论文标题
测量沙拉碗：Twitter上的超级多样性
Measuring the Salad Bowl: Superdiversity on Twitter
论文作者
论文摘要
超级多样性是指由于移民而导致的人口中的大量文化多样性。在本文中，与标准语言相比，我们基于多元文化社区使用的单词的情感内容的变化介绍了一个超级多样性指数。为了计算我们的索引，我们使用Twitter数据，并开发了一种算法来扩展词典以基于词典的情感分析。我们通过将指数与欧盟委员会联合研究中心获得的官方移民统计数据进行比较，通过D4I数据挑战进行了比较。我们表明，通常，我们的措施与移民率在各种地理决议下相关。我们的方法在跨语言中产生非常好的效果，并在此处在英语和意大利推文上进行了测试。我们认为，我们的指数在无法获得移民数据的确切数据的地区具有预测能力，为移民率的现象模型铺平了道路。
Superdiversity refers to large cultural diversity in a population due to immigration. In this paper, we introduce a superdiversity index based on the changes in the emotional content of words used by a multi-cultural community, compared to the standard language. To compute our index we use Twitter data and we develop an algorithm to extend a dictionary for lexicon-based sentiment analysis. We validate our index by comparing it with official immigration statistics available from the European Commission's Joint Research Center, through the D4I data challenge. We show that, in general, our measure correlates with immigration rates, at various geographical resolutions. Our method produces very good results across languages, being tested here both on English and Italian tweets. We argue that our index has predictive power in regions where exact data on immigration is not available, paving the way for a nowcasting model of immigration rates.
