Characterizing Social Media Behavior under Limited Observability: Retweet Link Prediction and Multimodal Intention Recognition

Abstract

Social-media behavior can be observed through both interaction structure and multimodal content, yet each view is often incomplete. We study two complementary tasks on separate datasets: predicting retweet links from an extremely sparse graph without node attributes, and generating user intentions from text-image posts with limited annotations. For link prediction, we use a leakage-controlled transductive protocol to compare GCN and GraphSAGE, classical topology heuristics, and three node-input designs. Across repeated runs, a GCN with compact degree and strength features achieves an average precision of 0.9243 and an MCC of 0.7051. Under joint edge-label corruption, topology-based GCNs retain strong ranking performance, whereas fixed-threshold MCC degrades substantially; adding fixed random node identifiers does not improve overall robustness. For intention recognition, we retain the MIKO framework and evaluate LoRA adaptation of three language models together with distribution-aware sampling based on Pearson correlation, cosine similarity, and KL divergence. Fine-tuning consistently improves lexical-overlap metrics, while distribution-aware sampling produces more uniform performance across intention types. Together, the two studies characterize who interacts and why users post when social-media observations are sparse, noisy, or limited.