Resolving healthcare forum posts via similar thread retrieval

Web communities such as healthcare web forums serve as popular platforms for users to get their complex medical queries resolved. A typical forum thread contains a query in its first post, and a discussion around it in subsequent posts. However many users do not receive satisfactory re- sponses from other members in the community, leaving them dissatisfied. We propose to help these users by exploiting an existing collection of discussion threads. Often many users suffer from the same medical condition and start multiple discussion threads on very similar queries. In this paper we develop and evaluate a plethora of special- ized search methods that treat an entire unresolved forum post as a query, and retrieve forum threads discussing simi- lar problems to help resolve it. The task is more challenging than a traditional document retrieval problem, since forum posts can contain a lot of irrelevant background information. The discussion threads to be retrieved are also quite different from traditional unstructured text documents. We evaluate our results on a dataset comprising over 350K discussion threads and show that our proposed methods outperform state of the art retrieval methods for the task. In particular, method based on non-uniform weighting of thread posts and semantic analysis of the query text perform quite well.