Predicting user engagement on social media platforms is increasingly vital for applications such as recommendations, Internet marketing, and content moderation. Existing methods face challenges in effectively capturing contextual information and ensuring relevance, often leading to the retrieval of semantically similar but contextually irrelevant UGCs. To address these challenges, we introduce DRUEP, a distilled retrieval-augmented framework that explores distilled multimodal representations through the information bottleneck technique to enhance UGC relevance retrieval, improving the quality of retrieved UGC for more accurate user engagement prediction. Our approach is able to effectively reduce redundant multi-modal noise, optimizes retrieval efficiency, and captures dynamic nature of UGC. Extensive experiments on multimodal large-scale datasets demonstrate that DRUEP significantly outperforms existing state-of-the-art methods.

Multimodal sentiment analysis is critical in many real-world applications like smart cities, healthcare, and human-computer interaction, where sentiment is conveyed through various modalities, including text, audio, and video. However, existing methods still face several critical challenges in mitigating the impact of random modality loss, particularly in preserving the reliability of emotional patterns. To address these limitations, we introduce UniMSA, a novel framework for multimodal sentiment analysis with missing modalities. It addresses the missing data issue by improving the bipolar emotional uncertainty learning. Our approach enhances the reliability of sentiment analysis by integrating both positive and negative emotional uncertainty estimations to recover emotional patterns in randomly missing modalities. Extensive experiments conducted on large-scale multimodal sentiment datasets demonstrate the effectiveness of UniMSA in comparison to state-of-the-art methods.

Crime prediction stands as a pivotal concern within the realm of urban management due to its potential threats to public safety. While prior research has predominantly focused on unraveling the intricate dependencies among urban regions and temporal dynamics, the challenges posed by the scarcity and uncertainty of historical crime data have not been thoroughly investigated. This study introduces an innovative spatial-temporal augmented learning framework for crime prediction, namely STAug. In STAug, we devise a CrimeMix to improve the ability of generalization. Furthermore, we harness a spatial-temporal aggregation to capture and incorporate multiple correlations covering the temporal, spatial, and crime-type aspects. Experiments on two real-world datasets underscore the superiority of STAug over several baselines.

Understanding human mobility has become an important aspect of location-based services in tasks such as personalized recommendation and individual moving pattern recognition, enabled by the large volumes of data from geo-tagged social media (GTSM). Prior studies mainly focus on analyzing human historical footprints collected by GTSM and assuming the veracity of the data, which need not hold when some users are not willing to share their real footprints due to privacy concerns—thereby affecting reliability/authenticity. In this study, we address the problem of Inferring Real Mobility (IRMo) of users, from their unreliable historical traces. Tackling IRMo is a non-trivial task due to the: (1) sparsity of check-in data; (2) suspicious counterfeit check-in behaviors; and (3) unobserved dependencies in human trajectories. To address these issues, we develop a novel Graph-enhanced Attention model called IRMoGA , which attempts to capture underlying mobility patterns and check-in correlations by exploiting the unreliable spatio-temporal data. Specifically, we incorporate the attention mechanism (rather than solely relying on traditional recursive models) to understand the regularity of human mobility, while employing a graph neural network to understand the mutual interactions from human historical check-ins and leveraging prior knowledge to alleviate the inferring bias. Our experiments conducted on four real-world datasets demonstrate the superior performance of IRMoGA over several state-of-the-art baselines, e.g., up to 39.16% improvement regarding the Recall score on Foursquare.