Social media users often hold several accounts in their effort to multiply
the spread of their thoughts, ideas, and viewpoints. In the particular case of
objectionable content, users tend to create multiple accounts to bypass the
combating measures enforced by social media platforms and thus retain their
online identity even if some of their accounts are suspended. User identity
linkage aims to reveal social media accounts likely to belong to the same
natural person so as to prevent the spread of abusive/illegal activities. To
this end, this work proposes a machine learning-based detection model, which
uses multiple attributes of users' online activity in order to identify whether
two or more virtual identities belong to the same real natural person. The
models efficacy is demonstrated on two cases on abusive and terrorism-related
Twitter content.

该研究提出了一个基于机器学习的检测模型，通过使用用户在线活动的多个属性来识别虚拟身份是否属于同一个真实人，以防止虚假 / 非法活动的传播。通过在两个滥用和涉及恐怖主义的 Twitter 内容上演示模型的有效性。

社交媒体中的用户身份链接利用语言和社交互动特征

User Identity Linkage in Social Media Using Linguistic and Social  Interaction Features

The increasing popularity and diversity of social media sites has encouraged
more and more people to participate in multiple online social networks to enjoy
their services. Each user may create a user identity to represent his or her
unique public figure in every social network. User identity linkage across
online social networks is an emerging task and has attracted increasing
attention, which could potentially impact various domains such as
recommendations and link predictions. The majority of existing work focuses on
mining network proximity or user profile data for discovering user identity
linkages. With the recent advancements in graph neural networks (GNNs), it
provides great potential to advance user identity linkage since users are
connected in social graphs, and learning latent factors of users and items is
the key. However, predicting user identity linkages based on GNNs faces
challenges. For example, the user social graphs encode both \textit{local}
structure such as users' neighborhood signals, and \textit{global} structure
with community properties. To address these challenges simultaneously, in this
paper, we present a novel graph neural network framework ({\m}) for user
identity linkage. In particular, we provide a principled approach to jointly
capture local and global information in the user-user social graph and propose
the framework {\m}, which jointly learning user representations for user
identity linkage. Extensive experiments on real-world datasets demonstrate the
effectiveness of the proposed framework.

本文提出了一种基于图神经网络的新型用户身份链接框架，称为 (m)，该框架通过学习社交图中用户的局部和全局特征来预测用户身份链接，并在实际数据集上进行了广泛实验来证明其有效性。