A Comprehensive Study of Spatiotemporal Feature Learning for Social Medial Popularity Prediction

For accurately predicting the popularity of social media, the multi-modal approach was usually adopted to have promising performance. However, the popularity is highly correlated to its identity (i.e., user ID). Inappropriate data splitting could result in lower generalizability in real-world applications. Specifically, we observed that the training and testing datasets are partitioned on a specific timestamp, whereas some users were registered after the timestamp, implying that the partial identities could be missing in the testing phase. It turns the social media prediction (SMP) tasks temporally irrelevant, making the temporal-related feature useless due to missing identities. Therefore, we form the SMP task as an identity-preserving time-series task to observe the popularity scores for the specific identity. In addition, more valuable and essential features could be explored. In this paper, by reformulating the SMP tasks and integrating the multi-modal feature aggregation to the base learner for better performance, how identity-preserving is an essential property for SMP tasks is discussed. We firstly explore the impact of the temporal features with/without identity information in the conventional SMP tasks. Moreover, we reformulate the SMP task in the time-series data-splitting and evaluate the temporal features' importance. Comprehensive experiments are conducted to deliver the suggestions for the SMP tasks and offer the corresponding solutions for effectively predicting the popularity scores.