Geolocated social media data hold a hitherto untapped potential for exploring the relationship between user mobility and their interests at a large scale. Using geolocated Twitter data from Nigeria, we provide a feasibility study that demonstrates how the linkage of (1) a trajectory analysis of Twitter users' geolocation and (2) natural language processing of Twitter users' text content can reveal information about the interests of migrants. After identifying migrants via a trajectory analysis, we train a language model to automatically detect the topics of the migrants' tweets. Biases of manual labelling are circumvented by learning community-defined topics from a Nigerian web forum. Results suggest that differences in users' mobility correlate with varying interests in several topics, most notably religion. We find that Twitter data can be a flexible source for exploring the link between users' mobility and interests in large-scale analyses of urban populations. The joint use of spatial techniques and text analysis enables migration researchers to (a) study migrant perspectives in greater detail than is possible with census data and (b) at a larger scale than is feasible with interviews. Thereby, it provides a valuable complement to interviews, surveys and censuses, and holds a large potential for further research.

中文翻译：

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移民视角：使用地理定位推文衡量移民的动向和兴趣

地理定位的社交媒体数据在大规模探索用户移动性与其兴趣之间的关系方面具有迄今为止尚未开发的潜力。使用来自尼日利亚的地理定位 Twitter 数据，我们提供了一项可行性研究，展示了 (1) Twitter 用户地理位置的轨迹分析和 (2) Twitter 用户文本内容的自然语言处理如何联系起来揭示有关移民利益的信息。通过轨迹分析识别移民后，我们训练一个语言模型来自动检测移民推文的主题。通过从尼日利亚网络论坛学习社区定义的主题，可以避免手动标记的偏差。结果表明，用户移动性的差异与对多个主题（尤其是宗教）的不同兴趣相关。我们发现 Twitter 数据可以成为在大规模城市人口分析中探索用户流动性和兴趣之间联系的灵活来源。空间技术和文本分析的联合使用使移民研究人员能够（a）比人口普查数据更详细地研究移民观点，以及（b）比访谈更大的规模。因此，它为访谈、调查和人口普查提供了宝贵的补充，并具有进一步研究的巨大潜力。