Modeling opinion influence with user dual identity

Exploring the mechanism that explains howa user's opinion changes under the influence of his/her neighbors is of practical importance (e.g., for predicting the sentiment of his/her future opinion) and has attracted wide attention from both enterprises and academics. Though various opinion influence models have been proposed for opinion prediction, they only consider users' personal identities, but ignore their social identities with which people behave to fit the expectations of the others in the same group. In this work, we explore users' dual identities, including both personal identities and social identities to build a more comprehensive opinion influence model for a better understanding of opinion behaviors. A novel joint learning framework is proposed to simultaneously model opinion dynamics and detect social identity in a unified model. The effectiveness of the proposed approach is demonstrated through the experiments conducted on Twitter datasets.