Modeling User Intrinsic Characteristic on Social Media for Identity Linkage Most users on social media have intrinsic characteristics, such as interests and political views, that can be exploited to identify and track them, thus raising privacy and identity concerns in online communities. In this article, we investigate the problem of user identity linkage on two behavior datasets collected from different experiments. Specifically, we focus on user linkage based on usersxe2x80x99 interaction behaviors with respect to content topics. We propose an embedding method to model a topic as a vector in a latent space to interpret its deep semantics. Then a user is modeled as a vector based on his or her interactions with topics. The embedding representations of topics are learned by optimizing the jointobjective: the compatibility between topics with similar semantics, the discriminative abilities of topics to distinguish identities, and the consistency of the same userxe2x80x99s characteristics from two datasets. The effectiveness of our method is verified on reallife datasets and the results show that it outperforms related methods. We also analyze failure cases in the application of our identity linkage method. Our analysis shows that factors such as the visibility and variance of user behaviors and usersxe2x80x99 group psychology can result in mislinkages. We also analyze the details of the behaviors of some representative users to understand the essential reasons for their identity being mislinked. We find that these users have high variance level in their behaviors. According to the above experimental results, we introduce a confidence score into identity linkage to provide information about the accuracy of the method results.

A CoEvolutionary Model for Inferring Online Social Network User Behaviors Accurate online social network user behavior inference can improve the performance of the other applications significantly, such as friend commendation, hot topic prediction, and personal website assistant. Previous works mainly focus on the trend analysis of user behaviors or adopt the method to fit the supposed userbehavior distribution, but they ignore the dynamic mutual influence among the users and behaviors on social networks. This paper proposes a coevolutionary model to formulate the interaction pattern among the users and behaviors, in which a systematic method is used for embedding the distinctiveness and permanence properties of the users and behaviors into latent features. This model could naturally capture the dynamic evolving process of the user behaviors with the time. Whatxe2x80x99s more, we also take into account the following relationship to depict the interaction information among users. Extensive experiments show that our algorithm achieves 0.024 of the MAE (hour) in the crime time inference, and 0.506 and 0.579 of the accuracy in the user and behavior inference, which surpass the stateofarts more than 7.19xc3x97, 1.12xc3x97 and 1.14xc3x97, respectively. Additional experiments on different training echoes of our model are provided to further explore its effectiveness and scalability.