Predicting near-future monetization in virtual livestreaming remains methodologically challenging because paid-support events are sparse, temporally dependent, and vulnerable to leakage under inappropriate evaluation designs. This study develops a leakage-guarded, window-based machine-learning framework for predicting next-window Superchat occurrence from VTuber live-chat dynamics. Public VTuber live-chat and Superchat logs were reconstructed into non-overlapping five-minute windows, and features were organized into audience activity, member composition, message intensity, donation-state information, and short-horizon dynamic groups. To reduce optimistic bias, the primary evaluation used video-level grouped splitting and compared a strict setting that excluded direct current-window donation-state variables with an extended donation-state-aware setting. HistGradientBoosting achieved the strongest performance. In the strict setting, it reached PR-AUC = 0.899, ROC-AUC = 0.920, F1 = 0.822, and Brier score = 0.171, while the extended setting produced only modest additional gains. Additional zero-chat sensitivity, repeated grouped split, channel-level robustness, graph-proxy baseline, feature-ablation, and calibration analyses supported the stability and interpretability of the framework. The results suggest that next-window Superchat occurrence can be predicted from participation breadth, chat activity, message intensity, and temporally shifted behavioral dynamics under leakage-aware evaluation.

Automatic detection of deceptive intent in negotiation dialogue remains difficult because deceptive utterances are rare, context-dependent, and pragmatically subtle. This study develops a deployment-oriented evaluation pipeline for negotiation analytics using the Diplomacy corpus and compares sparse, dense, and imbalance-aware neural models under a unified protocol. The pipeline integrates context-window benchmarking, validation-based threshold selection, 10-seed robustness analysis, model-agnostic explanation case studies, and controlled perturbation stress tests. Across binary speaker-intention and receiver-perception tasks, contextualized inputs consistently outperform isolated utterances, confirming that deception-related interpretation is inherently sequential. The sparse term frequency–inverse document frequency (TF-IDF) model remains the strongest and most efficient overall benchmark, whereas stronger imbalance-aware neural baselines can improve minority deceptive-instance sensitivity at substantially higher computational cost. Error analysis further shows that socially mediated deception-quadrant prediction is markedly harder than direct binary in-tent prediction, with many failures collapsing toward majority straightforward cases. Controlled perturbation tests show that sparse modeling is especially stable under light-weight surface corruption, while neural robustness remains architecture-dependent. The main contribution is therefore a practical decision framework for selecting among efficient sparse monitoring, dense baselines, and minority-sensitive neural detection under operational constraints.