  The overwhelming amount and rate of information update in online social media
is making it increasingly difficult for users to allocate their attention to
their topics of interest, thus there is a strong need for prioritizing news
feeds. The attractiveness of a post to a user depends on many complex
contextual and temporal features of the post. For instance, the contents of the
post, the responsiveness of a third user, and the age of the post may all have
impact. So far, these static and dynamic features has not been incorporated in
a unified framework to tackle the post prioritization problem. In this paper,
we propose a novel approach for prioritizing posts based on a feature modulated
multi-dimensional point process. Our model is able to simultaneously capture
textual and sentiment features, and temporal features such as self-excitation,
mutual-excitation and bursty nature of social interaction. As an evaluation, we
also curated a real-world conversational benchmark dataset crawled from
Facebook. In our experiments, we demonstrate that our algorithm is able to
achieve the-state-of-the-art performance in terms of analyzing, predicting, and
prioritizing events. In terms of interpretability of our method, we observe
that features indicating individual user profile and linguistic characteristics
of the events work best for prediction and prioritization of new events.
