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  3. One day in 2008 an anonymous Twitter user posted a message: "I am certainly not bored, way busy! feel great!" That is all well a

One day in 2008 an anonymous Twitter user posted a message: "I am certainly not bored, way busy! feel great!" That is all well a

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问题     One day in 2008 an anonymous Twitter user posted a message: "I am certainly not bored, way busy! feel great!" That is all well and good, one might think, but utterly uninteresting to anyone besides the author and, perhaps, a few friends. Not so, according to Johan Bollen, of Indiana University Bloomington, who collected the tweet, along with plenty of others sent that day. All were rated for emotional content. Many proved similarly chirpy, scoring high on confidence, energy and happiness. Indeed, Dr Bollen reckons, on the day the tweet was posted, America’s collective mood perked up a notch. When he and his team examined all the data for the autumn and winter of 2008, they found that Twitter users’ collective mood swings coincided with national events. Happiness shot up around Thanksgiving, for example.
    The idea of tapping web-based data to build a real-time measure of users’ emotions and preferences is not new. Nor is that of using the results to predict their behaviour. Interest in internet forecasting was sparked by a paper published in 2009 by Hal Varian, Google’s chief economist. He found that the peaks and troughs in the volume of Google searches for certain products, such as cars and holidays, preceded fluctuations, in sales of those products. Other researchers have shown that searches for job-related terms are a good predictor of unemployment rates and that mentions of political candidates on Twitter correlate with electoral outcomes.
    Dr Bollen spotted another curious correlation. When he compared trends in the national mood with movements of the Dow Jones Industrial Average(DJIA)he noticed that changes in one of the mood measure’s seven components, anxiety, predicted swings in the share-price index. Spikes in anxiety levels were followed, around three days later, by dips in the price of shares. Why this happens remains unclear, but one possible explanation is that the falling prices were caused by traders’ tendency to exit risky positions when feeling strung up.
    Dr Bollen’s algorithm, which he described in a paper published in February in the Journal of Computational Science, has been licensed to Derwent Capital Markets, a hedge fund based in London. Derwent will use it to help guide the investments made with a ￡25m($41m)fund that the firm hopes to launch in the next few months. Other funds are rumoured to be using similar tricks already.
    All such initiatives face a problem, though. Humans excel at extracting meaning and sentiment from even the tiniest snippets of text, a task that stumps machines. To a computer, a tweet that reads "Feeling joyful after my trip to the dentist. Yeah, really" says that the author has been to the dentist and is now happy. Researchers have recently made strides in teaching machines to recognise such sarcasm, as well as double meanings or cultural references.
We can infer from Paragraph 1 that______.
选项 A、Dr. Bollen has examined all the Internet data of 2008 to do his research
B、tweets of one person is totally boring, even to his/her friends
C、one Tweeter user’s feeling cannot represent America’s collective mood
D、Tweeter users’ mood in the second half of 2008 accorded with the country’s events
答案D
解析 属信息推断题。选项A犯了夸大其词的错误，第一段最后一句只提到了J．B．及其团队研究了2008年秋天和冬天的微博数据，而非所有网络数据，故选项A错误。选项B犯了偷梁换柱的错误，第一段第二句提到，只有作者本人，可能还有一些他的朋友对这条微博感兴趣，而不是所有人都对其没兴趣，故选项B错误。选项C犯了无中生有的错误，文中并无此意，故错误。选项D可通过第一段倒数第二句推知，故选项D符合题意。
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