Geotagging on social media has become an important proxy for understanding
people's mobility and social events. Research that uses geotags to infer public
opinions relies on several key assumptions about the behavior of geotagged and
non-geotagged users. However, these assumptions have not been fully validated.
Lack of understanding the geotagging behavior prohibits people further
utilizing it. In this paper, we present an empirical study of geotagging
behavior on Twitter based on more than 40 billion tweets collected from 20
million users. There are three main findings that may challenge these common
assumptions. Firstly, different groups of users have different geotagging
preferences. For example, less than 3% of users speaking in Korean are
geotagged, while more than 40% of users speaking in Indonesian use geotags.
Secondly, users who report their locations in profiles are more likely to use
geotags, which may affects the generability of those location prediction
systems on non-geotagged users. Thirdly, strong homophily effect exists in
users' geotagging behavior, that users tend to connect to friends with similar
geotagging preferences.

本文基于 Twitter 平台 4 万多亿条 tweet，对 geotagging 行为进行了实证研究。结果显示，用户群体之间的 geotagging 偏好各不相同；在用户资料中报告位置信息的用户更容易使用 geotags；用户在 geotagging 行为上存在明显的同质性倾向。

Twitter 上地理标记行为的大规模经验研究

A Large-Scale Empirical Study of Geotagging Behavior on Twitter

Embedding network data into a low-dimensional vector space has shown
promising performance for many real-world applications, such as node
classification and entity retrieval. However, most existing methods focused
only on leveraging network structure. For social networks, besides the network
structure, there also exists rich information about social actors, such as user
profiles of friendship networks and textual content of citation networks. These
rich attribute information of social actors reveal the homophily effect,
exerting huge impacts on the formation of social networks. In this paper, we
explore the rich evidence source of attributes in social networks to improve
network embedding. We propose a generic Social Network Embedding framework
(SNE), which learns representations for social actors (i.e., nodes) by
preserving both the structural proximity and attribute proximity. While the
structural proximity captures the global network structure, the attribute
proximity accounts for the homophily effect. To justify our proposal, we
conduct extensive experiments on four real-world social networks. Compared to
the state-of-the-art network embedding approaches, SNE can learn more
informative representations, achieving substantial gains on the tasks of link
prediction and node classification. Specifically, SNE significantly outperforms
node2vec with an 8.2% relative improvement on the link prediction task, and a
12.7% gain on the node classification task.

本文提出了一种基于网络结构和社交演员属性相似性的社交网络嵌入框架，在节点分类和链接预测任务上均实现了显著的性能提升，比如在链接预测任务上相比 node2vec 提高了 8.2%。