科学美国人60秒 SSS 误导地区情绪分析的3个单词（在线收听）

This is Scientific American's 60-second Science. I'm Karen Hopkin.

这里是科学美国人——60秒科学。我是凯伦·霍普金。

You can tell a lot about people's general state of mind based on their social media feeds.

你可以依据人们的社交媒体信息判断出他们的到他们的一般心理状态。

Are they always tweeting about their biggest peeves or posting pics of particularly cute kitties?

他们经常发帖吐槽自已最讨厌的事吗？还是会发特别可爱的猫咪的图片？

Well, in a similar fashion, researchers are turning to Twitter for clues about the overall happiness of entire geographic communities.

研究人员正在以类似方式，在推特上寻找关于整个地理社区的整体幸福感的线索。

What they're finding is that regional variation in the use of common phrases produces predictions that don't always reflect the local state of well-being.

他们发现，常用短语使用的地区差异产生的预测并不总是反映当地的幸福状况。

But removing from their analyses just three specific terms—good, love and LOL—greatly improves the accuracy of the methods.

但只要从他们的分析中去掉“good”（好）、“love”（爱）和“LOL”（大笑）三个特定词语，研究就去的准确性就能大大提高。

"We're living in a crazy COVID-19 era.

“我们生活在疯狂的新冠肺炎时代。

And now more than ever, we're using social media to adapt to a new normal and reach out to the friends and family that we can't meet face-to-face."

如今我们比以往任何时候都更频繁地利用社交媒体来适应新常态，接触我们无法面对面见到的朋友和家人。”

Kokil Jaidka studies computational linguistics at the National University of Singapore.

在新加坡国立大学研究计算机语言学的科基尔·贾伊德卡说到。

"But our words aren't useful just to understand what we, as individuals, think and feel.

“但我们的词语并不仅仅用来理解我们个人的想法和感受。

They're also useful clues about the community we live in."

同时也是有关我们所居住社区的有用线索。”

One of the simpler methods that many scientists use to parse the data involves correlating words with positive or negative emotions.

许多科学家用于分析数据的更简单方法之一是，将单词与积极或消极情绪关联起来。

But when those tallies are compared with phone surveys that assess regional well-being,

但贾伊德卡表示，在与评估地区幸福感的电话调查结果进行比较时，

Jaidka says, they don't paint an accurate picture of the local zeitgeist.

这些记录无法准确描述出当地的时代精神。

To find out why, Jaidka and her colleague Johannes Eichstaedt of Stanford University analyzed billions of tweets from around the United States.

为了找出原因，贾伊德卡和同事——斯坦福大学的约翰尼斯·艾希施泰德——分析了全美数十亿条推文。

And they found that among the most frequently used terms on Twitter are LOL, love and good.

他们发现推特上使用频率最高的词汇包括“LOL”、“love”和“good”。

"And they actually throw the analysis off.

“这三个词与分析结果脱沟。

In fact, when we removed these three words alone, we managed to improve upon the simpler word-counting methods—

事实上，单独将这三个词去掉后，我们就改进了这种更简单的词语计算方法，

and obtain better, if not perfect, estimates of happiness."

并获得了更好的——也许不是完美的——幸福评估结果。”

Why the disconnect? Well, Jaidka says one issue is...

为何会脱沟？贾伊德卡表示，一个问题是……

"Internet language is really a different beast than regular spoken language.

“网络语言和常规口语真的不同。

We've adapted words from the English vocabulary to mean different things in different situations."

我们已将英语词汇中的单词改编成在不同的情况下表示不同的意思。”

Take, for example, LOL.

以LOL为例。

"I've tweeted the word LOL to flirt, express irony, annoyance and sometimes just pure surprise.

“我在推特上用LOL这个词来调情，表达讽刺、烦恼，有时只纯粹表示惊喜。

When the methods for measuring LOL as a marker of happiness were created in the 1990s, it still meant laughing out loud."

而在上世纪90年代人们将LOL作为衡量开心的标志时，这个词还只有‘放声大笑’的意思。”

There are plenty of terms that are less misleading, says Eichstaedt.

艾希施泰德表示，有很多误导性较小的词语。

"Our models tell us that words like excited, fun, great, opportunity, interesting, fantastic and those are better words for measuring subjective well-being, just looking at the data."

“我们的模型显示，仅从数据来看，‘兴奋’、‘好玩’、‘巨大的’、‘机会’、‘有趣’、‘奇妙’等词更适合衡量主观幸福感。”

Their work appears in the Proceedings of the National Academy of Sciences.

他们的研究成果发表在《美国国家科学院院刊》上。

Being able to get an accurate read on the mood of the population is no laughing matter.

能够准确解读人们的情绪并非无关紧要。

"That's particularly important now, in the time of COVID, where we're expecting a mental health crisis—

“这在如今的新冠肺炎时代尤为重要，我们预计会出现一场精神健康危机，

and we're already seeing in survey data the largest diminishment in subjective well-being in 10 years at least, if not ever."

而且我们已经在调查数据中看到了个人幸福感下降，降幅即使不是有史以来最大的，也至少是10年来最大的。”

No doubt we could all use more fantastic opportunities for great fun and excitement—give or take the LOL.

毫无疑问，我们都可以利用更多“奇妙的”“机会”来获得“巨大的”“乐趣”和“刺激”，多少都会‘放声大笑'。

Thanks for listening for Scientific American's 60-second Science. I'm Karen Hopkin.

谢谢大家收听科学美国人——60秒科学。我是凯伦·霍普金。