Misinformation rarely presents itself as entirely true or entirely false. Instead, it often embeds partial truths within misleading contexts, creating narratives that blur the boundary between fact and falsehood. Traditional binary fact-checking frameworks fail to capture this nuance, forcing complex claims into oversimplified categories. To address this gap, we introduce MEGA, a multidimensional graph framework designed to classify ambiguous claims, with a particular focus on those labelled Somewhat True. MEGA integrates event evidence, spatio-temporal metadata, and a quantifiable nuance score. Its Event Candidate Extraction (ECE) module identifies supporting or contradicting evidence, while the Nuance Control Module (NCM) injects or removes nuance to assess its effect on classification. Experiments show that nuance is both detectable and learnable: adding nuance improves borderline discrimination, while stripping it leads the decisions toward false extremes and conceals partial truth. Our top model— nuance-injected without score weighting— improve accuracy and F1 score by 15 and 16 points over the claims-only baseline, and 6 and 9 points over the ECE-only variant. These results show that explicitly modeling nuance alongside context is crucial for classifying mixed-truth claims and advancing fact-checking beyond binary judgments.

In multi-person communications, conflicts often arise. Each individual may have their own perspective, which can differ. Additionally, commonly referenced offensive datasets frequently neglect contextual information and are primarily constructed with a focus on intended offenses. This study suggests that conflicts are pivotal in revealing a broader range of human interactions, including instances of unintended offensive language. This paper proposes a conflict-based data collection method to utilize inter-conflict cues in multi-person communications. By focusing on specific cue posts within conversation threads, our proposed approach effectively identifies relevant instances for analysis. Detailed analyses are provided to showcase the proposed approach efficiently gathers data on subtly offensive content. The experimental results indicate that incorporating elements of conflict into data collection significantly enhances the comprehensiveness and accuracy of detecting offensive language but also enriches our understanding of conflict dynamics in digital communication.