A Study of Question Effectiveness Using Reddit "Ask Me Anything" Threads

Arumae, Kristjan; Qi, Guo-Jun; Liu, Fei

doi:10.48550/arxiv.1805.10389

Cited by 1 publication

(2 citation statements)

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“…Hassan et al [25] developed SOQDE, a supervised learning based model and performed an empirical study to determine how the difficulty of a question impacts its outcome. Arumae et al [1] used variable-length context convolutional neural network model to investigate whether it is possible to predict if question will be answered. Chang and Pal [10] developed a routing mechanism that uses availability and expertise of the users to recommend answerers to a question.…”

Section: Difficulty Ranking Of Questionsmentioning

confidence: 99%

“…Such platforms have recently gained huge attention due to various features such as quick response from the contributers, quick access to the experts of different topics, succinct explanation, etc. For instance, in August 2010, Stack Overflow accommodated 300k users and 833k questions; these numbers have currently jumped to 8.3m users and 15m questions posted 1 . This in turn provides tremendous opportunity to the researchers to consider such CQA services as large knowledge bases to solve various interesting problems [43,68,74].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Thukral¹,

Pandey²,

Gupta³

et al. 2019

Preprint

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Automatic estimation of relative difficulty of a pair of questions is an important and challenging problem in community question answering (CQA) services. There are limited studies which addressed this problem. Past studies mostly leveraged expertise of users answering the questions and barely considered other properties of CQA services such as metadata of users and posts, temporal information and textual content. In this paper, we propose DiffQue, a novel system that maps this problem to a network-aided edge directionality prediction problem. DiffQue starts by constructing a novel network structure that captures different notions of difficulties among a pair of questions. It then measures the relative difficulty of two questions by predicting the direction of a (virtual) edge connecting these two questions in the 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 four datasets) with human annotated ground-truth show that DiffQue outperforms four state-of-the-art methods by a significant margin (28.77% higher F 1 score and 28.72% higher AUC than the best baseline). As opposed to the other baselines, (i) DiffQue appropriately responds to the training noise, (ii) DiffQue is capable of adapting multiple domains (CQA datasets), and (iii) DiffQue can efficiently handle 'cold start' problem which may arise due to the lack of information for newly posted questions or newly arrived users.

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Section: Difficulty Ranking Of Questionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%