How to quantify influences between users, seeing that social network users influence each other in their temporal behaviors? Previous work has directly defined an independent model parameter to capture the interpersonal influence between each pair of users. To do so, these models need a parameter for each pair of users, which results in high-dimensional models becoming easily trapped into the overfitting problem. However, such models do not consider how influences depend on each other if influences are sent from the same user or if influences are received by the same user. Therefore, we propose a model that defines parameters for every user with a latent influence vector and a susceptibility vector, opposite to define influences on user pairs. Such low-dimensional representations naturally cause the interpersonal influences involving the same user to be coupled with each other, thus reducing the model’s complexity. Additionally, the model can easily consider the temporal information and sentimental polarities of users’ messages. Finally, we conduct extensive experiments on two real-world Microblog datasets, showing that our model with such representations achieves best performance on three prediction tasks, compared to the state-of-the-art and pair-wise baselines.

Whereas it is well known that social network users influence each other, a fundamental problem in influence maximization, opinion formation and viral marketing is that users' influences are difficult to quantify. Previous work has directly defined an independent model parameter to capture the interpersonal influence between each pair of users. However, such models do not consider how influences depend on each other if they originate from the same user or if they act on the same user. To do so, these models need a parameter for each pair of users, which results in high-dimensional models becoming easily trapped into the overfitting problem. Given these problems, another way of defining the parameters is needed to consider the dependencies. Thus we propose a model that defines parameters for every user with a latent influence vector and a susceptibility vector. Such low-dimensional and distributed representations naturally cause the interpersonal influences involving the same user to be coupled with each other, thus reducing the model's complexity. Additionally, the model can easily consider the sentimental polarities of users' messages and how sentiment affects users' influences. In this study, we conduct extensive experiments on real Microblog data, showing that our model with distributed representations achieves better accuracy than the state-of-the-art and pair-wise models, and that learning influences on sentiments benefit performance.