Understanding influencers’ perspectives and predicting public sentiment are crucial for event assessment and guidance in computational social systems, enabling more informed decision-making. However, this task is inherently challenging due to the unstructured, context-sensitive, and heterogeneous nature of online communication. To address these challenges, we propose a novel intelligent computational framework, Multi-domain Opinion Leader Agents Emotion Prediction (MOAEP). Our framework comprises three key components: (1) An Automatic Question Generation (AQG) module employing “Who, What, Where, When, Why, and How” (5W1H) questioning to systematically explore topic dimensions; (2) A Multi-domain Opinion Leader Agents (MOA) module that integrates enhanced Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to produce domain-specific responses; and (3) An emotion prediction engine that synthesizes agent interactions to forecast collective emotional responses, enabling proactive social computing analysis that surpasses conventional post-event methods. Experimental results demonstrate the framework’s efficacy: the AQG module generates high-fidelity outputs, while the influencer agents maintain consistent performance, achieving an average “Generative Pre-trained Transformer 4” (GPT-4) evaluation score of 6.85 (on a 0–10 scale) across multiple dimensions. In a social media conflict case study, “Russia-Ukraine War”, our framework successfully predicts key influencers’ perspectives and aligns emotional forecasts with observed real-world sentiment trends. These findings underscore the potential of MOAEP to provide actionable insights for decision-making in computational social science.

Acquiring high-resolution light fields (LFs) is expensive. LF angular superresolution aims to synthesize the required number of views from a given sparse set of spatially high-resolution images. Existing methods struggle with sparsely sampled LFs captured with large baselines. Some methods rely on depth estimation and view reprojection, and are sensitive to textureless and occluded regions. Other non-depth based methods suffer from aliasing or blurring effects due to the large disparity. In addition, most methods require specific models for different interpolation rates, which reduces their flexibility in practice. In this paper, we propose a learning framework that overcomes these challenges by exploiting the global and local structures of LFs. Our framework includes aggregation across both the angular and spatial dimensions to fully exploit the input data and a novel bilateral upsampling module that upsamples each epipolar plane image while better preserving its local parallax structure. Furthermore, our method predicts the weights of the interpolation filters based on both subpixel offset and range difference, allowing angular superresolution at different rates with a single model. We show that our non-depth based method outperforms the state-of-the-art methods in terms of handling large disparities and flexibility on both real-world and synthetic LF images.

Existing omnidirectional image quality assessment (OIQA) models extract local features from each viewport independently, increasing computational complexity and making it difficult to describe the correlations between viewports using an end-to-end fusion model. To solve these issues, a quality assessment method was proposed based on feature sharing and adaptive fusion of multiple viewports. By utilizing shared backbone networks, the method transformed the viewport segmentation and computation that were independent of each other to the feature domain, enabling local feature extraction of the image through one-shot feed-forward computation. In addition, a viewport segmentation method in the feature domain using spherical uniform sampling was employed to guarantee consistent pixel density between view space and observation space, with semantic information guiding the adaptive fusion of local quality features of viewpoints. The Pearson linear correlation coefficient (PLCC) and Spearman rank order correlation coefficient (SRCC) on the compressed virtual reality image quality (CVIQ) and OIQA datasets were both above 0.96, showing superior performance compared with mainstream evaluation methods. Compared with the traditional evaluation method structural similarity index measure (SSIM), its average PLCC and average SRCC on the above two datasets were improved by 9.52% and 8.7%, respectively; compared with the latest evaluation method multi-perceptual features image quality assessment (MPFIQA), its average PLCC and average SRCC were improved by 1.71% and 1.44%, respectively.