Community question answering is the task of answering questions on a Q&A forum or board, such as Stack Overflow or Quora.
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.
Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim.
This problem domain has been the subject of much research and includes both language-agnostic as well as language conscious solutions. We also propose a model to route newly posted questions to appropriate users based on the difficulty level of the question and the expertise of the user.
Thousands of complex natural language questions are submitted to community question answering websites on a daily basis, rendering them as one of the most important information sources these days. However, oftentimes submitted questions are unclear and cannot be answered without further clarification questions by expert community members.