ContextClaim: A New Paradigm of Moving Evidence Retrieval to the Fact-Checking Detection Stage

ContextClaim proposes a context-driven fact-checkability detection method. Its core is to move evidence retrieval—originally part of the claim verification stage—to the detection stage, using external knowledge sources like Wikipedia to provide background information for claims, aiming to improve the accuracy of the early filtering stage in automated fact-checking systems. This method breaks the strict separation between detection and verification in traditional fact-checking processes, helping detection models make more informed judgments by introducing external context.

Bottlenecks in Fact-Checking

In the era of information explosion, automated fact-checking systems need to quickly filter "check-worthy" claims. However, if the detection stage in traditional processes misses important claims or misjudges non-checkable content, subsequent work becomes meaningless.

Limitations of Existing Methods

Existing checkable claim detection methods rely only on the text of the claim itself and lack necessary background information. For example, to determine whether "a certain policy reduced the unemployment rate by 3%" is checkable, one needs to know the country where the policy was implemented, its implementation time, and official data, etc. It is difficult to make an accurate judgment based solely on the text.

The core insight of ContextClaim is to move evidence retrieval from the verification stage to the detection stage. Its workflow includes three key steps:

1. Entity Extraction: Identify key entity mentions in the claim;
2. Information Retrieval: Retrieve relevant information from structured knowledge sources like Wikipedia;
3. Context Summarization: Use large language models to generate concise background summaries for downstream classifiers. In this way, the detection model no longer relies solely on the claim text but makes judgments based on rich external context.

Experimental Datasets

The research team evaluated on two representative datasets:

• CheckThat! 2022 COVID-19 Twitter Dataset (social media scenario, pandemic-related claims);
• PoliClaim Political Debate Dataset (formal political discourse scenario, policy-related claims).

Experimental Design

Various model architectures (encoder-only and decoder-only models) and learning settings (fine-tuning, zero-shot, few-shot) are considered to ensure the generalizability of results and provide references for practical applications.

The research results show that context enhancement can improve detection performance, but the effect is not uniformly distributed:

• Domain Differences: Claims in different subject domains have different degrees of dependence on context;
• Model Architecture Impact: Encoder-only and decoder-only models differ in how they utilize context;
• Sensitivity to Learning Settings: The value of external context is more prominent in resource-constrained scenarios (zero-shot/few-shot). In addition, through component analysis, human evaluation, and error analysis, the timing and reasons why context contributes to reliable judgments are explored.

Practical Significance

ContextClaim is applicable to:

• Social media monitoring: Quickly identify claims that need attention in massive content;
• News editing assistance: Help judge whether reader feedback is worth following up;
• Real-time analysis of political debates: Mark suspicious claims during live broadcasts for subsequent verification.

Future Outlook

Pending issues include: the timeliness limitation of Wikipedia, the impact of retrieval quality on performance, applicability to multilingual scenarios, etc. These are directions for future research.

The value of ContextClaim lies in re-examining the boundaries and order of the fact-checking process. The seemingly simple adjustment of moving evidence retrieval forward may bring significant performance improvements to the entire fact-checking pipeline. In today's era of rampant misinformation, this method takes an important step toward building a more reliable fact-checking system.