Social media fact-checking is increasingly challenged by evidence-level and aggregation-level conflicts, where erroneous evidence often mimics authoritative news sources. To support conflict verification task, we construct ContraNote, a real-world dataset derived from X Community Notes. ContraNote is curated to mirror the two aforementioned challenges, providing 33,686 posts for evaluating evidence-level conflict resolution, and 54,474 instances for evaluating aggregation-level evidence prioritization. This paper further proposes CoVer, a framework designed to resolve these contradictions through factual adjudication. Cover uses a three-stage pipeline consisting of evidence schema normalization, factual consensus and support verification, prioritizing definitive evidence over noise to prevent it from compromising the final verdict. Technical evaluations across multiple benchmarks show that CoVer outperforms state-of-the-art baselines, achieving an accuracy of 88.4% on CONFACT-HumC and 89.4% on CONFACT-ModC.

Evidence grounding makes generated content verifiable by linking statements to their sources. However, effective verifiability requires two properties that existing methods fail to jointly provide: *verification granularity*, the ability to localize fine-grained evidence for each statement, and *verification completeness*, the ability to capture both support and contradiction. Existing methods either operate at granularities too coarse for rapid inspection or are support-only. We formalize *polarity-aware evidence grounding*: linking each response sentence to its contextual evidence with a supporting, contradicting, or irrelevant label. We construct **eTracer-Bench**, a human-annotated benchmark with 7,905 supporting and contradicting sentence pairs across seven domains, covering both explicit and implicit contradictions. We further propose **eTracer**, a post-hoc framework whose context-aware signed scorer recovers the full evidence map in a single forward pass per response, with localization and polarity emerging jointly from one mechanism. **eTracer** achieves the best overall Citation F1 of **82.39**, driven by a Contradict F1 of **89.20**. A pre-registered within-subjects user study (n = 30) shows that polarity-aware grounding nearly doubles contradiction detection over the document-level baseline (**27.9% → 51.9%**, Cohen’s d = 0.82, p = 0.0003), and suggests a false-completeness bias under which support-only grounding fails to improve over this baseline (**23.4% vs. 27.9%**). Dataset and code: https://github.com/chubohao/eTracer.