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Purpose In community question and answer (CQA) services, because of user subjectivity and the limits of knowledge, the distribution of answer quality can vary drastically – from highly related to irrelevant or even spam answers. Previous studies of CQA portals have faced two important issues: answer quality analysis and spam answer filtering. Therefore, the purposes of this study are to filter spam answers in advance using two-phase identification methods and then automatically classify the different types of question and answer (QA) pairs by deep learning. Finally, this study proposes a comprehensive study of answer quality prediction for different types of QA pairs. Design/methodology/approach This study proposes an integrated model with a two-phase identification method that filters spam answers in advance and uses a deep learning method [recurrent convolutional neural network (R-CNN)] to automatically classify various types of questions. Logistic regression (LR) is further applied to examine which answer quality features significantly indicate high-quality answers to different types of questions. Findings There are four prominent findings. (1) This study confirms that conducting spam filtering before an answer quality analysis can reduce the proportion of high-quality answers that are misjudged as spam answers. (2) The experimental results show that answer quality is better when question types are included. (3) The analysis results for different classifiers show that the R-CNN achieves the best macro-F1 scores (74.8%) in the question type classification module. (4) Finally, the experimental results by LR show that author ranking, answer length and common words could significantly impact answer quality for different types of questions. Originality/value The proposed system is simultaneously able to detect spam answers and provide users with quick and efficient retrieval mechanisms for high-quality answers to different types of questions in CQA. Moreover, this study further validates that crucial features exist among the different types of questions that can impact answer quality. Overall, an identification system automatically summarises high-quality answers for each different type of questions from the pool of messy answers in CQA, which can be very useful in helping users make decisions.

Purpose In academic work, it is important to identify a specific domain of research. Many researchers may look to conference issues to determine interesting or new topics. Furthermore, conference issues can help researchers identify current research trends in their field and learn about cutting-edge developments in their area of specialization. However, so much conference information is published online that it can be difficult to navigate and analyze in a meaningful or productive way. Hence, the use of knowledge management (KM) could be a way to resolve these issues. In KM, ontology is widely adopted, but most ontology construction methods do not consider social information between target users. Therefore, this study aims to propose a novel method of constructing research topic maps using an open directory project (ODP) and social information. Design/methodology/approach The approach is to incorporate conference information (i.e. title, keywords and abstract) as sources and to consider the ways in which social information automatically produces research topic maps. The methodology can be divided into four modules: data collection, element extraction, social information analysis and visualization. The data collection module collects the required conference data from the internet and performs pre-processing. Then, the element extraction module extracts topics, associations and other basic elements of topic maps while considering social information. Finally, the results will be shown in the visualization module for researchers to browse and search. Findings The results of this study propose three main findings. First, creating topic maps with the ODP category information can help capture a richer set of classification associations. Second, social information should be considered when constructing topic maps. This study includes the relationship among different authors and topics to support information in social networks. By considering social information, such as co-authorship/collaborator, this method helps researchers find research topics that are unfamiliar but interesting or potential cooperative opportunities in the future. Third, this study presents topic maps that show a clear and simple pathway in interested domain knowledge. Research limitations implications First, this study analyzes and collects conference information, including the titles, keywords and abstracts of conference papers, so the data set must include all of the abovementioned information. Second, social information only analyzes co-authorship associations (collabship associations); other social information could be extracted in the future study. Third, this study only analyzes the associations between topics. The intensity of associations is not discussed in the study. Originality/value The study will have a great impact on learned societies because it bridges the gap between theory and practice. The study is useful for researchers who want to know which conferences are related to their research. Moreover, social networks can help researchers expand and diversify their research.