Fact Checking in Community Forums
This addresses misinformation in community forums, which is an incremental improvement over existing fact-checking methods by applying them to a new domain.
The paper tackles the problem of verifying factual accuracy in community question answering forums by creating a specialized dataset and proposing a multi-faceted model that integrates answer content, author profiles, community context, and external sources. The model achieves a MAP of 86.54, which is 21 points above the baseline.
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.