  With a growing number of social apps, people have become increasingly willing
to share their everyday photos and events on social media platforms, such as
Facebook, Instagram, and WeChat. In social media data mining, post popularity
prediction has received much attention from both data scientists and
psychologists. Existing research focuses more on exploring the post popularity
on a population of users and including comprehensive factors such as temporal
information, user connections, number of comments, and so on. However, these
frameworks are not suitable for guiding a specific user to make a popular post
because the attributes of this user are fixed. Therefore, previous frameworks
can only answer the question "whether a post is popular" rather than "how to
become famous by popular posts". In this paper, we aim at predicting the
popularity of a post for a specific user and mining the patterns behind the
popularity. To this end, we first collect data from Instagram. We then design a
method to figure out the user environment, representing the content that a
specific user is very likely to post. Based on the relevant data, we devise a
novel dual-attention model to incorporate image, caption, and user environment.
The dual-attention model basically consists of two parts, explicit attention
for image-caption pairs and implicit attention for user environment. A
hierarchical structure is devised to concatenate the explicit attention part
and implicit attention part. We conduct a series of experiments to validate the
effectiveness of our model and investigate the factors that can influence the
popularity. The classification results show that our model outperforms the
baselines, and a statistical analysis identifies what kind of pictures or
captions can help the user achieve a relatively high "likes" number.
