The traces we leave on the Web and on our digital devices can give advertisers and others surprising, and sometimes disturbing, insights into our psychology.
圖片來源：Laurence Dutton Getty Images
·Users’digital footprints disclose certain preferences and characteristics, such as their personality or mood.
·Companies are very interested in such data. Automated language analysis is already being used in the hiring of personnel. And advertising seems to be more successful when its message is adapted to the personality or mood of the customer.
·These technological advances open opportunities not only for commerce but for public health. Among those possibilities: smartphone apps may in the future recognize when a bipolar patient is slipping into a depressive phase and can inform the person’s physician.
·But the technology poses risks. Unless it is managed carefully and ethically, it can invade privacy.
If you enjoy computerized personality tests, you might consider visiting Apply Magic Sauce (https://applymagicsauce.com). The Web site prompts you to enter some text you have written—such as e-mails or blogs—along with information about your activities on social media. You do not have to provide social media data, but if you want to do it, you either allow Apply Magic Sauce to access your Facebook and Twitter accounts or follow directions for uploading selected data from those sources, such as your history of pressing Facebook’s “like” buttons. Once you click “Make Prediction,” you will see a detailed psychogram, or personality profile, that includes your presumed age and sex, whether you are anxious or easily stressed, how quickly you give in to impulses, and whether you are politically and socially conservative or liberal.
如果你對線上性格測試感興趣，可以試著訪問Apply Magic Sauce（由劍橋大學開發的知名個性化分析引擎，地址：https://applymagicsauce.com)。該網站會提示你輸入一些你寫過的文字——比如電子郵件或博客——以及你在社交媒體上的狀態信息。你不需要提供社交媒體數據，但如果你想這么做，你要么允許Apply Magic Sauce訪問你的Facebook和Twitter賬戶，要么按照指示從這些來源上傳選定的數據，比如你在Facebook上點贊的歷史。一旦點擊“做出預測”，你就會看到一個詳細的心理記錄表或者是個性簡介，包括你的預判年齡和性別，你是焦慮還是易緊張，你在失去理智情況下做出沖動行為的速度，你在政治和社會上是保守派還是自由派……
Examining the psychological profile that the algorithm derives from your online traces can certainly be entertaining. On the other hand, the algorithm’s ability to draw inferences about us illustrates how easy it is for anyone who tracks our digital activities to gain insight into our personalities—and potentially invade our privacy. What is more, psychological inferences about us might be exploited to manipulate, say, what we buy or how we vote.
It seems that our like clicks by themselves can be pretty good indicators of what makes us tick. In 2015 David Stillwell and Youyou Wu, both at the University of Cambridge, and Michal Kosinski of Stanford University demonstrated that algorithms can evaluate what psychologists call the Big Five dimensions of personality quite accurately just by examining a Facebook user’s likes. These dimensions—openness to experience, conscientiousness, extroversion, agreeableness and neuroticism—are viewed as representing the basic dimensions of personality. The degree to which they are present in individuals describes who those people are.
似乎點贊這一行為本身就能很好地表明是什么驅使我們“點贊”。2015年，劍橋大學的戴維•斯蒂威爾(David Stillwell)和吳友友( Youyou Wu)，以及斯坦福大學的米哈爾•科辛斯基(Michal Kosinski)證明，算法可以通過檢測Facebook用戶的點贊來準確評估心理學家所說的“五大人格論”，即開放性、責任心、外傾性、親和性和神經質，通過這些人格特征在個體中的表現程度可以描述他（她）是什么樣的人。
The researchers trained their algorithm using data from more than 70,000 Facebook users. All the participants had earlier filled out a personality questionnaire, and so their Big Five profile was known. The computer then went through the Facebook accounts of these test subjects looking for likes that are often associated with certain personality characteristics. For example, extroverted users often give a thumbs-up to activities such as “partying” or “dancing.” Users who are especially open may like Spanish painter Salvador Dalí.
Then the investigators had the program examine the likes of other Facebook users. If the software had as few as 10 for analysis, it was able to evaluate that person about as well as a co-worker did. Given 70 likes, the algorithm was about as accurate as a friend. With 300, it was more successful than the person’s spouse. Even more astonishing to the researchers, feeding likes into their program enabled them to predict whether someone suffered from depression or took drugs and even to infer what the individual studied in school.
The project grew out of work that Stillwell began in 2007, when he created a Facebook app that enabled users to fill out a personality questionnaire and get feedback in exchange for allowing investigators to use the data for research. Six million people participated until the app was shut down in 2012, and about 40 percent gave permission for the researchers to obtain access to their past Facebook activities—including their history of likes.
Researchers around the world became very interested in the data set, parts of which were made available in anonymized form for noncommercial research. More than 50 articles and doctoral dissertations have been based on it, in part because the Facebook data reveal what people actually do when they are unaware that their behavior is the subject of research.
One obvious use for such psychological insights beyond the realm of research is in advertising, as Sandra C. Matz of Columbia University and her colleagues (among them Stillwell and Kosinski) demonstrated in a 2017 paper. The team made use of something that Facebook offers to its business customers: the ability to target advertisements to people with particular likes. They developed 10 different ads for the same cosmetic product, some meant to appeal to extroverted women and some to introverts. One of the “extrovert” ads, for example, showed a woman dancing with abandon at a disco; underneath it the slogan read, “Dance like no one’s watching (but they totally are).” An “introvert” ad showed a young woman applying makeup in front of a mirror. The slogan said, “Beauty doesn’t have to shout.”
哥倫比亞大學的桑德拉 C.馬茨(Sandra C. Matz)和她的同事(其中包括斯蒂威爾和科辛斯基)在2017年的一篇論文中闡述，這種心理學洞見在研究領域之外的一個明顯用途是廣告。該團隊利用Facebook向其商業客戶提供的一項功能：廣告的精準投放。他們為同一種化妝品制作了10個不同的廣告，其中一些旨在吸引外向的女性，另一些則是為了吸引內向的女性。例如，其中一則“外向者”廣告中，一位女士在迪斯科舞廳縱情舞蹈，下面的標語寫著：“孤芳自舞（然眾觀之）。”在一則“內向者”的廣告中，一位年輕女子在鏡子前化妝，標語上則寫著：“美不名狀。”
Both campaigns ran on Facebook for a week and together reached about three million female Facebook users, who received messages that were matched to their personality type or to the opposite of their type. When the ads fit the personality, Facebook viewers were about 50 percent more likely to buy the product than when the ads did not fit.
Advertisers often pursue a different approach: they look for customers who have bought or liked a particular product in the past to ensure that they target people who are already well disposed to their wares. In limiting a target group, it makes sense to take previous consumption into account, Matz says, but this study demonstrated the power of adapting how the message is communicated to a consumer’s personality.
It is a power not lost on marketers. Numerous companies have discovered automated personality analysis and turned it into a business model, boasting about the value it can provide to their customers—although how well the methods used by any individual company actually work is hard to judge.
The now defunct Cambridge Analytica offers an infamous example of how personality profiling based on Facebook data has been applied in the real world. In March 2018 news reports alleged that as early as 2014, the company had begun buying personal Facebook data about more than 80 million users. (Stillwell’s group makes a point of emphasizing that Cambridge Analytica had no access to its data, algorithms or expertise.) The company claimed to specialize in personalized election advertising: the packaging and pinpoint targeting of political messages. In 2016 Alexander Nix, then the company’s CEO, described Cambridge Analytica’s strategy in a presentation in New York City, providing an example of how to convince people who care about gun rights to support a selected candidate. (See a YouTube video of his talk at www.youtube.com/watch?v=n8Dd5aVXLCc.) For voters deemed neurotic (who are prone to worrying), Nix proposed an emotionally based campaign featuring the threat of a burglary and the protective value of a gun. For agreeable people (who value community and family), on the other hand, the approach could feature fathers teaching their sons to hunt.
已倒閉的劍橋分析公司（Cambridge Analytica）就是一個臭名昭著的例子，這個案例展現了基于Facebook數據的個性分析如何在現實世界中得到應用。2018年3月的新聞報道稱，早在2014年，該公司就開始購買約8000多萬用戶的Facebook個人數據（斯蒂威爾的團隊強調，劍橋分析公司無法獲得其數據、算法或專業技術）。該公司聲稱其專注于個性化選舉廣告，即政治信息的包裝和精確定位。2016年，時任公司首席執行官的亞歷山大•尼克斯（Alexander Nix）在紐約的一次演講中描述了劍橋分析公司的戰略并列舉了一個例子，以說明他們如何說服關心槍支權利的人去支持特定候選人。（在YouTube網站上可以看到他的演講視頻：www.youtube.com/watch?v=n8Dd5aVXLCc）對于那些被認為是神經質（容易焦慮）的選民，尼克斯提出了一個基于情感的競選方案，這一方案以宣傳盜竊威脅論和槍支自保論為特點。另一方面，對于那些平易近人（重視社區和家庭）的人來說，這一宣傳的側重可能是父親教兒子打獵。
Cambridge Analytica worked for the presidential campaigns of Ted Cruz and Donald Trump. Nix claimed in his talk that the strategy helped Cruz advance in the primaries, and the company later took some credit for Trump’s victory—although exactly what it did for the Trump campaign and how valuable its work was are in dispute.
Philosopher Philipp Hübl, who, among other things, examines the power of the unconscious, is dubious of the Trump claim. He notes that selling cosmetics costing a few dollars, as in Matz’s study, is very different from swaying voters in an election campaign. “In elections, even undecided voters weigh the possibilities, and it takes more than a few banner ads and fake news to convince them,” Hübl says.
Matz, too, sees limits in what psychological marketing in its current stage of development can accomplish in political campaigns. “Undecided voters in particular may be made more receptive to one or another position,” she says, “but turning a Clintonista into a MAGA voter, well, that was pretty unlikely to happen.” Nevertheless, Matz thinks that such marketing is likely to have some effect on voters, calling the notion that it has no effect “extremely improbable.”
馬茨也看到了當前發展階段心理營銷在政治競選中所能達到的局限。她提到：“尤其是舉棋不定的選民，可能更容易接受一個或另一個候選人。但是把克林頓的支持者變成MAGA（指特朗普標志性的“Make American Great Again”口號，這里代指特朗普）的選民，嗯......這是不太可能發生的。”不過，馬茨認為，這種營銷還是會對選民產生一些影響，說這種營銷沒有效果那是“極不可能的”。
Facebook activity is by no means the only data that can be used to assess your personality. In a 2018 study, computer scientist Sabrina Hoppe of the University of Stuttgart in Germany and her colleagues fitted students with eye trackers. The volunteers then walked around campus and went shopping. Based on their eye movements, the researchers were able to predict four of the Big Five dimensions correctly.
How we speak—our individual tone of voice—may also divulge clues about our personality. Precire Technologies, a company based in Aachen, Germany, specializes in analyzing spoken and written language. It has developed an automated job interview: job seekers speak with a computer by telephone, which then creates a detailed psychogram based on their responses. Among other things, Precire analyzes word selection and certain word combinations, sentence structures, dialectal influences, errors, filler words, pronunciations and intonations. Its algorithm is based on data from more than 5,000 interviews with individuals whose personalities were analyzed.
Precire’s clients include German company Fraport, which manages the Frankfurt Airport, and the international recruitment agency Randstad, which uses the software as a component of its selection process. Andreas Bolder, head of personnel at Randstad’s German branch, says the approach is more efficient and less costly than certain more time-consuming tests.
Software that analyzes faces for clues to mood, personality or other psychological features is being explored as well. It highlights both what is possible and what to fear.
POSSIBILITIES AND PROBLEMS
In early 2018 four programmers at a hacker conference, nwHacks, introduced an app that discerns mood by analyzing face-tracking data captured from the front camera of the iPhone X. The app, called Loki, recognizes emotions such as happiness, sadness, anger and surprise in real time as someone looks at a news feed, and it delivers content based on the person’s emotional state. In an article about Loki, one of the developers said that he and his colleagues created the app to “illustrate the plausibility of social media platforms tracking user emotions to manipulate the content that gets shown to them.” For instance, when a user engages with a news feed or other app, such software could secretly track the person’s emotions and use this “emotion detector” as a guide for targeting advertising. Studies have shown that people tend to loosen their purse strings when they are in a good mood; advertisers might want to push ads to your phone when you are feeling particularly up.
2018年初，在一個名為nwHacks的黑客大會上（nwHacks是Northwest Hacks的縮寫，是在不列顛哥倫比亞大學舉辦的為期兩天的hackathon活動），四個程序員介紹了一個通過iPhone x的前置攝像頭來捕捉面部追蹤數據并進行分析來識別情緒的應用程序——洛基（Loki）。例如通過對某人在閱讀推送新聞時或快樂，或悲傷，或憤怒，或驚喜等情緒的識別，為其推送符合這一情緒狀態的內容。在一篇關于洛基的文章中，其中一名開發者說，他和他的同事開發這款應用，是為了“證明社交媒體平臺追蹤用戶情緒、操縱推送給用戶內容的可行性”。例如，當用戶查看新聞推送或使用其他應用程序時，此類軟件可以秘密跟蹤用戶的情緒，并使用這種“情緒探測器”作為定向廣告的指南。研究表明，人們在心情好的時候往往會放松錢包：因此當你心情特別好的時候，廣告商可能會把廣告推送到你的手機上。
Astonishingly, Loki took just 24 hours to build. In making it, the developers relied on machine learning, a common approach to automated image recognition. They first trained the program with about 100 facial expressions, labeling the emotions that corresponded to each expression. This training enabled the app to “figure out” how facial expression relates to mood, such as, presumably, that the corners of the mouth rise when we smile.
Kosinski, too, has examined whether automated image-recognition technology can surreptitiously discern psychological traits from digital activity. In an experiment published in 2018, he and his Stanford colleague Yilun Wang fed hundreds of thousands of photographs from a dating portal into a computer, along with information on whether the person in question was gay or straight. They then presented the software with pairs of unknown faces: one of a homosexual person and another of a heterosexual individual of the same sex. The program correctly distinguished the sexual orientation of men 81 percent of the time and of women 71 percent of the time; human beings were much less accurate in their assessments.
Given that gay people continue to fear for their lives in many parts of the world, it is perhaps not surprising that the results elicited negative reactions. Indeed, Kosinski got death threats. “People didn’t understand that my intention wasn’t to show how cool it is to predict sexual orientation,” Kosinski says. “The whole paper is actually a warning, a call for increasing privacy.”
By analyzing 83 measuring points on faces, an algorithm correctly identified the sexual orientation of many men based on their photograph in a dating portal. In addition, the program generated supposedly “archetypal straight” (left) and “archetypal gay” (center) faces and calculated how the facial expressions differed on average (right). The researchers say they conducted the study partly to warn that photographs posted on the Internet could be mined for private data. Credit: Yilun Wang and Michal Kosinski; Source: “Deep Neural Networks Are More Accurate Than Humans at Detecting Sexual Orientation from Facial Images,” by Michal Kosinski and Yilun Wang, in Journal of Personality and Social Psychology, Vol. 114, No. 2; February 2018.
通過分析人臉上的83個測量點，一種算法根據約會網站上大量男性的照片，正確地辨認了他們的性取向。此外，該程序還生成了被認為是“原型異性戀”(左)和“原型同性戀”(中)的面孔，并計算出面部表情的平均差異(右)。研究人員說，他們進行這項研究的一部分原因就是為了發出警告——人們發布在互聯網上的照片很可能會被挖掘為私人數據。資料來源：米哈爾·科辛斯基(Michal Kosinski)和王一倫(Yilun Wang)在《人格與社會心理學雜志》(Journal of Personality and Social Psychology, Vol.)上發表的論文《深度神經網絡在從面部圖像判斷性取向方面比人類更準確》(Deep Neural network Are More Accurate Than human)，2018年2月第二期第114冊。
In late 2016 computer scientists at the Swiss Federal Institute of Technology Zurich demonstrated that the personalities of Facebook users can be pinned down more precisely if their likes are coupled with analyses of their profile photograph. Interestingly, the researchers, like many others who use machine-learning software, do not know exactly how the algorithm forms its judgment—for example, whether it relies on such features as a person’s haircut or the formality of the individual’s dress. They are in the dark because machine-learning programs do not reveal the rules they apply in drawing conclusions. The investigators know that the software finds correlations between features in the data and personality but not exactly how it concludes that a man in a photograph is attracted to other men or which characteristics in my e-mail might indicate that I am conscientious and somewhat introverted.
“The image we are often given is that predicting personality is a kind of magic,” says Rasmus Rothe, who was involved in the Swiss study. “But in the final analysis, computer models do nothing other than find correlations.”
The use of facial-recognition technology for analyzing psychology is not merely an object of research. It has been adopted by several commercial enterprises. Israeli company Faception, for example, says it can recognize whether a person has a high IQ or pedophilic tendencies or is a potential terrorist threat.
Even if a correlation is found with a trait, experts have their doubts about the usefulness of such analyses. “All that the algorithms give us are statistical probabilities,” Rothe says. It simply is not possible to identify with certainty whether a person is Mensa material. “What the program can tell us is that someone who looks sort of like you is statistically more likely to have a high IQ. It could easily guess wrong four times out of 10.”
With some applications, incorrect predictions are tolerable. Who cares if Apply Magic Sauce comes to comically erroneous conclusions? But the effect can be devastating in other circumstances. Notably, when the characteristic being analyzed is uncommon, more errors are likely to be made. Even if a company’s computer algorithms were to finger terrorists correctly 99 percent of the time, the false positives found 1 percent of the time could bring harm to thousands of innocent people in populous places where terrorists are rare, such as in Germany or the U.S.
對于某些應用程序，不正確的預測是可以容忍的。誰會在意Apply Magic Sauce是否會得出可笑的錯誤結論呢？但在其他情況下，這種影響則可能是毀滅性的。值得注意的是，當分析的特征不常見時，很可能會產生更多的錯誤。即使一家公司的計算機算法在99%的情況下都能正確識別恐怖分子，但在德國或美國等恐怖分子稀少的人口稠密地區，1%的誤報率也可能給成千上萬的無辜民眾帶來傷害。
LANGUAGE RECOGNITION AND SUICIDE PREVENTION
Of course, automatic psychological assessments can be used to help people live better. Suicide-prevention efforts are emblematic. Facebook has such an initiative. The company had noticed that users on its platform occasionally announce there that they intend to kill themselves. Some have even live streamed their death. An automatic language-processing algorithm is now programmed to report suicide threats to the social network’s contact checkers. If a trained reviewer determines that a person is at risk, the person is shown support options.
Twitter posts might likewise be worth analyzing, according to Glen Coppersmith, a researcher at Qntfy, a company based in Arlington, Va., that combines data science and psychology to creates technologies for public health. Coppersmith has noted that Twitter messages sometimes contain strong evidence of suicide risk and has argued that their use for screening should be seriously considered.
Taking a different tack, University Hospital Carl Gustav Carus in Dresden is using smartphones to measure behavioral changes, looking for those characteristic of severe depression. In particular, it is attempting to determine when patients with a bipolar affective disorder are in a manic or depressive phase (see “Smartphone Analysis: Crash Prevention”).
Even designers of algorithms that are created with good intentions must balance the potential for good against the risk of privacy invasion. Samaritans, a nonprofit organization that aims to help people at risk of suicide in the U.K. and Ireland, found this out the hard way a few years ago. In 2014 it introduced an app that scanned Twitter messages for evidence of emotional distress (for example, “tired of being alone” or “hate myself”), enabling Twitter users to learn whether friends or loved ones were undergoing an emotional emergency. But Samaritans did not obtain the consent of the people whose Tweets were being collected. Criticism of the app was overwhelming. Nine days after the program started, Samaritans shut it down. The Dresden hospital has not made the same mistake: it obtains permission from participants before it monitors their smartphone use.
Automated psychological assessments are becoming a part of the digital landscape. Whether they will ultimately be used mainly for good or ill remains to be seen.
SMARTPHONE ANALYSIS: CRASH PREVENTION
If Jan Smith (a pseudonym) were to spend the morning in bed and miss a class, his absence would definitely sound an alarm. This is because the 25-year-old student has a virtual companion that is pretty well informed about the details of his daily life—when he goes for a walk and where, how often he calls his friends, how long he stays on the phone, and so on. It knows that he sent four WhatsApp messages and two e-mails late last night, one of which contained more than 2,000 keystrokes.
Smith suffers from bipolar disorder, a mental illness in which mood and behavior constantly swing between two extremes. Some weeks he feels so depressed that he can hardly get out of bed or manage the basic tasks of everyday life. Then there are phases during which he is so euphoric and full of energy that he completes projects without seeming to need sleep.
Smith installed a program on his smartphone that records all his activities, including not only phone calls but also his GPS and pedometer readings and when he uses which apps. This information transfers to a server at regular intervals. Smith is taking part in a study coordinated by University Hospital Carl Gustav Carus in Dresden. The goal of the project, known as Bipolife, is to improve the diagnosis and treatment of bipolar disorders. Researchers intend to monitor the smartphones of 180 patients for two years.
They plan to collect moment-to-moment information about each participant’s mental state. Such data should be useful because bipolar patients are often unaware when they are about to have a depressive or manic episode. That was certainly Smith’s experience: “When I was on a high, I threw myself into my work, slept maybe three or four hours, and wrote e-mails to professors at three in the morning. It never occurred to me that this might not be normal. Everyone I knew envied my energy and commitment.”
The smartphone app is meant to send up warning flares. “The transferred data are analyzed by a computer algorithm,” explains Esther Mühlbauer, a psychologist at the Dresden hospital. For example, it recognizes when a participant makes significantly fewer phone calls or suddenly stops leaving the house—or works around the clock, neglecting sleep. “If our program sees that, it automatically e-mails the patient’s psychiatrist,” Mühlbauer says. Then the psychiatrist gets in touch with the patient.
這款智能手機應用意在發出警告信號。德累斯頓醫院的心理學家Esther Muhlbauer解釋說：“傳輸的數據將通過計算機算法進行分析。”例如，當一個參與者打的電話明顯變少，或者突然不出門、不睡覺，它就會識別出來。“如果我們的程序看到這一點，它就會自動給病人的精神科醫生發送電子郵件，隨后精神病醫生與病人將取得聯系。”Esther Muhlbauer說。
The researchers first have to get a baseline, determining, for example, how particular patients use their cell phones during asymptomatic phases. Then the software notes when the behavior deviates from a patient’s norm so that treatment can be given quickly. Smith finds this monitoring very reassuring: “It means that there is always someone there who looks after my condition,” he says. “This can be a significant support, especially for people who live alone.”
注：《最了解你的不是另一半 而是互聯網》來源于Scientific American(http://www.cbdio.com/BigData/2019-03/28/content_6060100.htm)。數據觀王婕/編譯，轉載請注明譯者和來源。