Investigating the diffusion of morphosyntactic innovations using social media

我们建议研究语言语法的变化（“创新”）从少数说话者传播到更大部分人口（“扩散”）的方式。通过传统手段收集的数据类型并不特别适合研究扩散过程。传统上，方言学家通过调查不同地区的说话人来收集数据。然而，在每个地方调查大量的讲者通常是不切实际的，这意味着当生活在同一地方的讲者之间存在差异时，这种调查效果很差（在持续变化期间通常是这种情况）。社会语言学家通常在一个地方采访大量的说话者;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，我们建议使用来自社交媒体平台（如Twitter）的数据来研究扩散过程。虽然是书面语言的一种形式，但社交媒体数据往往是高度非正式的，并且通常提供与口语数据的良好近似。通过使用来自社交媒体的数据，我们将能够从大区域的许多地方收集大量的本地化数据。我们将探索使用社交媒体数据来调查语言变异和变化的新方法，并展示使用这些数据来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收集三个数据集（“语料库”）的tweet在英国的英语和威尔士语，以及在挪威的挪威语，为期九个月。这三种语言的选择使我们能够比较不同的人口和地理情景对传播模式的影响：威尔士语作为少数/较少使用的语言与英语和挪威语作为多数语言;挪威人口密度低与英格兰大部分地区人口密度高;等。我们将补充这些Twitter语料库与额外的数据获得的数据，从挪威语和威尔士语，然后，我们将确定目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。我们希望调查的变化包括一个新的第二人称代词“chdi”（“你”）在威尔士语中的传播，未来结构的传播与“komme til”和“bli til”在挪威语中的传播，以及英语中“需要”结构的语法变化（例如“你需要洗头发”与“你需要洗头发”）。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的典型使用位置。通过将这些发现与早期研究中已知的或在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，从而确定扩散过程的特性。通过这种方式，我们将能够回答以下问题：这些言语社区的变化是否会在土地上持续传播（“传染性扩散”），或者在到达农村地区之前从一个城市跳到另一个城市（“分层扩散”）？这种传播方式是否受到有关变化类型、区域人口结构或其他因素的影响？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）来构建这些用户的社交网络模型。然后，我们将能够比较该网络模型作为扩散路径的预测器与地理模型的有效性。我们的研究结果将通过网络应用程序进行实际演示，这些应用程序利用用户对语言使用问题的回答来预测用户的来源，并将通过一个在线地图集式网站向公众提供。

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.