Incomplete-Modality Multimodal Sentiment Analysis extends multimodal sentiment analysis (MSA) to infer sentiment from language, audio, and visual streams when one or more modalities are incomplete at inference time. Existing methods improve incomplete-modality MSA through feature reconstruction, distillation, or fusion adjustment, but usually assign similar correction strength to all samples with incomplete inputs. This overlooks the uneven emotional impact of modality missingness: mild missingness often leaves sentiment recognizable, whereas severe missingness can distort the expressed emotion and therefore requires stronger correction. We propose HyDRA (Hyperbolic Damage-aware Recovery and Adaptation), a damage-aware and text-guided hyperbolic framework that aligns correction strength with missingness-induced sentiment damage. HyDRA first recovers lost emotional cues from available modalities, then places each sample in the Poincaré ball according to estimated damage severity, and finally applies damage-weighted hyperbolic correction toward a complete-modality teacher anchor. Since hyperbolic distances expand near the boundary, heavily incomplete samples receive stronger correction while lightly incomplete samples are gently adjusted. Experiments on MOSI, MOSEI, and SIMS show SOTA robustness across incomplete-modality conditions.

Multimodal fake news detection predicts post veracity from text, images, and inter-post context. Existing content-centric and graph-propagation methods model post evidence and inter-post context, but graph propagation still assumes connected posts provide compatible veracity evidence. In multimodal fake news graphs, edges often represent post relatedness rather than veracity consistency, so propagation can pass misleading evidence across conflicting posts. In this paper, we propose Semantic-Structural Propagation Calibration (SSPC), which uses noisy LLM pseudo-labels to calibrate where and how much propagation should be trusted, rather than using them as direct labels. SSPC refines graph edges from high-confidence pseudo-label agreement, computes a graph-encoded base prediction and an explicitly graph-propagated prediction, and learns node-level propagation reliance with a cross-fitted utility gate. Experiments on Twitter, PHEME, and Weibo show SOTA performance over strong multimodal and graph-propagation fake news detection baselines.

“依 存 语 法 框 架 下 的 依 存 距 离 是 衡 量 句 法 分 析 难 度 的 重 要 指 标 。 本 文 基 于UD-Vietnamese依存树库对越南语依存距离的分布及影响越南语依存距离均值的因素进行分析研究。研究发现伬越南语依存距离分布符合幂律分布和指数分布的混合模型伻句长、长距离依存关系、依存方向均能对依存距离均值产生重要影响。该研究结果有助于从依存语法的角度揭示越南语的句法特点和规律伬为提出更科学合理的依存句法分析算法提供语言学支撑。”