VOA慢速英语2011--Political Data Miners Really Get to Know You

THIS IS AMERICA - Political Data Miners Really Get to Know You

 

STEVE EMBER: Welcome to THIS IS AMERICA in VOA Special English. I'm Steve Ember.

BARBARA KLEIN: And I'm Barbara Klein. This week on our program, we look at how political campaigns use data mining to try to predict how people will vote.

(MUSIC)

STEVE EMBER: Andrew Dreschler is a data miner. He works with millions of pieces of information. He looks for details about people -- what neighborhood they live in, what they buy, what they like to do on their weekends.

Mr. Dreschler's job is to collect enough details to form a sense of how people think. That also makes him a little like a storyteller.

ANDREW DRESCHLER: "One art that we've sort of been working on perfecting over the years is telling a story with information."

Data miners look for connections in the ways that people behave. What they find can help stores decide which items to put closer together. It can help advertisers target their messages. And it can help political campaigns know where to look for votes.

A campaign worker puts up signs for former Republican presidential candidate John McCain in Nashville, Tennessee in 2008

BARBARA KLEIN: The use of data mining in politics is nothing new. Michael Kazin, a Georgetown University history professor, gives an example in his book "A Godly Hero." The example involves William Jennings Bryan.

Bryan ran for president of the United States in eighteen ninety-six, nineteen hundred and again eight years later. His supporters wrote him letters -- thousands and thousands of letters. His brother Charles read them all.

Professor Kazin writes: "Charles jotted¹ down every bit of information he could find about a correspondent: party affiliation², job, religion, even income. He updated the file constantly for the next thirty years and used it to send out regular mailings to the Bryan network. The index contained some 200,000 names in 1897 and grew to half a million by 1912."

STEVE EMBER: In other words, Charles was data mining. He was trying to learn what kind of people supported his brother for president. Then he used the data to guide the campaign.

Did it work well? Apparently³ not well enough. Bryan lost every time.

These days campaigns hire professional data miners. They usually work for one party, either the Democrats⁵ or the Republicans.

Andrew Dreschler is vice⁶ president of Strategic Telemetry, a company in Washington that works with Democrats. In two thousand eight, Strategic Telemetry worked for Barack Obama's presidential campaign. Mr. Dreschler says his company began by looking for the easiest data to find.

ANDREW DRESCHLER: "To be quite honest, when we first started working for Obama we were only using census⁷ data."

BARBARA KLEIN: The Census Bureau provides basic information about an area. It could be a small neighborhood or a whole state. The population data includes things like racial and ethnic⁸ percentages, average income, education levels and how many people are married or have children.

Data miners then match this information with public voting records of people who live in that area. Mr. Dreschler says this information is known as the voter file.

ANDREW DRESCHLER: "When we get the voter file it's usually first name, last name, address, phone number, and then vote history, is the typical information in the voter file."

Voter files will not say who a person voted for, but they will say whether or not the person voted.

Data miners also buy information from companies.

ANDREW DRESCHLER: "You know, what type of magazine do people read, what kind of car do they drive, do they rent, do they own, do they have pets, what sort of pet do they have?”

Each detail is a data point, and by the time data miners are finished, they can know a lot about a person.

ANDREW DRESCHLER: "So we will have close to a thousand data points on the voters."

(MUSIC)

BARBARA KLEIN: Data mining sounded like fun, so we decided⁹ to try it ourselves. We went out on the street here in Washington and found a woman sitting on a bench. We asked her age.

WOMAN: "Thirty-three."

Where she lives.

WOMAN: "Maryland."

What she buys at the grocery store.

WOMAN: "I always buy this herbed mixed green salad."

And a few other questions.

REPORTER: "Do you have any pets?"

WOMAN: "I don’t."

REPORTER: "You said you’re married?"

WOMAN: "Yes."

REPORTER: "Kids?"

WOMAN: "No kids."

REPORTER: "Do you have a car?"

WOMAN: "Yes."

REPORTER: "What kind?"

REPORTER: "It’s a Lexus."

And now for our big question. Based on this information, what would a political data miner think?

REPORTER: "Do you think they think that you’re a Democrat⁴ or a Republican?"

WOMAN: "Based on what I said? Um, wow. Based on what I said, I might be a Republican."

REPORTER: "And are you, can you tell me?"

WOMAN: "No, I’m not."

STEVE EMBER: So why does she say a data miner might think she was a Republican? Is it because she drives a Lexus, an expensive car? Judging people's politics by the kind of cars they drive may not be meaningless.

Americans in the top twenty percent of income are more likely to vote for Republicans -- more likely, but not always. Andrew Dreschler says data mining is not as simple as finding one important piece of information.

ANDREW DRESCHLER: "There's not one silver bullet. There’s not, 'If we can just find the cat owners or the bourbon drinkers, we can win this election.'"

Those examples may sound silly. But look at it another way. Why would a candidate for local dog catcher want to waste time and money reaching out to cat owners?

BARBARA KLEIN: Andrew Dreschler says instead of focusing on one person or one data point, data miners look for patterns across large groups. They are not trying to learn everything about you. They are looking for ten thousand people like you. To do that, they use math, maps and machines.

Mr. Dreschler's office is so small, if he sneezed, his two employees would probably have to go home sick.

ANDREW DRESCHLER: "Our office is relatively¹⁰ small. But, it’s, in our server room we have power -- our IT director one time said we have enough power to power a company of three thousand employees.”

Let’s say there are four million people in a state -- a state like Kentucky. Mr. Dreschler and his employees cannot talk to all of them. But they can use an automated¹¹ telephone service to call ten thousand of them and ask who they plan to vote for. The data miners add those responses to the other data points they have about those people.

From there, they use their computer power to crunch¹² the numbers. The goal is to predict how the people they did not talk to -- the other three million nine hundred ninety thousand -- will vote.

ANDREW DRESCHLER: "Whatever question that we ask we can model and show the likelihood of every voter in the state supporting -- er, responding to that question as if we did talk to everybody.

STEVE EMBER: So how do campaigns use that information? Mr. Dreschler stands up and gets a framed map off the wall.

ANDREW DRESCHLER: "Our offices are no-thrills offices, but this is just a -- we do have a couple maps and campaigns that we’re particularly proud of."

The map shows Iowa in the Midwest, the American heartland. Iowa traditionally votes at the beginning of the presidential nominating season.

Some areas of the map are colored in purple. These are the areas that Mr. Dreschler's team thought would support Barack Obama. The gray areas on the map are those they thought would probably not support him. Campaign officials used these maps to help them decide where to advertise and send volunteers.

ANDREW DRESCHLER: "This is just a good example of a visual that we often give to campaigns just showing -- mapping -- where the support is, generally speaking. A lot of times campaigns know that, but it just helps illustrate¹³ where they need to focus and where, frankly¹⁴, they don’t need to focus."

Dianne Bystrom at Iowa State University studies political campaigns. She says that in Iowa, the Obama campaign used data to target people who did not usually vote in the political meetings called caucuses¹⁵. By bringing in new voters, she says, Mr. Obama was able to win that state.

DIANNE BYSTROM: "And that's what really won him the caucuses. He turned out more than the traditional Democratic caucus-going base in the state of Iowa."

BARBARA KLEIN: Ms. Bystrom is director of the Carrie Chapman Catt Center for Women and Politics at Iowa State. She points to the way Ronald Reagan's campaign used data in nineteen eighty when he ran for president. She says the data helped his Republican team target messages to women based on the issues most important to them.

That year, women voted at a greater rate than men for the first time. That change helped close the “gender gap” and send Ronald Reagan to the White House. Professor Bystrom says stories like this show the value of data, even if some people might be concerned about personal privacy.

DIANNE BYSTROM: "For the campaigns, I think they're very powerful. For the voter, and the consumer, I think as technology gets more sophisticated, it gets a little creepy."

BARBARA KLEIN: Daniel Kreiss is an assistant professor at the University of North Carolina.

Professor Kreiss says campaigns will increasingly combine information about what people do online and offline in their everyday life.

DANIEL KREISS: "What I think is starting to happen now and is genuinely new -- although there were the first steps in this direction in two thousand eight -- is the increasing ability of campaigns now to sync their general voter databases with online user data that they’re getting from other sources."

STEVE EMBER: Privacy is not the only concern that some people have about political data mining. Professor Kreiss says some scholars worry that it will harm democracy. They say highly targeted campaigning means fewer people will hear a candidate’s message.

Andrew Dreschler, the data miner in Washington, thinks just the opposite.

ANDREW DRESCHLER: "I would take that argument and turn it around. You are talking to those who are most interested and most likely to participate and those who should really have the information."

(MUSIC)

BARBARA KLEIN: Our program was written by Kelly Nuxoll. We welcome your comments at voanews.cn. You can also find a transcript¹⁶ and MP3 of our program and a PDF version for e-readers. I'm Barbara Klein.

STEVE EMBER: And I'm Steve Ember. Join us again next week for THIS IS AMERICA in VOA Special English.

1 jotted   

v.匆忙记下( jot的过去式和过去分词 )；草草记下，匆匆记下

参考例句：

• I jotted down her name. 我匆忙记下了她的名字。 来自《简明英汉词典》
• The policeman jotted down my address. 警察匆匆地将我的地址记下。 来自《现代英汉综合大词典》

2 affiliation   

n.联系，联合

参考例句：

• There is no affiliation between our organization and theirs,even though our names are similar.尽管两个组织的名称相似，但我们之间并没有关系。
• The kidnappers had no affiliation with any militant group.这些绑架者与任何军事组织都没有紧密联系。

3 apparently   

adv.显然地；表面上，似乎

参考例句：

• An apparently blind alley leads suddenly into an open space.山穷水尽，豁然开朗。
• He was apparently much surprised at the news.他对那个消息显然感到十分惊异。

4 democrat   

n.民主主义者，民主人士；民主党党员

参考例句：

• The Democrat and the Public criticized each other.民主党人和共和党人互相攻击。
• About two years later,he was defeated by Democrat Jimmy Carter.大约两年后，他被民主党人杰米卡特击败。

5 democrats   

n.民主主义者，民主人士( democrat的名词复数 )

参考例句：

• The Democrats held a pep rally on Capitol Hill yesterday. 民主党昨天在国会山召开了竞选誓师大会。
• The democrats organize a filibuster in the senate. 民主党党员组织了阻挠议事。 来自《简明英汉词典》

6 vice   

n.坏事；恶习；[pl.]台钳，老虎钳；adj.副的

参考例句：

• He guarded himself against vice.他避免染上坏习惯。
• They are sunk in the depth of vice.他们堕入了罪恶的深渊。

7 census   

n.(官方的)人口调查，人口普查

参考例句：

• A census of population is taken every ten years.人口普查每10年进行一次。
• The census is taken one time every four years in our country.我国每四年一次人口普查。

8 ethnic   

adj.人种的，种族的，异教徒的

参考例句：

• This music would sound more ethnic if you played it in steel drums.如果你用钢鼓演奏，这首乐曲将更具民族特色。
• The plan is likely only to aggravate ethnic frictions.这一方案很有可能只会加剧种族冲突。

9 decided   

adj.决定了的，坚决的；明显的，明确的

参考例句：

• This gave them a decided advantage over their opponents.这使他们比对手具有明显的优势。
• There is a decided difference between British and Chinese way of greeting.英国人和中国人打招呼的方式有很明显的区别。

10 relatively   

adv.比较...地，相对地

参考例句：

• The rabbit is a relatively recent introduction in Australia.兔子是相对较新引入澳大利亚的物种。
• The operation was relatively painless.手术相对来说不痛。

11 automated   

a.自动化的

参考例句：

• The entire manufacturing process has been automated. 整个生产过程已自动化。
• Automated Highway System (AHS) is recently regarded as one subsystem of Intelligent Transport System (ITS). 近年来自动公路系统(Automated Highway System,AHS)，作为智能运输系统的子系统之一越来越受到重视。

12 crunch   

n.关键时刻；艰难局面；v.发出碎裂声

参考例句：

• If it comes to the crunch they'll support us.关键时刻他们是会支持我们的。
• People who crunch nuts at the movies can be very annoying.看电影时嘎吱作声地嚼干果的人会使人十分讨厌。

13 illustrate   

v.举例说明，阐明；图解，加插图

参考例句：

• The company's bank statements illustrate its success.这家公司的银行报表说明了它的成功。
• This diagram will illustrate what I mean.这个图表可说明我的意思。

14 frankly   

adv.坦白地，直率地；坦率地说

参考例句：

• To speak frankly, I don't like the idea at all.老实说，我一点也不赞成这个主意。
• Frankly speaking, I'm not opposed to reform.坦率地说，我不反对改革。

15 caucuses   

n.（政党决定政策或推举竞选人的）核心成员( caucus的名词复数 )；决策干部；决策委员会；秘密会议

参考例句：

• Republican caucuses will happen in about 410 towns across Maine. 共和党团会议选举将在缅因州的约410个城镇进行。 来自互联网

16 transcript   

n.抄本，誊本，副本，肄业证书

参考例句：

• A transcript of the tapes was presented as evidence in court.一份录音带的文字本作为证据被呈交法庭。
• They wouldn't let me have a transcript of the interview.他们拒绝给我一份采访的文字整理稿。