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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.
By mentioning Hal Varian, the author intends to state that______.
选项 A、his finding has aroused thinking and research on predictions based on Internet data
B、his finding has predicted the increase of unemployment rate
C、his finding has forecast the price fluctuation of certain products
D、his finding has been highly valued by being published on newspaper
答案A
解析 属逻辑关系题。通过题干中的人名H．V可迅速定位至文章第二段第三句，H．V的发现激发了人们利用网络进行预测的兴趣，选项A为原文的同义替换，故正确。选项B犯了移花接木的错误，后文中说其他研究者对工作相关项目搜索的研究预测了失业率，而不是H．V的研究，故选项B错误。选项C犯了曲解文意的错误，原文并未提到他预测了某些商品的价格波动，只是发现了人们搜索的商品和相关商品销售的关系，故选项C错误。选项D犯了答非所问的错误，其表述出现在原文中，但并非作者的真正意图，故错误。
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