Eisenstein Jacob, O'Connor Brendan, Smith Noah A, Xing Eric P
School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia, United States of America.
School of Computer Science, University of Massachusetts, Amherst, Massachusetts, United States of America.
PLoS One. 2014 Nov 19;9(11):e113114. doi: 10.1371/journal.pone.0113114. eCollection 2014.
Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity - especially with regard to race - plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. Rather than moving towards a single unified "netspeak" dialect, language evolution in computer-mediated communication reproduces existing fault lines in spoken American English.
计算机介导的交流正在推动书面语言本质的根本性变化。我们通过对一个包含1.07亿条推特消息（由270万个唯一用户账户撰写）的数据集进行统计分析来研究这些变化。使用潜向量自回归模型对数千个单词进行汇总，我们识别出美国语言变化传播中的高层次模式。我们的模型对推特采样率的不可预测变化具有鲁棒性，并提供了宏观语言影响与一组人口和地理预测因素之间关系的概率表征。该分析结果为先前关注地理 proximity 和人口规模的论点提供了支持。然而，人口统计学上的相似性——尤其是在种族方面——发挥着更为核心的作用，因为具有相似种族人口统计学特征的城市更有可能共享语言影响。计算机介导的交流中的语言演变并非朝着单一统一的“网络语言”方言发展，而是再现了美国英语口语中现有的断层线。
