Social media enables the rapid spread of many kinds of information, from
memes to social movements. However, little is known about how information
crosses linguistic boundaries. We apply causal inference techniques on the
European Twitter network to quantify multilingual users' structural role and
communication influence in cross-lingual information exchange. Overall,
multilinguals play an essential role; posting in multiple languages increases
betweenness centrality by 13%, and having a multilingual network neighbor
increases monolinguals' odds of sharing domains and hashtags from another
language 16-fold and 4-fold, respectively. We further show that multilinguals
have a greater impact on diffusing information less accessible to their
monolingual compatriots, such as information from far-away countries and
content about regional politics, nascent social movements, and job
opportunities. By highlighting information exchange across borders, this work
sheds light on a crucial component of how information and ideas spread around
the world.

通过 Twitter 网络上使用因果推断技术，量化多语种用户在跨语言信息交流中的结构角色和沟通影响，揭示了多语种的重要作用，并进一步表明多语种在扩散不能被他们的单语族群接触到的信息方面具有更大的影响，从而在跨越边界的信息交流中发挥着至关重要的作用。

跨越国界：量化多语使用者在社交媒体上的交流作用

Bridging Nations: Quantifying the Role of Multilinguals in Communication  on Social Media

We propose a graphical model for representing networks of stochastic
processes, the minimal generative model graph. It is based on reduced
factorizations of the joint distribution over time. We show that under
appropriate conditions, it is unique and consistent with another type of
graphical model, the directed information graph, which is based on a
generalization of Granger causality. We demonstrate how directed information
quantifies Granger causality in a particular sequential prediction setting. We
also develop efficient methods to estimate the topological structure from data
that obviate estimating the joint statistics. One algorithm assumes
upper-bounds on the degrees and uses the minimal dimension statistics
necessary. In the event that the upper-bounds are not valid, the resulting
graph is nonetheless an optimal approximation. Another algorithm uses
near-minimal dimension statistics when no bounds are known but the distribution
satisfies a certain criterion. Analogous to how structure learning algorithms
for undirected graphical models use mutual information estimates, these
algorithms use directed information estimates. We characterize the
sample-complexity of two plug-in directed information estimators and obtain
confidence intervals. For the setting when point estimates are unreliable, we
propose an algorithm that uses confidence intervals to identify the best
approximation that is robust to estimation error. Lastly, we demonstrate the
effectiveness of the proposed algorithms through analysis of both synthetic
data and real data from the Twitter network. In the latter case, we identify
which news sources influence users in the network by merely analyzing tweet
times.

提出了一个用于表示随机过程网络的图形模型 —— 最小生成模型图。该模型是基于时间上联合分布的简化因子化建立的，可以量化 Granger 因果性，并开发了高效的方法来从数据中估计拓扑结构。该算法已在 Twitter 网络的分析上得到了验证。