ @ @ @ @ @ even with just a few early upvotes by bots on social media platforms such as Reddit , can have a huge impact on the subsequent popularity of a post .
At OSoMe , we have developed machine-learning algorithms to detect social bots .
One of these , Botometer , is a public tool that extracts 1,200 features from a given Twitter account to characterize its profile , friends , social network structure , temporal activity patterns , language and other features .
The program compares these characteristics with those of tens of thousands @ @ @ @ @ @ @ Twitter account a score for its likely use of automation .
In 2017 we estimated that up to 15 percent of active Twitter accounts were bots—and that they had played a key role in the spread of misinformation during the 2016 U.S. election period .
Within seconds of a fake news article being posted—such as one claiming the Clinton campaign was involved in occult rituals—it would be tweeted by many bots , and humans , beguiled by the apparent popularity of the content , would retweet it .
Bots also influence us by @ @ @ @ @ @ @ .
A bot only has to follow , like and retweet someone in an online community to quickly infiltrate it .
OSoMe researcher Xiaodan Lou developed another model in which some of the agents are bots that infiltrate a social network and share deceptively engaging low-quality content—think of clickbait .
One parameter in the model describes the probability that an authentic agent will follow bots—which , for the purposes of this model , we define as agents that generate memes of zero quality and retweet only one another .
Our simulations show that @ @ @ @ @ @ @ ecosystem 's information quality by infiltrating only a small fraction of the network .
Bots can also accelerate the formation of echo chambers by suggesting other inauthentic accounts to be followed , a technique known as creating “ follow trains.
” Some manipulators play both sides of a divide through separate fake news sites and bots , driving political polarization or monetization by ads .
At OSoMe , we recently uncovered a network of inauthentic accounts on Twitter that were all coordinated by the same entity .
Some pretended to be pro-Trump supporters @ @ @ @ @ @ @ , whereas others posed as Trump “ resisters ” ; all asked for political donations .
Such operations amplify content that preys on confirmation biases and accelerate the formation of polarized echo chambers .
Understanding our cognitive biases and how algorithms and bots exploit them allows us to better guard against manipulation .
OSoMe has produced a number of tools to help people understand their own vulnerabilities , as well as the weaknesses of social media platforms .
One is a mobile app called Fakey that helps users learn how to spot misinformation @ @ @ @ @ @ @ news feed , showing actual articles from low- and high-credibility sources .
Users must decide what they can or should not share and what to fact-check .
Analysis of data from Fakey confirms the prevalence of online social herding : users are more likely to share low-credibility articles when they believe that many other people have shared them .
Another program available to the public , called Hoaxy , shows how any extant meme spreads through Twitter .
In this visualization , nodes represent actual Twitter accounts , and links depict how retweets @ @ @ @ @ @ @ the meme from account to account .
Each node has a color representing its score from Botometer , which allows users to see the scale at which bots amplify misinformation .
These tools have been used by investigative journalists to uncover the roots of misinformation campaigns , such as one pushing the “ pizzagate ” conspiracy in the U.S .
They also helped to detect bot-driven voter-suppression efforts during the 2018 U.S. midterm election .
Manipulation is getting harder to spot , however , as machine-learning algorithms become better at emulating human behavior @ @ @ @ @ @ @ misinformation campaigns can also divert attention from other , more serious problems .
To combat such manipulation , we have recently developed a software tool called BotSlayer .
It extracts hashtags , links , accounts and other features that co-occur in tweets about topics a user wishes to study .
For each entity , BotSlayer tracks the tweets , the accounts posting them and their bot scores to flag entities that are trending and probably being amplified by bots or coordinated accounts .
The goal is to enable reporters , civil-society organizations and @ @ @ @ @ @ @ influence campaigns in real time .
These programmatic tools are important aids , but institutional changes are also necessary to curb the proliferation of fake news .
Education can help , although it is unlikely to encompass all the topics on which people are misled .
Some governments and social media platforms are also trying to clamp down on online manipulation and fake news .
But who decides what is fake or manipulative and what is not ?
Information can come with warning labels such as the ones Facebook and Twitter have started @ @ @ @ @ @ @ apply those labels be trusted ?
The risk that such measures could deliberately or inadvertently suppress free speech , which is vital for robust democracies , is real .
The dominance of social media platforms with global reach and close ties with governments further complicates the possibilities .
One of the best ideas may be to make it more difficult to create and share low-quality information .
This could involve adding friction by forcing people to pay to share or receive information .
Payment could be in the form of time , mental @ @ @ @ @ @ @ fees for subscriptions or usage .
Automated posting should be treated like advertising .
Some platforms are already using friction in the form of CAPTCHAs and phone confirmation to access accounts .
Twitter has placed limits on automated posting .
These efforts could be expanded to gradually shift online sharing incentives toward information that is valuable to consumers .
Free communication is not free .
By decreasing the cost of information , we have decreased its value and invited its adulteration .
To restore the health of our information ecosystem , we must @ @ @ @ @ @ @ and how the economics of information can be leveraged to protect us from being misled .

Consider Andy, who is worried about contracting COVID in 2020. Unable to read all the articles he sees on it, he relies on trusted friends for tips. When one opines on Facebook that pandemic fears are overblown, Andy dismisses the idea at first. But then the hotel where he works closes its doors, and with his job at risk, Andy starts wondering how serious the threat from the virus really is. No one he knows has died, after all. A colleague posts an article about the COVID “scare” having been created by Big Pharma in collusion with corrupt politicians, which jibes with Andy’s distrust of government. His Web search quickly takes him to articles claiming that COVID is no worse than the flu. Andy joins an online group of people who have been or fear being laid off and soon finds himself asking, like many of them, “What pandemic?” When he learns that several of his new friends are planning to attend a rally demanding an end to lockdowns, he decides to join them. Almost no one at the massive protest, including him, wears a mask. When his sister asks about the rally, Andy shares the conviction that has now become part of his identity: COVID is a hoax.
This example illustrates a minefield of cognitive biases. We prefer information from people we trust, our in-group. We pay attention to and are more likely to share information about risks—for Andy, the risk of losing his job. We search for and remember things that fit well with what we already know and understand. These biases are products of our evolutionary past, and for tens of thousands of years, they served us well. People who behaved in accordance with them—for example, by staying away from the overgrown pond bank where someone said there was a viper—were more likely to survive than those who did not.
Modern technologies are amplifying these biases in harmful ways, however. Search engines direct Andy to sites that inflame his suspicions, and social media connects him with like-minded people, feeding his fears. Making matters worse, bots—automated social media accounts that impersonate humans—enable misguided or malevolent actors to take advantage of his vulnerabilities.
Compounding the problem is the proliferation of online information. Viewing and producing blogs, videos, tweets and other units of information called memes have become so cheap and easy that the information marketplace is inundated. Unable to process all this material, we let our cognitive biases decide what we should pay attention to. These mental shortcuts influence which information we search for, comprehend, remember and repeat to a harmful extent.
The need to understand these cognitive vulnerabilities and how algorithms use or manipulate them has become urgent. At the University of Warwick in England and at Indiana University Bloomington’s Observatory on Social Media (OSoMe, pronounced “awesome”), our teams are using cognitive experiments, simulations, data mining and artificial intelligence to comprehend the cognitive vulnerabilities of social media users. Insights from psychological studies on the evolution of information conducted at Warwick inform the computer models developed at Indiana, and vice versa. We are also developing analytical and machine-learning aids to fight social media manipulation. Some of these tools are already being used by journalists, civil-society organizations and individuals to detect inauthentic actors, map the spread of false narratives and foster news literacy.
Information Overload
The glut of information has generated intense competition for people’s attention. As Nobel Prize–winning economist and psychologist Herbert A. Simon noted, “What information consumes is rather obvious: it consumes the attention of its recipients.” One of the first consequences of the so-called attention economy is the loss of high-quality information. The OSoMe team demonstrated this result with a set of simple simulations. It represented users of social media such as Andy, called agents, as nodes in a network of online acquaintances. At each time step in the simulation, agents may either create a meme or reshare one that they see in a news feed. To mimic limited attention, agents are allowed to view only a certain number of items near the top of their news feeds.
Running this simulation over many time steps, Lilian Weng, now at OpenAI, and researchers at OSoMe found that as agents’ attention became increasingly limited, the propagation of memes came to reflect the power-law distribution of actual social media: the probability that a meme would be shared a given number of times was roughly an inverse power of that number. For example, the likelihood of a meme being shared three times was approximately nine times less than that of its being shared once.
This winner-take-all popularity pattern of memes, in which most are barely noticed while a few spread widely, could not be explained by some of them being more catchy or somehow more valuable: the memes in this simulated world had no intrinsic quality. Virality resulted purely from the statistical consequences of information proliferation in a social network of agents with limited attention. Even when agents preferentially shared memes of higher quality, researcher Xiaoyan Qiu, then at OSoMe, observed little improvement in the overall quality of those shared the most. Our models revealed that even when we want to see and share high-quality information, our inability to view everything in our news feeds inevitably leads us to share things that are partly or completely untrue.
Source: “Limited Individual Attention and Online Virality of Low-Quality Information,” by Xiaoyan Qiu et al., in Nature Human Behaviour, Vol. 1, June 2017
Cognitive biases greatly worsen the problem. In a set of groundbreaking studies in 1932, psychologist Frederic Bartlett told volunteers a Native American legend about a young man who hears war cries and, pursuing them, enters a dreamlike battle that eventually leads to his real death. Bartlett asked the volunteers, who were non-Native, to recall the rather confusing story at increasing intervals, from minutes to years later. He found that as time passed, the rememberers tended to distort the tale’s culturally unfamiliar parts such that they were either lost to memory or transformed into more familiar things. We now know that our minds do this all the time: they adjust our understanding of new information so that it fits in with what we already know. One consequence of this so-called confirmation bias is that people often seek out, recall and understand information that best confirms what they already believe.
This tendency is extremely difficult to correct. Experiments consistently show that even when people encounter balanced information containing views from differing perspectives, they tend to find supporting evidence for what they already believe. And when people with divergent beliefs about emotionally charged issues such as climate change are shown the same information on these topics, they become even more committed to their original positions.
Making matters worse, search engines and social media platforms provide personalized recommendations based on the vast amounts of data they have about users’ past preferences. They prioritize information in our feeds that we are most likely to agree with—no matter how fringe—and shield us from information that might change our minds. This makes us easy targets for polarization. Nir Grinberg and his co-workers at Northeastern University showed in 2019 that conservatives in the U.S. are more receptive to misinformation. But our own analysis of consumption of low-quality information on Twitter shows that the vulnerability applies to both sides of the political spectrum, and no one can fully avoid it. Even our ability to detect online manipulation is affected by our political bias, though not symmetrically: Republican users are more likely to mistake bots promoting conservative ideas for humans, whereas Democrats are more likely to mistake conservative human users for bots.
Social Herding
In New York City in August 2019, people began running away from what sounded like gunshots. Others followed, some shouting, “Shooter!” Only later did they learn that the blasts came from a backfiring motorcycle. In such a situation, it may pay to run first and ask questions later. In the absence of clear signals, our brains use information about the crowd to infer appropriate actions, similar to the behavior of schooling fish and flocking birds.
Such social conformity is pervasive. In a fascinating 2006 study involving 14,000 Web-based volunteers, Matthew Salganik, then at Columbia University, and his colleagues found that when people can see what music others are downloading, they end up downloading similar songs. Moreover, when people were isolated into “social” groups, in which they could see the preferences of others in their circle but had no information about outsiders, the choices of individual groups rapidly diverged. But the preferences of “nonsocial” groups, where no one knew about others’ choices, stayed relatively stable. In other words, social groups create a pressure toward conformity so powerful that it can overcome individual preferences, and by amplifying random early differences, it can cause segregated groups to diverge to extremes.
Credit: Filippo Menczer
Social media follows a similar dynamic. We confuse popularity with quality and end up copying the behavior we observe. Experiments on Twitter by Bjarke Mønsted, then at the Technical University of Denmark, and his colleagues indicate that information is transmitted via “complex contagion”: when we are repeatedly exposed to an idea, typically from many sources, we are more likely to adopt and reshare it. This social bias is further amplified by what psychologists call the “mere exposure effect”: when people are repeatedly exposed to the same stimuli, such as certain faces, they grow to like those stimuli more than those they have encountered less often.
Such biases translate into an irresistible urge to pay attention to information that is going viral—if everybody else is talking about it, it must be important. In addition to showing us items that conform with our views, social media platforms such as Facebook, Twitter, YouTube and Instagram place popular content at the top of our screens and show us how many people have liked and shared something. Few people realize that these cues do not provide independent assessments of quality.
In fact, programmers who design the algorithms for ranking memes on social media assume that the “wisdom of crowds” will quickly identify high-quality items; they use popularity as a proxy for quality. Our analysis of vast amounts of anonymous data about clicks shows that all platforms—social media, search engines and news sites—preferentially serve up information from a narrow subset of popular sources.
To understand why, we modeled how they combine signals for quality and popularity in their rankings. In this model, agents with limited attention—those who see only a given number of items at the top of their news feeds—are also more likely to click on memes ranked higher by the platform. Each item has intrinsic quality, as well as a level of popularity determined by how many times it has been clicked on. Another variable tracks the extent to which the ranking relies on popularity rather than quality. Simulations of this model reveal that such algorithmic bias typically suppresses the quality of memes even in the absence of human bias. Even when we want to share the best information, the algorithms end up misleading us.
Echo Chambers
Most of us do not believe we follow the herd. But our confirmation bias leads us to follow others who are like us, a dynamic that is sometimes referred to as homophily—a tendency for like-minded people to connect with one another. Social media amplifies homophily by allowing users to alter their social network structures through following, unfriending, and so on. The result is that people become segregated into large, dense and increasingly misinformed communities commonly described as echo chambers.
At OSoMe, we explored the emergence of online echo chambers through another simulation, EchoDemo. In this model, each agent has a political opinion represented by a number ranging from −1 (say, liberal) to +1 (conservative). These inclinations are reflected in agents’ posts. Agents are also influenced by the opinions they see in their news feeds, and they can unfollow users with dissimilar opinions. Starting with random initial networks and opinions, we found that the combination of social influence and unfollowing greatly accelerates the formation of polarized and segregated communities.
Credit: Jen Christiansen; Source: Dimitar Nikolov and Filippo Menczer (data)
Indeed, the political echo chambers on Twitter are so extreme that individual users’ political leanings can be predicted with high accuracy: you have the same opinions as the majority of your connections. This chambered structure efficiently spreads information within a community while insulating that community from other groups. In 2014 our research group was targeted by a disinformation campaign claiming that we were part of a politically motivated effort to suppress free speech. This false charge spread virally mostly in the conservative echo chamber, whereas debunking articles by fact-checkers were found mainly in the liberal community. Sadly, such segregation of fake news items from their fact-check reports is the norm.
Social media can also increase negativity. In a 2018 laboratory study, Robert Jagiello, now at the University of Oxford, and one of us (Hills) found that socially shared information not only bolsters biases but also becomes more resilient to correction. We investigated how information is passed from person to person in a so-called social diffusion chain. In the experiment, the first person in the chain read a set of articles about either nuclear power or food additives. The articles were designed to be balanced, containing as much positive information (for example, about less carbon pollution or longer-lasting food) as negative information (such as risk of meltdown or possible harm to health).
The first person in the social diffusion chain told the next person about the articles, the second told the third, and so on. We observed an overall increase in the amount of negative information as it passed along the chain—known as the social amplification of risk. Moreover, work by Danielle J. Navarro and her colleagues at the University of New South Wales in Australia found that information in social diffusion chains is most susceptible to distortion by individuals with the most extreme biases.
Even worse, social diffusion also makes negative information more “sticky.” When Jagiello and Hills subsequently exposed people in the social diffusion chains to the original, balanced information—that is, the news that the first person in the chain had seen—the balanced information did little to reduce individuals’ negative attitudes. The information that had passed through people not only had become more negative but also was more resistant to updating.
A 2015 study by Emilio Ferrara and Zeyao Yang, then both OSoMe researchers, analyzed empirical data about such “emotional contagion” on Twitter and found that people overexposed to negative content tend to share negative posts, whereas those overexposed to positive content tend to share more positive posts. Because negative content spreads faster than positive content, it is easy to manipulate emotions by creating narratives that trigger negative responses such as fear and anxiety. Ferrara, now at the University of Southern California, and his colleagues at the Bruno Kessler Foundation in Italy have shown that during Spain’s 2017 referendum on Catalan independence, social bots were leveraged to retweet violent and inflammatory narratives, increasing their exposure and exacerbating social conflict.
Rise of the Bots
Information quality is further impaired by social bots, which can exploit all our cognitive loopholes. Bots are easy to create. Social media platforms provide so-called application programming interfaces that make it fairly trivial for a single actor to set up and control thousands of bots. But amplifying a message, even with just a few early upvotes by bots on social media platforms such as Reddit, can have a huge impact on the subsequent popularity of a post.
At OSoMe, we have developed machine-learning algorithms to detect social bots. One of these, Botometer, is a public tool that extracts 1,200 features from a given Twitter account to characterize its profile, friends, social network structure, temporal activity patterns, language and other features. The program compares these characteristics with those of tens of thousands of previously identified bots to give the Twitter account a score for its likely use of automation.
In 2017 we estimated that up to 15 percent of active Twitter accounts were bots—and that they had played a key role in the spread of misinformation during the 2016 U.S. election period. Within seconds of a fake news article being posted—such as one claiming the Clinton campaign was involved in occult rituals—it would be tweeted by many bots, and humans, beguiled by the apparent popularity of the content, would retweet it.
Bots also influence us by pretending to represent people from our in-group. A bot only has to follow, like and retweet someone in an online community to quickly infiltrate it. Xiaodan Lou of Beijing Normal University, working with OSoMe, developed another model in which some of the agents are bots that infiltrate a social network and share deceptively engaging low-quality content—think of clickbait. One parameter in the model describes the probability that an authentic agent will follow bots—which, for the purposes of this model, we define as agents that generate memes of zero quality and retweet only one another. Our simulations show that these bots can effectively suppress the entire ecosystem’s information quality by infiltrating only a small fraction of the network. Bots can also accelerate the formation of echo chambers by suggesting other inauthentic accounts to be followed, a technique known as creating “follow trains.”
Some manipulators play both sides of a divide through separate fake news sites and bots, driving political polarization or monetization by ads. At OSoMe, we uncovered a network of inauthentic accounts on Twitter that were all coordinated by the same entity. Some pretended to be pro-Trump supporters of the Make America Great Again U.S. election campaign, whereas others posed as Trump “resisters” all asked for political donations. Such operations amplify content that preys on confirmation biases and accelerate the formation of polarized echo chambers.
Curbing Online Manipulation
Understanding our cognitive biases and how algorithms and bots exploit them allows us to better guard against manipulation. OSoMe has produced a number of tools to help people understand their own vulnerabilities, as well as the weaknesses of social media platforms. One is a mobile app called Fakey that helps users learn how to spot misinformation. The game simulates a social media news feed, showing actual articles from low- and high-credibility sources. Users must decide what they can or should not share and what to fact-check. Analysis of data from Fakey confirms the prevalence of online social herding: users are more likely to share low-credibility articles when they believe that many other people have shared them.
Another program available to the public, called Hoaxy, shows how any extant meme spreads through Twitter. In this visualization, nodes represent actual Twitter accounts, and links depict how retweets, quotes, mentions and replies propagate the meme from account to account. Each node has a color representing its score from Botometer, which allows users to see the scale at which bots amplify misinformation. These tools have been used by investigative journalists to uncover the roots of misinformation campaigns, such as one pushing the “pizzagate” conspiracy in the U.S. They also helped to detect bot-driven voter-suppression efforts during the 2018 U.S. midterm election. Manipulation is getting harder to spot, however, as machine-learning algorithms become better at emulating human behavior.
Apart from spreading fake news, misinformation campaigns can also divert attention from other, more serious problems. To combat such manipulation, we developed a software tool called BotSlayer. It extracts hashtags, links, accounts and other features that co-occur in tweets about topics a user wishes to study. For each entity, BotSlayer tracks the tweets, the accounts posting them and their bot scores to flag entities that are trending and probably being amplified by bots or coordinated accounts. The goal is to enable reporters, civil-society organizations and political candidates to spot and track inauthentic influence campaigns in real time.
These programmatic tools are important aids, but institutional changes are also necessary to curb the proliferation of fake news. Education can help, although it is unlikely to encompass all the topics on which people are misled. Some governments and social media platforms are also trying to clamp down on online manipulation and fake news. But who decides what is fake or manipulative and what is not? Information can come with warning labels such as the ones Facebook and Twitter provide, but can the people who apply those labels be trusted? The risk that such measures could either deliberately or inadvertently suppress free speech, which is vital for robust democracies, is real. The dominance of social media platforms with global reach and close ties with governments further complicates the possibilities.
One of the best ideas may be to make it more difficult to create and share low-quality information. This could involve adding friction by forcing people to pay to share or receive information. Payment could be in the form of time, mental work such as puzzles, or microscopic fees for subscriptions or usage. Automated posting should be treated like advertising. Some platforms are already using friction in the form of CAPTCHAs and phone confirmation to access accounts. Twitter has placed limits on automated posting. These efforts could be expanded to gradually shift online sharing incentives toward information that is valuable to consumers.
Free communication is not free. By decreasing the cost of information, we have decreased its value and invited its adulteration. To restore the health of our information ecosystem, we must understand the vulnerabilities of our overwhelmed minds and how the economics of information can be leveraged to protect us from being misled.


scientificamerican--2020-11-19--Ice Drownings Expected to Rise as Winters Warm

Ice skating , ice fishing , snowmobiling : These iconic winter activities are a way of life for many cold-climate communities .
But in some regions , they may be on thin ice—literally .
New research suggests that winter drownings increase with rising winter temperatures .
The warmer the air , the more likely it is that someone will fall through an unstable sheet of ice .
That means drownings could increase in cold countries as the climate continues to heat up , the study ’ s authors warn .
Published yesterday in the journal PLOS ONE , the research @ @ @ @ @ @ @ from 10 cold-climate countries across the Northern Hemisphere , including the United States , Canada , Finland , Sweden , Russia and Germany .
In some countries , the data spanned several decades .
In general , drownings were rare in temperatures below 14 degrees Fahrenheit and above freezing—times when lake ice is at its most stable or when it melts away .
They increased sharply as temperatures began to approach the freezing point .
Drowning rates tended to be highest at the beginning and end of winter—March or April in most of @ @ @ @ @ @ @ to be warmer .
The study shows that there ’ s a statistical relationship between winter temperatures and winter drownings—it doesn ’ t necessarily prove that the temperatures are the cause .
But the researchers suggest that ice thickness is the most likely explanation .
As temperatures rise , lake ice becomes less stable and less safe for sport or recreation .
That means rising winter temperatures could be a safety risk in cold climates , the authors suggest .
The study involved an international group of researchers working together to investigate the @ @ @ @ @ @ @ and human communities .
The group had begun to look into the implications for winter recreation and cultural events , like ice fishing tournaments , when they realized that winter drownings might be worth investigating .
“ This group of collaborators , we had a call every month , ” said Sapna Sharma , the study ’ s lead author and a researcher at York University in Canada .
“ I remember saying to the group , ’ Hey , we had a really tragic drowning through ice on a lake just north @ @ @ @ @ @ @ Collaborators in the United States and Sweden said similar incidents had occurred recently in their communities as well .
The group began to wonder if there were any connections between winter drownings and winter weather .
Their findings suggest that cold-climate communities may wish to reassess the ways they regulate or communicate about winter recreation .
While drownings did generally increase as winter temperatures approached freezing , the rate of drownings still varied widely from one country to the next .
The researchers noted that in countries with stringent rules about winter recreation—local @ @ @ @ @ @ @ prohibit ice fishing and other activities—drownings were far less frequent .
On the other hand , the number of drowning incidents tended to be higher in places where winter activities have strong cultural ties , such as Indigenous communities in northern Canada .
The researchers suggest that these communities may be at higher risk as winter temperatures rise .
The study provides a human angle to a phenomenon scientists are already tracking .
Thinning and melting winter lake ice is already an expected consequence of climate change .
Last year , a study @ @ @ @ @ @ @ a few degrees of warming could cause thousands of lakes across the Northern Hemisphere to go from freezing reliably each winter to freezing only some of the time .
Sharma also led that study .
“ I think if I could make one recommendation , it would be to incorporate winter safety and winter ice safety into swimming lessons for children and teens , ” Sharma suggested .
In general , she said , raising awareness about the dangers of warming winters is key to helping keep communities safe .
“ It will @ @ @ @ @ @ @ is changing , and it ’ s not happening just for the polar bears or for hurricane strength—it ’ s happening within our backyards and the lakes and rivers and creeks that we might spend time on in the winters , ” she said .
“ And we need to adapt our behavior and our decisionmaking on whether a body of water is safe to use.
” Reprinted from Climatewire with permission from E & E News .
E & E provides daily coverage of essential energy and environmental news at www.eenews.net .

Ice skating, ice fishing, snowmobiling: These iconic winter activities are a way of life for many cold-climate communities. But in some regions, they may be on thin ice—literally.
New research suggests that winter drownings increase with rising winter temperatures. The warmer the air, the more likely it is that someone will fall through an unstable sheet of ice.
That means drownings could increase in cold countries as the climate continues to heat up, the study’s authors warn.
Published yesterday in the journal PLOS ONE, the research documents more than 4,000 wintertime drowning incidents from 10 cold-climate countries across the Northern Hemisphere, including the United States, Canada, Finland, Sweden, Russia and Germany. In some countries, the data spanned several decades.
When analyzed alongside monthly winter climate data, several patterns emerged.
In general, drownings were rare in temperatures below 14 degrees Fahrenheit and above freezing—times when lake ice is at its most stable or when it melts away. They increased sharply as temperatures began to approach the freezing point.
Drowning rates tended to be highest at the beginning and end of winter—March or April in most of the 10 countries—when the weather is likely to be warmer.
The study shows that there’s a statistical relationship between winter temperatures and winter drownings—it doesn’t necessarily prove that the temperatures are the cause. But the researchers suggest that ice thickness is the most likely explanation. As temperatures rise, lake ice becomes less stable and less safe for sport or recreation.
That means rising winter temperatures could be a safety risk in cold climates, the authors suggest.
The study involved an international group of researchers working together to investigate the links between climate change, lake ice and human communities. The group had begun to look into the implications for winter recreation and cultural events, like ice fishing tournaments, when they realized that winter drownings might be worth investigating.
“This group of collaborators, we had a call every month,” said Sapna Sharma, the study’s lead author and a researcher at York University in Canada. “I remember saying to the group, ’Hey, we had a really tragic drowning through ice on a lake just north of Toronto where I live.’”
Collaborators in the United States and Sweden said similar incidents had occurred recently in their communities as well. The group began to wonder if there were any connections between winter drownings and winter weather.
Their findings suggest that cold-climate communities may wish to reassess the ways they regulate or communicate about winter recreation.
While drownings did generally increase as winter temperatures approached freezing, the rate of drownings still varied widely from one country to the next. The researchers noted that in countries with stringent rules about winter recreation—local laws in Italy, for instance, prohibit ice fishing and other activities—drownings were far less frequent.
On the other hand, the number of drowning incidents tended to be higher in places where winter activities have strong cultural ties, such as Indigenous communities in northern Canada. The researchers suggest that these communities may be at higher risk as winter temperatures rise.
The study provides a human angle to a phenomenon scientists are already tracking. Thinning and melting winter lake ice is already an expected consequence of climate change.
Last year, a study in Nature Climate Change suggested that just a few degrees of warming could cause thousands of lakes across the Northern Hemisphere to go from freezing reliably each winter to freezing only some of the time. Sharma also led that study.
“I think if I could make one recommendation, it would be to incorporate winter safety and winter ice safety into swimming lessons for children and teens,” Sharma suggested.
In general, she said, raising awareness about the dangers of warming winters is key to helping keep communities safe.
“It will just make more people aware that climate is changing, and it’s not happening just for the polar bears or for hurricane strength—it’s happening within our backyards and the lakes and rivers and creeks that we might spend time on in the winters,” she said. “And we need to adapt our behavior and our decisionmaking on whether a body of water is safe to use.”
Reprinted from Climatewire with permission from E&E News. E&E provides daily coverage of essential energy and environmental news at www.eenews.net.


scientificamerican--2020-11-19--Legendary Arecibo Telescope Will Close Forever, And Scientists Are Reeling

One of astronomy ’ s most renowned telescopes—the 305-metre-wide radio telescope at Arecibo , Puerto Rico—is permanently closing .
Engineers can not find a safe way to repair it after two cables supporting the structure suddenly and catastrophically broke , one in August and one in early November .
It is the end of one of the most iconic and scientifically productive telescopes in the history of astronomy—and scientists are mourning its loss .
“ I don ’ t know what to say , ” says Robert Kerr , a former director of the observatory .
“ It ’ s @ @ @ @ @ @ @ devastated , ” says Abel Mendéz , an astrobiologist at the University of Puerto Rico in Arecibo who uses the observatory .
The Arecibo telescope , which was built in 1963 , was the world ’ s largest radio telescope for decades and has historical and modern importance in astronomy .
It was the site from which astronomers sent an interstellar radio message in 1974 , in case any extraterrestrials might hear it , and where the first known extrasolar planet was discovered , in 1992 .
It has also done pioneering work @ @ @ @ @ @ @ puzzling celestial blasts known as fast radio bursts , and studying many other phenomena .
All of those lines of investigation are now shut down for good , although limited science continues at some smaller facilities at the Arecibo site .
The cables that broke helped support a 900-tonne platform of scientific instruments , which hangs above the main telescope dish .
The first cable smashed panels at the edge of the dish , but the second tore huge gashes in a central portion of it.A high-resolution satellite image , produced at Nature @ @ @ @ @ @ @ Earth-observing company , shows the extent of the damage wrought by the second cable ; the green of the vegetation below shows through large holes in the dish .
A second photograph , released this week by observatory officials , also reveals the destruction .
They are the only public glimpses of the damage to date .
If any more cables fail—which could happen at any time—the entire platform could crash into the dish below .
The US National Science Foundation ( NSF ) , which owns Arecibo Observatory , is working on @ @ @ @ @ @ @ in a controlled fashion .
But those plans will take weeks to develop , and there ’ s no telling whether the platform might crash down uncontrollably in the meantime .
“ Even attempts at stabilization or at testing the cables could result in accelerating the catastrophic failure , ” said Ralph Gaume , the NSF ’ s head of astronomy , at a 19 November media briefing .
So NSF decided to close the Arecibo dish permanently .
“ This decision is not an easy one to make , but safety is @ @ @ @ @ @ @ Jones , head of NSF ’ s mathematical and physical sciences directorate .
The closure is likely to come as a shock to the wider astronomical community .
“ Losing the Arecibo Observatory would be a big loss for science , for planetary defense , and for Puerto Rico , ” said Desireé Cotto-Figueroa , an astronomer at the University of Puerto Rico at Humacao , in an email prior to the announced closure .
NSF officials insist that the cable failures came as a surprise .
After the first , engineering teams @ @ @ @ @ @ @ the second cable , which was more crucial to holding up the structure , but they did not see it as a major problem because the weight it was carrying was well within its design capacity .
“ It was not seen as an immediate threat , ” says Ashley Zauderer , programme manager for Arecibo at the NSF .
Over the years , external review committees have highlighted the ongoing need to maintain the telescope ’ s ageing cables .
Zauderer said that maintenance had been completed according to schedule .
Before @ @ @ @ @ @ @ problems at the observatory were in January 2014 , when a magnitude-6.4 earthquake caused damage to another of the main cables , which engineers repaired .
The ageing structure has suffered other shocks in recent years , including Hurricane Maria in 2017 and a series of smaller earthquakes in January of this year .
There is no estimate yet on the cost of decommissioning the telescope .
Some of the observatory ’ s scientific projects may be able to be transferred to other facilities , Gaume said—and that he expects scientists to propose @ @ @ @ @ @ @ Science does continue at other portions of the Arecibo observatory , which encompasses more than the 305-metre dish .
They include two lidar facilities that shoot lasers into the atmosphere to study atmospheric phenomena .
Arecibo had been regularly upgraded , with several new instruments slated to be installed in the coming years .
“ The telescope is in no way obsolete , ” says Christopher Salter , an astronomer at the National Radio Astronomy Observatory in Green Bank , West Virginia , who worked at Arecibo for years .
The observatory is @ @ @ @ @ @ @ Rico , fostering the careers of many astronomers and engineers .
And it has become a part of the pop culture lexicon , featuring in major movies such as Contact , based on a Carl Sagan novel , and the James Bond film GoldenEye .
The last major radio telescope disaster happened in 1988 , when a 300-foot-wide antenna at the observatory in Green Bank , West Virginia , collapsed one night due to structural failure .
This article is reproduced with permission and was first published on November 19 2020 .

One of astronomy’s most renowned telescopes—the 305-metre-wide radio telescope at Arecibo, Puerto Rico—is permanently closing. Engineers cannot find a safe way to repair it after two cables supporting the structure suddenly and catastrophically broke, one in August and one in early November.
It is the end of one of the most iconic and scientifically productive telescopes in the history of astronomy—and scientists are mourning its loss.
“I don’t know what to say,” says Robert Kerr, a former director of the observatory. “It’s just unbelievable.”
“I am totally devastated,” says Abel Mendéz, an astrobiologist at the University of Puerto Rico in Arecibo who uses the observatory.
The Arecibo telescope, which was built in 1963, was the world’s largest radio telescope for decades and has historical and modern importance in astronomy. It was the site from which astronomers sent an interstellar radio message in 1974, in case any extraterrestrials might hear it, and where the first known extrasolar planet was discovered, in 1992. It has also done pioneering work in detecting near-Earth asteroids, observing the puzzling celestial blasts known as fast radio bursts, and studying many other phenomena. All of those lines of investigation are now shut down for good, although limited science continues at some smaller facilities at the Arecibo site.
Assessing the damage
The cables that broke helped support a 900-tonne platform of scientific instruments, which hangs above the main telescope dish. The first cable smashed panels at the edge of the dish, but the second tore huge gashes in a central portion of it.A high-resolution satellite image, produced at Nature’s request by Planet, an Earth-observing company, shows the extent of the damage wrought by the second cable; the green of the vegetation below shows through large holes in the dish. A second photograph, released this week by observatory officials, also reveals the destruction. They are the only public glimpses of the damage to date.
If any more cables fail—which could happen at any time—the entire platform could crash into the dish below. The US National Science Foundation (NSF), which owns Arecibo Observatory, is working on plans to safely lower the platform down in a controlled fashion.
But those plans will take weeks to develop, and there’s no telling whether the platform might crash down uncontrollably in the meantime. “Even attempts at stabilization or at testing the cables could result in accelerating the catastrophic failure,” said Ralph Gaume, the NSF’s head of astronomy, at a 19 November media briefing.
So NSF decided to close the Arecibo dish permanently. “This decision is not an easy one to make, but safety is the number-one priority,” said Sean Jones, head of NSF’s mathematical and physical sciences directorate.
The closure is likely to come as a shock to the wider astronomical community. “Losing the Arecibo Observatory would be a big loss for science, for planetary defense, and for Puerto Rico,” said Desireé Cotto-Figueroa, an astronomer at the University of Puerto Rico at Humacao, in an email prior to the announced closure.
NSF officials insist that the cable failures came as a surprise. After the first, engineering teams spotted a handful of broken wires on the second cable, which was more crucial to holding up the structure, but they did not see it as a major problem because the weight it was carrying was well within its design capacity. “It was not seen as an immediate threat,” says Ashley Zauderer, programme manager for Arecibo at the NSF.
Over the years, external review committees have highlighted the ongoing need to maintain the telescope’s ageing cables. Zauderer said that maintenance had been completed according to schedule.
Before this year, the last major cable problems at the observatory were in January 2014, when a magnitude-6.4 earthquake caused damage to another of the main cables, which engineers repaired. The ageing structure has suffered other shocks in recent years, including Hurricane Maria in 2017 and a series of smaller earthquakes in January of this year.
There is no estimate yet on the cost of decommissioning the telescope.
A legendary site
Some of the observatory’s scientific projects may be able to be transferred to other facilities, Gaume said—and that he expects scientists to propose where to shift their research to. Science does continue at other portions of the Arecibo observatory, which encompasses more than the 305-metre dish. They include two lidar facilities that shoot lasers into the atmosphere to study atmospheric phenomena.
Arecibo had been regularly upgraded, with several new instruments slated to be installed in the coming years. “The telescope is in no way obsolete,” says Christopher Salter, an astronomer at the National Radio Astronomy Observatory in Green Bank, West Virginia, who worked at Arecibo for years.
The observatory is also a major science-educational centre for Puerto Rico, fostering the careers of many astronomers and engineers. And it has become a part of the pop culture lexicon, featuring in major movies such as Contact, based on a Carl Sagan novel, and the James Bond film GoldenEye.
The last major radio telescope disaster happened in 1988, when a 300-foot-wide antenna at the observatory in Green Bank, West Virginia, collapsed one night due to structural failure.
This article is reproduced with permission and was first published on November 19 2020.


scientificamerican--2020-11-19--Evaluating COVID Risk on Planes, Trains and Automobiles

With COVID-19 reaching the most dangerous levels the U.S. has seen since the pandemic began , the country faces a problematic holiday season .
Despite the risk , many people are likely to travel using various forms of transportation that will inevitably put them in relatively close contact with others .
Many transit companies have established frequent cleaning routines , but evidence suggests that airborne transmission of the novel coronavirus poses a greater danger than surfaces .
The virus is thought to be spread primarily by small droplets , called aerosols , that hang in the air and larger droplets @ @ @ @ @ @ @ feet or so .
Although no mode of public transportation is completely safe , there are some concrete ways to reduce risk , whether on an airplane , train or bus—or even in a shared car .
At a casual glance , air travel might seem like the perfect recipe for COVID transmission : it packs dozens of people into a confined space , often for hours at a time .
But many planes have excellent high-efficiency particulate air ( HEPA ) filters that capture more than 99 percent of particles in the @ @ @ @ @ @ @ the coronavirus that causes COVID .
When their recirculation systems are operating , most commercial passenger jets bring in outside air in a top-to-bottom direction about 20 to 30 times per hours .
This results in a 50–50 mix of outside and recirculated air and reduces the potential for airborne spread of a respiratory virus .
Many airlines now require passengers to wear a mask during flights except for mealtimes , and some are blocking off middle seats to allow more distancing between people .
Companies have also implemented rigorous cleaning procedures between @ @ @ @ @ @ @ into overall risk ?
“ An airplane cabin is probably one of the most secure conditions you can be in , ” says Sebastian Hoehl of the Institute for Medical Virology at Goethe University Frankfurt in Germany , who has co-authored two papers on COVID-19 transmission on specific flights , which were published in JAMA Network Open and the New England Journal of Medicine , respectfully .
Still , a handful of case studies have found that limited transmission can take place onboard .
One such investigation of a 10-hour journey from London @ @ @ @ @ @ @ that 15 people were likely infected with COVID-19 in-flight—and that 12 of them had sat within a couple of rows of a single symptomatic passenger in business class .
( The results were published this month in the U.S. Centers for Disease Control and Prevention ’ s journal Emerging Infectious Diseases . )
Most of these flights occurred early on in the pandemic , however , and in the case of the March 1 flight , masks were likely not worn , the researchers wrote .
Meanwhile a recent Department of Defense study @ @ @ @ @ @ @ mannequins exhaling simulated virus particles and found that a person would have to be exposed to an infectious passenger for at least 54 hours to get an infectious dose .
This finding assumes the infected passenger is wearing a surgical mask , however , and it does not account for the dangers involved in removing the mask for meals or talking or in moving about on the plane .
With flying , COVID risk really comes down to how closely one sits to other people and for how long , whether or not @ @ @ @ @ @ @ how infectious any passengers are at the time .
If you happen to be seated close to a person who is actively “ shedding ” the virus , especially if it is a long flight or that person is not wearing a mask , there is a higher chance that you will get the disease .
But if you are seated relatively far from others , and everyone is wearing a mask , your risk is probably fairly low .
Being in a crowded airport or taking a taxi to get there could @ @ @ @ @ @ @ This past spring the novel coronavirus tore through New York City , reaching a peak of thousands of new cases per day in April .
Initially some researchers blamed the metropolis ’ s bustling subway—which carried 5.5 million commuters on a typical pre-COVID weekday—for making it the epicenter of the pandemic in the U.S .
But later reviews of the evidence suggest mass transit systems have not been major drivers of viral spread .
In August the New York Times asked several international transportation agencies whether any superspreading events had been linked to @ @ @ @ @ @ @ had .
A September report by the American Public Transportation Association ( APTA ) examined the coronavirus ’ s spread in cities around the globe that had robust public transit systems .
It found no correlation between mass transit use and transmission of the virus .
Many of the cities in question required commuters to wear masks .
The APTA report suggested that commuters should reduce risk by wearing a mask and staying six feet apart and that train cars should be well ventilated .
Most major cities ’ subways and trains constantly @ @ @ @ @ @ @ older air , both of which are pulled through a filter rated on the MERV ( minimum efficiency reporting value ) scale .
MERV-13 filters are less efficient than HEPA filters , but the U.S. Environmental Protection Agency recommends them for reducing the number of airborne viral particles .
A number of subway and train systems use lower-rated MERV filters that are still capable of at least reducing the volume of coronavirus in the air .
The APTA report notes that on most systems ’ subway cars and buses , new air replaces @ @ @ @ @ @ @ .
Passenger train operator Amtrak claims to cycle air through cabins 44 times an hour .
But even with fresh air available , an infected passenger can still transmit the coronavirus to someone who is sitting in close proximity—so riders should attempt to space themselves out and avoid densely packed cars whenever possible .
Mask wearing reduces risk and is now mandatory on many forms of transit in the U.S. , including New York City ’ s subway and bus systems , as well as Amtrak trains throughout the country .
Finally , @ @ @ @ @ @ @ a ride can also be helpful : 15 minutes on a subway is safer than a multihour train trip .
Many buses have HVAC ( heating , ventilating and air-conditioning ) systems similar to those on subways and trains , with one additional factor : buses are more likely to have windows that open .
Windows , as well as open ceiling vents , allow fresh air to enter the vehicle .
In addition , in a city system ( as opposed to a long-distance ride ) , buses make frequent stops .
@ @ @ @ @ @ @ in each time the doors open .
In a case study of a bus in China , a passenger with the novel coronavirus was able to infect many other riders , including those seated up to seven rows away .
There seemed to be less risk of transmission , however , for people who were seated near windows and doors that could open .
In addition to snagging window seats , bus riders should look for the same safety features they would on a subway : a mask requirement , good ventilation and @ @ @ @ @ @ @ to short trips may also be helpful .
Some bus companies have installed clear partitions between drivers and passengers .
Doing so might limit the spread of virus-carrying droplets from coughing , sneezing , breathing or speaking but is unlikely to prevent viral transmission via smaller airborne particles .
Before the pandemic , many people would not think twice about ordering a ride-share car or hailing a taxi .
But now the idea of being in such an enclosed space with a stranger can seem like a life-and-death proposition .
It is nearly @ @ @ @ @ @ @ six feet apart in a car , so transmission is definitely possible .
An NEJM study in March reported on a Thai taxi driver who fell ill and tested positive for the coronavirus after driving some tourists who had been coughing but wearing a mask .
Reliable data on the frequency of COVID transmission in cars is lacking , however .
Keeping the windows open and making sure the air system is set to take in outside air instead of recycling it should reduce the risk .
Wearing a mask also probably helps @ @ @ @ @ @ @ both require drivers and passengers to do so .
These companies have also stopped offering car pools ( except in Uber ’ s case for people who work together ) , so passengers are only exposed to the driver and anyone in their own party .
Some drivers have even installed a plastic shield between the front and back seats .
But as is the case with buses , these barriers would likely only protect against larger droplets , not aerosols .
Shorter rides—especially those under roughly 15 minutes—pose a lower risk than @ @ @ @ @ @ @ a minimum could also reduce the danger because talking is known to release aerosols that can spread the virus .
If the rate of community transmission in your region is low , chances are that taking the occasional taxi or ride-share car is not a huge risk—provided you wear a mask and keep the windows open as much as possible .
Read more about the coronavirus outbreak from Scientific American here .
And read coverage from our international network of magazines here .

With COVID-19 reaching the most dangerous levels the U.S. has seen since the pandemic began, the country faces a problematic holiday season. Despite the risk, many people are likely to travel using various forms of transportation that will inevitably put them in relatively close contact with others. Many transit companies have established frequent cleaning routines, but evidence suggests that airborne transmission of the novel coronavirus poses a greater danger than surfaces. The virus is thought to be spread primarily by small droplets, called aerosols, that hang in the air and larger droplets that fall to the ground within six feet or so. Although no mode of public transportation is completely safe, there are some concrete ways to reduce risk, whether on an airplane, train or bus—or even in a shared car.
Credit: Amanda Montañez
Airplanes
At a casual glance, air travel might seem like the perfect recipe for COVID transmission: it packs dozens of people into a confined space, often for hours at a time. But many planes have excellent high-efficiency particulate air (HEPA) filters that capture more than 99 percent of particles in the air, including microbes as SARS-CoV-2, the coronavirus that causes COVID. When their recirculation systems are operating, most commercial passenger jets bring in outside air in a top-to-bottom direction about 20 to 30 times per hours. This results in a 50–50 mix of outside and recirculated air and reduces the potential for airborne spread of a respiratory virus. Many airlines now require passengers to wear a mask during flights except for mealtimes, and some are blocking off middle seats to allow more distancing between people. Companies have also implemented rigorous cleaning procedures between flights. So how does this translate into overall risk?
“An airplane cabin is probably one of the most secure conditions you can be in,” says Sebastian Hoehl of the Institute for Medical Virology at Goethe University Frankfurt in Germany, who has co-authored two papers on COVID-19 transmission on specific flights, which were published in JAMA Network Open and the New England Journal of Medicine, respectfully. Still, a handful of case studies have found that limited transmission can take place onboard. One such investigation of a 10-hour journey from London to Hanoi starting on March 1 found that 15 people were likely infected with COVID-19 in-flight—and that 12 of them had sat within a couple of rows of a single symptomatic passenger in business class. (The results were published this month in the U.S. Centers for Disease Control and Prevention’s journal Emerging Infectious Diseases.) Most of these flights occurred early on in the pandemic, however, and in the case of the March 1 flight, masks were likely not worn, the researchers wrote. Meanwhile a recent Department of Defense study modeled the risk of in-flight infection using mannequins exhaling simulated virus particles and found that a person would have to be exposed to an infectious passenger for at least 54 hours to get an infectious dose. This finding assumes the infected passenger is wearing a surgical mask, however, and it does not account for the dangers involved in removing the mask for meals or talking or in moving about on the plane.
With flying, COVID risk really comes down to how closely one sits to other people and for how long, whether or not everyone is wearing a mask, and how infectious any passengers are at the time. If you happen to be seated close to a person who is actively “shedding” the virus, especially if it is a long flight or that person is not wearing a mask, there is a higher chance that you will get the disease. But if you are seated relatively far from others, and everyone is wearing a mask, your risk is probably fairly low. Being in a crowded airport or taking a taxi to get there could be a bigger concern, though.
Subways and Trains
This past spring the novel coronavirus tore through New York City, reaching a peak of thousands of new cases per day in April. Initially some researchers blamed the metropolis’s bustling subway—which carried 5.5 million commuters on a typical pre-COVID weekday—for making it the epicenter of the pandemic in the U.S. But later reviews of the evidence suggest mass transit systems have not been major drivers of viral spread. In August the New York Times asked several international transportation agencies whether any superspreading events had been linked to mass transit, and they said none had. A September report by the American Public Transportation Association (APTA) examined the coronavirus’s spread in cities around the globe that had robust public transit systems. It found no correlation between mass transit use and transmission of the virus. Many of the cities in question required commuters to wear masks.
The APTA report suggested that commuters should reduce risk by wearing a mask and staying six feet apart and that train cars should be well ventilated. Most major cities’ subways and trains constantly recirculate a mixture of fresh air and older air, both of which are pulled through a filter rated on the MERV (minimum efficiency reporting value) scale. MERV-13 filters are less efficient than HEPA filters, but the U.S. Environmental Protection Agency recommends them for reducing the number of airborne viral particles. A number of subway and train systems use lower-rated MERV filters that are still capable of at least reducing the volume of coronavirus in the air. The APTA report notes that on most systems’ subway cars and buses, new air replaces stale air almost 18 times an hour. Passenger train operator Amtrak claims to cycle air through cabins 44 times an hour. But even with fresh air available, an infected passenger can still transmit the coronavirus to someone who is sitting in close proximity—so riders should attempt to space themselves out and avoid densely packed cars whenever possible. Mask wearing reduces risk and is now mandatory on many forms of transit in the U.S., including New York City’s subway and bus systems, as well as Amtrak trains throughout the country. Finally, experts suggest that limiting the length of a ride can also be helpful: 15 minutes on a subway is safer than a multihour train trip.
Buses
Many buses have HVAC (heating, ventilating and air-conditioning) systems similar to those on subways and trains, with one additional factor: buses are more likely to have windows that open. Windows, as well as open ceiling vents, allow fresh air to enter the vehicle. In addition, in a city system (as opposed to a long-distance ride), buses make frequent stops. Doing so allows outside air to flood in each time the doors open. In a case study of a bus in China, a passenger with the novel coronavirus was able to infect many other riders, including those seated up to seven rows away. There seemed to be less risk of transmission, however, for people who were seated near windows and doors that could open.
In addition to snagging window seats, bus riders should look for the same safety features they would on a subway: a mask requirement, good ventilation and adequate spacing between passengers. Limiting rides to short trips may also be helpful. Some bus companies have installed clear partitions between drivers and passengers. Doing so might limit the spread of virus-carrying droplets from coughing, sneezing, breathing or speaking but is unlikely to prevent viral transmission via smaller airborne particles.
Taxis, Ubers and Lyfts
Before the pandemic, many people would not think twice about ordering a ride-share car or hailing a taxi. But now the idea of being in such an enclosed space with a stranger can seem like a life-and-death proposition. It is nearly impossible for drivers and passengers to remain six feet apart in a car, so transmission is definitely possible. An NEJM study in March reported on a Thai taxi driver who fell ill and tested positive for the coronavirus after driving some tourists who had been coughing but wearing a mask. Reliable data on the frequency of COVID transmission in cars is lacking, however.
Keeping the windows open and making sure the air system is set to take in outside air instead of recycling it should reduce the risk. Wearing a mask also probably helps, and ride-share companies Uber and Lyft both require drivers and passengers to do so. These companies have also stopped offering car pools (except in Uber’s case for people who work together), so passengers are only exposed to the driver and anyone in their own party. Some drivers have even installed a plastic shield between the front and back seats. But as is the case with buses, these barriers would likely only protect against larger droplets, not aerosols. Shorter rides—especially those under roughly 15 minutes—pose a lower risk than long ones. And keeping conversation to a minimum could also reduce the danger because talking is known to release aerosols that can spread the virus. If the rate of community transmission in your region is low, chances are that taking the occasional taxi or ride-share car is not a huge risk—provided you wear a mask and keep the windows open as much as possible.
Read more about the coronavirus outbreak from Scientific American here. And read coverage from our international network of magazines here.


scientificamerican--2020-11-20--Clues to Puebloan History Drip Away in Melting Ice Caves

Researchers have discovered charcoal dating back almost 2,000 years in New Mexican ice caves—providing physical evidence that ancestral Puebloans used the ice deposits for drinking water during droughts .
Scientists are working fast to sample cores from the deposits , which likely formed thousands of years ago .
Rising global temperatures have made it warm enough that the ice is beginning to disappear from the sites at El Malpais National Monument .
“ Our wish is to try and find as much climate information as possible , because this ice is going away , ” said Bogdan Onac , the @ @ @ @ @ @ @ better understand the collected ice cores , Onac worked with a team that included University of South Florida researchers and National Park Service archaeologists .
Evidence in the ice cores suggests the caves were used more intensely during periods of drought , based primarily on data from tree rings in the southwest , according to the report , published Wednesday in Scientific Reports .
The cores also provide valuable information for scientists interested in understanding weather patterns in New Mexico and the surrounding region .
That includes the severity and cause of local @ @ @ @ @ @ @ common tool to study historical weather patterns , can provide a rough look at the amount of moisture in a place each year .
But ice contains oxygen and hydrogen isotopes that can mark how precipitation in the region would have been influenced by La Niña and monsoon season , indicating shifts in regional weather patterns .
This information can be used to better prepare scientists for a region rapidly being altered by climate change .
Kenny Bowekaty , a longtime archaeologist , tour guide and member of the Ashiwi people in the @ @ @ @ @ @ @ people in the region who were around during the study period likely would have used the caves for religious purposes , as well as to store animals hunted nearby .
“ Ice to the Ashiwi people is still a resource of life , ” Bowekaty said .
“ There ’ s a lot of compounded uses for what would have been considered ice caves.
” Bowekaty said that one group of people in particular ventured north from the present-day Zuni settlement and into more mountainous regions , where ice would have been more @ @ @ @ @ @ @ the caves would have been one of the few places to find ice in the summer months .
As late as the early 1900s , Ashiwi people made pilgrimages to the caves for religious purposes , Bowekaty said .
But in recent years , the sites have become more restricted as the ice melts .
Now , archaeologists are working against the clock to preserve information from the historic deposits before it ’ s too late .
“ With the ice caves and the water resources , I think everyone is aware of @ @ @ @ @ @ @ .
“ It ’ s the behavior of our planet , our mother , and of course , like life and like all of us , she is still growing , and so how far we have as a planet is not known.
” Reprinted from Climatewire with permission from E & E News .
E & E provides daily coverage of essential energy and environmental news at www.eenews.net .

Researchers have discovered charcoal dating back almost 2,000 years in New Mexican ice caves—providing physical evidence that ancestral Puebloans used the ice deposits for drinking water during droughts.
Scientists are working fast to sample cores from the deposits, which likely formed thousands of years ago. Rising global temperatures have made it warm enough that the ice is beginning to disappear from the sites at El Malpais National Monument.
“Our wish is to try and find as much climate information as possible, because this ice is going away,” said Bogdan Onac, the lead researcher on the study.
To better understand the collected ice cores, Onac worked with a team that included University of South Florida researchers and National Park Service archaeologists.
Evidence in the ice cores suggests the caves were used more intensely during periods of drought, based primarily on data from tree rings in the southwest, according to the report, published Wednesday in Scientific Reports.
The cores also provide valuable information for scientists interested in understanding weather patterns in New Mexico and the surrounding region. That includes the severity and cause of local droughts.
Tree ring information, a common tool to study historical weather patterns, can provide a rough look at the amount of moisture in a place each year. But ice contains oxygen and hydrogen isotopes that can mark how precipitation in the region would have been influenced by La Niña and monsoon season, indicating shifts in regional weather patterns.
This information can be used to better prepare scientists for a region rapidly being altered by climate change.
Kenny Bowekaty, a longtime archaeologist, tour guide and member of the Ashiwi people in the nearby Pueblo of Zuni, said the people in the region who were around during the study period likely would have used the caves for religious purposes, as well as to store animals hunted nearby.
“Ice to the Ashiwi people is still a resource of life,” Bowekaty said. “There’s a lot of compounded uses for what would have been considered ice caves.”
Bowekaty said that one group of people in particular ventured north from the present-day Zuni settlement and into more mountainous regions, where ice would have been more common. When they came back, the caves would have been one of the few places to find ice in the summer months.
As late as the early 1900s, Ashiwi people made pilgrimages to the caves for religious purposes, Bowekaty said. But in recent years, the sites have become more restricted as the ice melts.
Now, archaeologists are working against the clock to preserve information from the historic deposits before it’s too late.
“With the ice caves and the water resources, I think everyone is aware of global warming here,” Bowekaty said. “It’s the behavior of our planet, our mother, and of course, like life and like all of us, she is still growing, and so how far we have as a planet is not known.”
Reprinted from Climatewire with permission from E&E News. E&E provides daily coverage of essential energy and environmental news at www.eenews.net.