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Automatic estimation of relative difficulty of a pair of questions is animportant and challenging problem in community question answering (CQA)services. There are limited studies which addressed this problem. Past studiesmostly leveraged expertise of users answering the questions and barelyconsidered other properties of CQA services such as metadata of users andposts, temporal information and textual content. In this paper, we proposeDiffQue, a novel system that maps this problem to a network-aided edgedirectionality prediction problem. DiffQue starts by constructing a novelnetwork structure that captures different notions of difficulties among a pairof questions. It then measures the relative difficulty of two questions bypredicting the direction of a (virtual) edge connecting these two questions inthe network. It leverages features extracted from the network structure,metadata of users/posts and textual description of questions and answers.Experiments on datasets obtained from two CQA sites (further divided into fourdatasets) with human annotated ground-truth show that DiffQue outperforms fourstate-of-the-art methods by a significant margin (28.77% higher F1 score and28.72% higher AUC than the best baseline). As opposed to the other baselines,(i) DiffQue appropriately responds to the training noise, (ii) DiffQue iscapable of adapting multiple domains (CQA datasets), and (iii) DiffQue canefficiently handle 'cold start' problem which may arise due to the lack ofinformation for newly posted questions or newly arrived users.

DiffQue: Estimating Relative Difficulty of Questions in Community Question Answering Services