Investigating the diffusion of morphosyntactic innovations using social media

使用社交媒体调查形态句法创新的传播

• 批准号: ES/P00752X/2
• 负责人: David Willis
• 金额: $ 10.59万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FP00752X%2F2
• 关键词: Investigating; diffusion; morphosyntactic; innovations; using

We propose to research the way in which changes in the grammar of languages ("innovations") spread out from a small number of speakers to a larger section of the population ("diffusion"). The types of data gathered by traditional means are not especially well-suited to studying diffusion processes. Dialectologists have traditionally gathered data by surveying speakers at a large range of different localities. However, it is usually impractical to survey a large number of speakers at each locality, meaning that such surveys do poorly when there is variation between speakers living in the same places (as is usually the case during ongoing change). Sociolinguists typically interview a large number of speakers at a single locality; it is possible to then investigate diffusion processes by comparing such datasets, but this only ever offers a very limited geographical range. Accordingly, we propose to use data from social-media platforms such as Twitter to investigate diffusion processes. Although a form of written language, social-media data tend to be highly informal and often provide a good approximation to spoken data. By using data from social media, we will be able to gather a very large quantity of localised data from many places across large areas. We will explore new ways of using social media data to investigate language variation and change and demonstrate the effectiveness of using such data to investigate geospatial diffusion and change in grammatical patterns.We will use Twitter to collect three datasets ("corpora") of tweets in English and Welsh in Britain, and in Norwegian in Norway covering a period of nine months. The selection of these three languages enables us to compare the effect of different demographic and geographic scenarios on patterns of diffusion: Welsh as a minority/lesser-used language vs. English and Norwegian as majority languages; low population density in Norway vs. higher population density in much of England; etc. We will supplement these Twitter corpora with additional data obtained by data-scraping from Norwegian- and Welsh-language-specific social media.We will then identify language changes currently diffusing in these populations and investigate their distribution in these corpora. Changes we expect to investigate include the spread of a new second-person pronoun 'chdi' ('you') in Welsh, the spread of future constructions with 'komme til å' and 'bli å' in Norwegian and changes in the syntax of constructions with 'need' in English (such as 'you need your hair washing' vs. 'you need your hair washed'). By identifying all instances of both the innovative and the older option(s) in our corpora we will be able to map where each different option is typically used. By comparing these findings with geographical patterns known from earlier studies or identified in other datasets, we will be able to map the spread of new forms over time and so identify the properties of processes of diffusion. In this way, we will be able to answer questions such as: do changes in these speech communities spread continuously over land ("contagious diffusion") or jump from city to city before reaching rural regions ("hierarchical diffusion")? Is this mode of diffusion affected by the type of change in question, by the demographics of the region or by some other factors?We will use interactions between users in our corpora (retweets, @direct messages, mutual following) to construct a model of these users' social network. We will then be able to compare the effectiveness of this network model as a predictor of the pathway of diffusion to the geographical model. Our results will be demonstrated in action through web-apps that predict users' origins using their responses to questions about their language use and they will furthermore be made available to the public via an online atlas-style website.

我们建议研究语言语法的变化（“创新”）如何从少数使用者传播到更大范围的人口（“扩散”）。通过传统方式收集的数据类型并不特别适合研究扩散过程。传统上，方言学家通过调查大量不同地点的说话者来收集数据。然而，对每个地点的大量说话者进行调查通常是不切实际的，这意味着当居住在同一地点的说话者之间存在差异时（在持续变化期间通常会出现这种情况），此类调查的效果很差。社会语言学家通常会在一个地点采访大量的说话者。然后可以通过比较这些数据集来研究扩散过程，但这只能提供非常有限的地理范围。因此，我们建议使用来自 Twitter 等社交媒体平台的数据来调查扩散过程。尽管社交媒体数据是一种书面语言，但它往往是高度非正式的，并且通常可以很好地近似口头数据。通过使用来自社交媒体的数据，我们将能够从大面积的许多地方收集大量本地化数据。我们将探索使用社交媒体数据调查语言变异和变化的新方法，并展示使用此类数据调查地理空间扩散和语法模式变化的有效性。我们将使用 Twitter 收集英国英语和威尔士语以及挪威挪威语的三个推文数据集（“语料库”），为期九个月。选择这三种语言使我们能够比较不同人口和地理情况对传播模式的影响：威尔士语作为少数/较少使用的语言与英语和挪威语作为主要语言；挪威的人口密度较低，而英格兰大部分地区的人口密度较高；我们将通过从挪威语和威尔士语特定社交媒体中抓取数据获得的额外数据来补充这些 Twitter 语料库。然后，我们将识别当前在这些人群中传播的语言变化，并调查它们在这些语料库中的分布。我们期望调查的变化包括威尔士语中新的第二人称代词“chdi”（“你”）的传播、挪威语中“komme til å”和“bli å”未来结构的传播，以及英语中“need”结构语法的变化（例如“you need your Hair Washing”与“you need your Hair Washing”）。通过识别语料库中创新选项和旧选项的所有实例，我们将能够映射每个不同选项通常使用的位置。通过将这些发现与早期研究中已知的或其他数据集中确定的地理模式进行比较，我们将能够绘制新形式随时间的传播情况，从而确定传播过程的特性。通过这种方式，我们将能够回答这样的问题：这些言语社区的变化是在陆地上持续传播（“传染性扩散”）还是在到达农村地区之前从一个城市跳到另一个城市（“等级扩散”）？这种传播模式是否受到相关变化类型、地区人口统计或其他因素的影响？我们将使用语料库中用户之间的交互（转发、@直接消息、相互关注）来构建这些用户社交网络的模型。然后，我们将能够比较该网络模型作为地理模型扩散路径预测器的有效性。我们的结果将通过网络应用程序得到实际证明，这些应用程序根据用户对语言使用问题的回答来预测用户的来源，并且还将通过在线地图集式网站向公众提供。

项目成果

Variation in British English morphosyntax in the Tweetolectology corpus

Tweetolectology 语料库中英式英语形态句法的变化

• 发表时间: 2020
• 作者: Willis D

Using social-media data to investigate morphosyntactic variation and dialect syntax in a lesser-used language: Two case studies from Welsh

使用社交媒体数据研究较少使用的语言的形态句法变异和方言句法：威尔士语的两个案例研究

• DOI: 10.5334/gjgl.1073
• 发表时间: 2020
• 期刊: a journal of general linguistics
• 作者: Willis D

Urban Matters - Current approaches in variationist sociolinguistics

城市事务 - 变异社会语言学的当前方法

• DOI: 10.1075/silv.27.11bla
• 发表时间: 2021
• 作者: Gopal D