Detecting, Modeling, and Predicting User Temporal Intention in Social Media

The content of social media has grown exponentially in the recent years and its role has evolved from narrating life events to actually shaping them. Unfortunately, content posted and shared in social networks is vulnerable and prone to loss or change, rendering the context associated with it (a tweet, post, status, or others) meaningless. The user sharing the resource has an implicit temporal intent: either the state of the resource at the time of sharing, or the current state of the resource at the time of the reader “clicking”. In this research, we propose a model to detect and predict the user’s temporal intention of the author upon sharing content in the social network and of the reader upon resolving this content. Furthermore, the proposed model will result in two main benefits. First, social media navigation will more closely match the implicit temporal intent of the users. Second, we will leverage the many existing public web archives and the Memento project to integrate the past and current web.