The Role of Context in Detecting Previously Fact-Checked Claims

The evolution of fact-checking technology has transitioned from manual verification by experts to automated systems powered by deep learning models like BERT and Transformer architectures. Early efforts focused on semantic similarity and information retrieval techniques such as BM25, with datasets like FEVER and CLAIMBUSTERS. Recent advances include neural ranking models, sentence embeddings, and multi-modal approaches. However, these models often treat context superficially, neglecting the layered dependencies present in real-world texts, especially in political debates where claims depend heavily on prior discourse and implicit references. Addressing these gaps is vital for scalable, accurate, and explainable fact verification systems.

The main challenge lies in effectively utilizing the layered contextual information embedded in political debates and fact-checking articles. Existing models primarily focus on target documents, ignoring the source context—such as preceding sentences, pronouns, and implicit references—that are crucial for understanding complex claims. This oversight hampers the detection of context-dependent claims, especially those requiring multi-sentence reasoning or coreference resolution. Consequently, the accuracy of automated fact-checking remains limited, particularly in dynamic, fast-paced environments like social media and live debates. Overcoming these bottlenecks is essential for real-time misinformation mitigation.

The paper's key innovations include: 1) integrating source-side context modeling to capture the influence of prior discourse; 2) employing coreference resolution tools (NeuralCoref, SpanBERT) to resolve pronouns and entity references; 3) applying Transformer-XH multi-hop attention for cross-sentence reasoning, enabling the model to connect information across multiple sentences; 4) combining local neighboring sentence features with global document representations for comprehensive context understanding. These innovations collectively address the limitations of prior models that focus solely on target text, offering a more nuanced and human-like comprehension of complex claims.

• �� Retrieve candidate claims using BM25 from debate transcripts and fact-checking articles. • Use BERT-based sentence embeddings to measure semantic similarity. • Apply coreference resolution (NeuralCoref and SpanBERT) to resolve pronouns and entity references in both source and target texts. • Construct layered context representations: local context from neighboring sentences, global context via Transformer-XH’s multi-hop attention. • Concatenate features from neighboring sentences (e.g., 3 before, 1 after) with similarity scores to form enriched feature vectors. • Use these features in a ranking model (RankSVM) to re-rank candidate claims, optimizing for MAP. • Fine-tune the entire system on the PolitiFact dataset, validating across different data splits.

The model is trained and evaluated on the PolitiFact dataset, which contains verified claims from political debates and associated transcripts. Multiple data splits are used, including time-based (chronological) and random splits, to assess robustness. Baseline models include traditional BM25 retrieval and simple semantic matching, while ablation studies remove components like source context or multi-hop reasoning to measure their impact. Hyperparameters such as the number of neighboring sentences and Transformer-XH layers are tuned. Performance is measured primarily by MAP, with additional metrics like precision and recall for specific claim categories. Experiments demonstrate the effectiveness of layered context modeling, especially source-side information, in complex, context-dependent claim detection.

The proposed model achieves over 10 MAP points improvement in the most challenging time-based split, reaching 0.429 MAP compared to 0.602 for baseline. Ablation results show that removing source context reduces performance significantly, confirming its importance. Incorporating coreference resolution and multi-hop reasoning further boosts accuracy, especially for 'context-dependent' claims. Across different data splits, the model maintains superior performance, with MAP scores up to 0.539 in semi-chronological splits. These results validate the hypothesis that layered context understanding, especially from the source side, is crucial for accurate claim matching in political debates.

This approach can be deployed in real-time social media monitoring, political debate analysis, and journalism support systems to quickly identify claims that have been previously fact-checked. It enables journalists and fact-checkers to efficiently verify claims during live events, reducing manual effort and increasing response speed. The model's interpretability also aids in explaining why a claim is matched or not, fostering trust. Long-term, integrating external knowledge bases and multimodal data could further enhance system capabilities, making automated fact-checking more comprehensive and accessible across various domains such as healthcare, science, and technology.

The system's reliance on coreference tools introduces potential errors, especially in ambiguous or lengthy texts, which can propagate and reduce accuracy. Multi-hop reasoning, while powerful, is computationally intensive, limiting scalability for large datasets or real-time applications. The model's validation is primarily within political debate contexts; its effectiveness in other fields remains to be tested. Additionally, the approach assumes the availability of well-structured transcripts and annotated data, which may not always be feasible in real-world scenarios. Future work should focus on improving efficiency, robustness, and cross-domain adaptability.