A Dynamic Influence Keyword Model for Identifying Implicit User Interests on Social Networks

The rapid growth of social networks have enabled users to instantly share what is happening around them. With the character-limitation and other feature constraints imposed by microblogs, users are obliged to express their intentions in implicit forms. This behavior poses many challenges for contextual approaches that aim to identify user intentions. Furthermore, users have the tendency to display different degree of preferences towards specific interests, simultaneously in time, making it difficult for models to rank the discovered interests. We propose a dynamic interest keyword model, a graph-based ranking mechanism, that identifies the different degrees of interests of a user. Our results show that the proposed system detects human-inferred interests, 94% of the time, showing that the model is feasible and contributes various insights that can be used to improve user intention identification systems.