LLM-based fact-checking verifies complex claims through decomposition, retrieval, and reasoning aggregation. Existing methods expand verification inputs to cover more factual conditions, but added fragments can be redundant or misleading, compete with decisive evidence, and bias predictions toward surface relevance. Debiasing methods mitigate this bias by estimating shortcut effects from claim-side cues or biased signals in constructed inputs. However, because they calibrate verifier predictions after input construction, they are not designed to repair missing or noisy verification states. Thus, complex fact-checking should balance factual coverage and noise control during verification-state construction. We propose ERGA, an Explorer–Refiner Game with Backdoor-Adjusted Verdict Aggregation. ERGA formulates verification as editable state construction, initializing the state from decomposed verification units, retrieved evidence, and local analyses. It then runs an adversarial Explorer–Refiner game over this state: the Explorer strengthens under-covered factual signals, while the Refiner merges redundancy and separates weak or misleading fragments. Through this game interaction, ERGA obtains a refined state with a better coverage–noise balance and a removed fragment record. For verdict aggregation, ERGA uses the removed-fragment record to condition verifier predictions and aggregates them in a backdoor-adjusted form to reduce residual shortcut effects. Experiments on HoVer and FEVEROUS-S show that ERGA outperforms strong baselines.

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.

As the text generated by large language models (LLMs) increasingly resembles human-written text (HWT), detecting LLM-generated text (LGT) is crucial to avoid malicious use of LGT. Recent research treats LGT detection as an out-of-distribution (OOD) detection problem and views HWT as the OOD. However, existing OOD detection methods assume that LGT is a single homogeneous distribution. In practice, LGT exhibits different characteristics under different generation conditions. Text from weaker LLMs tends to form distinct clusters and is easy to detect, whereas text from stronger models significantly overlaps with HWTs and is hard to detect. To address the issue, in this paper, we propose an LGT detection framework based on density-aware manifold learning and the construction of hybrid Mahalanobis energy. We apply density-aware manifold learning with Laplacian smoothness and density regularization in embedding space, amplifying differences between LGT and HWT. We further propose a density-adaptive hybrid Mahalanobis metric that combines global and local covariance via density weighting, enabling adaptation to the manifold-aware embedding space. Finally, based on the metric, we define the distribution energy as a measure of distribution discrepancy, and we employ energy learning and contrastive learning to separate distributions hierarchically, establishing a clear OOD decision boundary. Experiments show that our method outperforms strong baselines.

With the emergence of new topics on social media as sources of rumor propagation, addressing the domain shift between the source and target domain and the target domain samples scarcity remains a crucial task in cross-domain rumor detection. Traditional deep learning-based methods and LLM-based methods are mostly focused on the in-domain condition, thus having poor performance in cross-domain setting. Existing domain adaptation rumor detection approaches ignore the data generalization differences and rely on a large amount of unlabeled target domain samples to achieve domain adaptation, resulting in less effective on emerging topic rumor detection. In this paper, we propose a Gradient Coherence guided Meta-Learning approach (GCML) for emerging topics rumor detection. Firstly, we calculate the task generalization score of each source task (sampled from source domain) from a gradient coherence perspective, and selectively learn more “generalizable” tasks that are more beneficial in adapting to the target domain. Secondly, we leverage meta-learning to alleviate the target domain samples scarcity, which utilizes task generalization scores to re-weight meta-test gradients and adaptively updates learning rate. Extensive experimental results on real-world datasets show that our method substantially outperforms SOTA baselines.

Rumor detection on social media has become an emerging topic. Traditional deep learning-based methods model rumors based on content, propagation structure, or user behavior, but these approaches are constrained by limited modeling capacity and insufficient training corpora. Recent studies have explored using LLMs for rumor detection through supervised fine-tuning (SFT), but face two issues: 1) unreliable samples sometimes mislead the model learning; 2) the model only learns the most salient input-output mapping and skips in-depth analyses of the rumored content for convenience. To address these issues, we propose an SFT-based LLM rumor detection model with Influence guided Sample selection and Game-based multi-perspective Analysis (ISGA). Specifically, we first introduce the Influence Score (IS) to assess the impact of samples on model predictions and select samples for SFT. We also approximate IS via Taylor expansion to reduce computational complexity. Next, we use LLMs to generate in-depth analyses of news content from multiple perspectives and model their collaborative process for prediction as a cooperative game. Then we utilize the Shapley value to quantify the contribution of each perspective for selecting informative perspective analyses. Experiments show that ISGA excels existing SOTA on three datasets.