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15 語数: 398 語 出題校 法政大 5 We are already aware that our every move online is tracked and analyzed. But you 2-53 couldn't have known how much Facebook can learn about you from the smallest of social interactions - a 'like'*. (1) Researchers from the University of Cambridge designed (2) a simple machine-learning 2-54 system to predict Facebook users' personal information based solely on which pages they had liked. E "We were completely surprised by the accuracy of the predictions," says Michael 2-55 Kosinski, lead researcher of the project. Kosinski and colleagues built the system by scanning likes for a sample of 58,000 volunteers, and matching them up with other 10 profile details such as age, gender, and relationship status. They also matched up those likes with the results of personality and intelligence tests the volunteers had taken. The team then used their model to make predictions about other volunteers, based solely on their likes. The system can distinguish between the profiles of black and white Facebook users, 15 getting it right 95 percent of the time. It was also 90 percent accurate in separating males and females, Democrats and Republicans. Personality traits like openness and intelligence were also estimated based on likes, and were as accurate in some areas as a standard personality test designed for the task. Mixing what a user likes with many kinds of other data from their real-life activities could improve these predictions even more. 20 Voting records, utility bills and marriage records are already being added to Facebook's database, where they are easier to analyze. Facebook recently partnered with offline data companies, which all collect this kind of information. This move will allow even deeper insights into the behavior of the web users. 25 30 (3) - Sarah Downey, a lawyer and analyst with a privacy technology company, foresees insurers using the information gained by Facebook to help them identify risky customers, and perhaps charge them with higher fees. But there are potential benefits for users, too. Kosinski suggests that Facebook could end up as an online locker for your personal information, releasing your profiles at your command to help you with career planning. Downey says the research is the first solid example of the kinds of insights that can be made through Facebook. "This study is a great example of how the little things you do online show so much about you,” she says. "You might not remember liking things, " but Facebook remembers and (4) it all adds up.", * a 'like': フェイスブック上で個人の好みを表示する機能。 日本語版のフェイスブックでは「いいね!」 と表記される。 2-56 2-57 2-58 36

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約 万 ha 2.帯グラフ (1) 下の表の3行目の空欄を埋めて、右のサウジアラビアのグラフを完成させよう。 サウジアラビアの品目別輸出額 計2076億ドル [2016年] 品目 石油 石油製品 [プラスチック 衣類 43 4.8 輸出額(億ドル) 1361 237 142 機械類 2兆4942億ドル 43.8% 輸出額に占める割合(%) [2018年] 11.4 6.8 (2)作業 右のグラフから一次産品 (加工されていない農産物や 鉱産資源などのこと)を探して青色で塗ろう。 (3)次の①~④の文から, グラフを読み取った内容として適当で ないものを選ぼう。 その他 41.3 金属製品 3.8 医薬品 63 精密機械 4.3 ドイツ 1兆5624億ドル [2018年] 機械類 28.2% 自動車 16.5 その他 447 サウジアラビア 魚介類 その他 33.6 野菜・果実 チリ 銅鉱 24.8% 23.8 【9.58.31 中国の機械類の輸出額は, ドイツの機械類の輸出額の2倍以 上である。 ② 野菜 果実の輸出額は,エチオピアよりチリのほうが大きい。 ③ドイツの医薬品の輸出額は中国の繊維品の輸出額より大き い。 せんい ④エチオピアのコーヒー豆の輸出額は, 3億ドル以上である。 755億ドル [2018年] エチオピア 15億ドル コーヒー豆 ごま [2018年] /24.3%/ 19.0 18.2 肉類 6.6 その他 31.9 20 40 60 80 図各国の輸出品目

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