  Social media, such as Facebook and WeChat, empowers millions of users to
create, consume, and disseminate online information on an unprecedented scale.
The abundant information on social media intensifies the competition of WeChat
Public Official Articles (i.e., posts) for gaining user attention due to the
zero-sum nature of attention. Therefore, only a small portion of information
tends to become extremely popular while the rest remains unnoticed or quickly
disappears. Such a typical `long-tail' phenomenon is very common in social
media. Thus, recent years have witnessed a growing interest in predicting the
future trend in the popularity of social media posts and understanding the
factors that influence the popularity of the posts. Nevertheless, existing
predictive models either rely on cumbersome feature engineering or
sophisticated parameter tuning, which are difficult to understand and improve.
In this paper, we study and enhance a point process-based model by
incorporating visual reasoning to support communication between the users and
the predictive model for a better prediction result. The proposed system
supports users to uncover the working mechanism behind the model and improve
the prediction accuracy accordingly based on the insights gained. We use
realistic WeChat articles to demonstrate the effectiveness of the system and
verify the improved model on a large scale of WeChat articles. We also elicit
and summarize the feedback from WeChat domain experts.

Human-Computer Interaction