Fact Checking in Community Forums

TL;DR

This paper introduces a new approach for fact checking in community question answering forums by creating a specialized dataset and a multi-faceted model that considers content, author, community, and external sources, achieving high accuracy.

Contribution

It presents the first dedicated dataset and a novel multi-faceted model for verifying answer veracity in cQA forums, addressing a previously overlooked problem.

Findings

Achieved a MAP score of 86.54, significantly outperforming baseline.

Demonstrated the effectiveness of multi-source information integration.

Validated the model's capability to assess answer factuality accurately.

Abstract

Community Question Answering (cQA) forums are very popular nowadays, as they represent effective means for communities around particular topics to share information. Unfortunately, this information is not always factual. Thus, here we explore a new dimension in the context of cQA, which has been ignored so far: checking the veracity of answers to particular questions in cQA forums. As this is a new problem, we create a specialized dataset for it. We further propose a novel multi-faceted model, which captures information from the answer content (what is said and how), from the author profile (who says it), from the rest of the community forum (where it is said), and from external authoritative sources of information (external support). Evaluation results show a MAP value of 86.54, which is 21 points absolute above the baseline.