ACLSTM:A Novel Method for CQA Answer Quality Prediction Based on Question-Answer Joint Learning

Given the limitations of the community question answering(CQA)answer quality prediction method in measuring the semantic information of the answer text,this paper proposes an answer quality prediction model based on the question-answer joint learning(ACLSTM).The attention mechanism is used to obtain the dependency relationship between the Question-and-Answer(Q&A)pairs.Convolutional Neural Network(CNN)and Long Short-term Memory Network(LSTM)are used to extract semantic features of Q&A pairs and calculate their matching degree.Besides,answer semantic representation is combined with other effective extended features as the input representation of the fully connected layer.Compared with other quality prediction models,the ACLSTM model can effectively improve the prediction effect of answer quality.In particular,the mediumquality answer prediction,and its prediction effect is improved after adding effective extended features.Experiments prove that after the ACLSTM model learning,the Q&A pairs can better measure the semantic match between each other,fully reflecting the model’s superior performance in the semantic information processing of the answer text.

Analysis of community question-answering issues via machine learning and deep learning:State-of-the-art review

Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic interest.Scholars have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the ever-growing volume of content that CQAs engender.To clarify the current state of the CQA literature that has used ML and DL,this paper reports a systematic literature review.The goal is to summarise and synthesise the major themes of CQA research related to(i)questions,(ii)answers and(iii)users.The final review included 133 articles.Dominant research themes include question quality,answer quality,and expert identification.In terms of dataset,some of the most widely studied platforms include Yahoo!Answers,Stack Exchange and Stack Overflow.The scope of most articles was confined to just one platform with few cross-platform investigations.Articles with ML outnumber those with DL.Nonetheless,the use of DL in CQA research is on an upward trajectory.A number of research directions are proposed.

Influencing factors of answer adoption in social Q&A communities from users' perspective: Taking Zhihu as an example

Purpose: Taking Zhihu as the object for a case study, we intend to analyze the key factors that have affected users on adopting answers in social Q&A(SQA) websites.Design/methodology/approach: With information adoption model(IAM) as the theoretical foundation and widely accepted evaluation criteria for answer quality in SQA sites as variables, we constructed a factor model that has influenced SQA community users to adopt offered answers. With the partial least squares(PLS) technique, our model was then empirically tested through a sample of 311 Zhihu users.Findings: Our results showed that answer usefulness is the most effective variable, and answer interactivity and answer entertainment both have positive and significant impacts on users’ attitude to adopt answers in an SQA community. Except for novelty, other three components of answer quality, i.e. knowledge, reliability, and solution to the problem have all significant effect on answer usefulness.Research limitations: First, due to the limited sample size, it is still questionable if our research results based on Zhihu could be applied to other SQA communities. Second, our questionnaires were mainly designed to investigate how users felt about the answers in an SQA site, but did not differentiate the content of the answer itself.Practical implications: As a three-year-old SQA platform, Zhihu has developed very quickly with its high-quality answers and public intellectual users, and has been regarded as one of the representatives of fast emerging Chinese SQA communities in recent years. Our studycould help shed light on users’ information sharing and knowledge adoption behaviors in a Chinese SQA site, such as Zhihu. Originality/value: Compared with previous studies on answer quality assessments in SQA sites and on information adoption model, to the best of our knowledge, this is one of the pioneer studies which combined answer qualities with users’ intention of adopting SQA answers. Our study on user answer adoption in Zhihu community could further develop the theory of IAM. This study showed that answer usefulness is the most important motivation of Zhihu users in the process of adopting answers.