Transnational Transition to Adulthood: New insights from social media data

社交媒体数据代表了新的数据源，为社会科学提供了新的机遇(和挑战)(Savage&Burrow，2007；McCormick等人)。2015年)。该项目将调查Facebook作为人口统计研究的潜在数据源提供的机会。特别是，我建议使用社交媒体数据应用程序，通过利用个人在Facebook(以及其他社交媒体)上发布的数字痕迹来研究欧洲的移民流动。正如Raymer等人所说。(2013)，欧洲的国际移徙数据“在可获得性、定义和质量上不一致”。欧洲国家确实使用不同的系统和设计分别收集移民数据。我将提供人员流动的估计(按性别、教育程度等)。根据他们在Facebook上指定的地点的变化在欧洲国家之间进行迁移。这样的估计将补充现有的但不准确的官方移民流动估计。该项目还将通过记录“向成年期过渡”的出现及其驱动因素(与工作或学习有关的原因)，对社会人口统计学文献作出理论贡献。我建议使用贝叶斯推理，这将允许估计具有分层结构的复杂模型的参数。它还将为参数和其他感兴趣的数量提供不确定度测量。统计模型的贝叶斯分析将需要应用并可能扩展尖端计算方法，还可能导致以R和Python语言开发新的软件，这些软件将可移植到其他应用程序。

Social media data represent new data sources providing new opportunities (and challenges) for theSocial Sciences (Savage & Burrows 2007; McCormick et al. 2015). The project will investigate theopportunities offered by Facebook as a potential data source for demographic research.In particular, I propose an application for social media data which studies migration movements inEurope by exploiting the digital traces that individuals leave as posts on Facebook (and potentially othersocial media). As it is stated by Raymer et al. (2013), international migration data in Europe are"inconsistent in availability, definition, and quality". European countries do collect separately migrationdata with different systems and designs. I will produce estimates of the flows of people (by gender,education etc.) migrating between European countries based on the change in the location that theyspecify on Facebook. Such estimates will complement existing but inaccurate official estimates ofmigration flows. The project will also have a theoretical contribution to the socio-demographicliterature, by documenting the emergence of a 'transitional transition to adulthood' and its drivers (workor study-related reasons). I propose to utilise Bayesian inference which will allow estimation of the parameters of a complexmodel with hierarchical structure. It will also provide measures of uncertainty for the parameters andother quantities of interest. Bayesian analysis of statistical models will require applying and possiblyextending cutting-edge computational methods, and may also lead to development of new software inlanguages such as R and Python, which will be transferrable to other applications.