Search results

11 – 20 of over 80000
Article
Publication date: 8 June 2020

Ming Li, Ying Li, YingCheng Xu and Li Wang

In community question answering (CQA), people who answer questions assume readers have mastered the content in the answers. Nevertheless, some readers cannot understand all…

Abstract

Purpose

In community question answering (CQA), people who answer questions assume readers have mastered the content in the answers. Nevertheless, some readers cannot understand all content. Thus, there is a need for further explanation of the concepts that appear in the answers. Moreover, the large number of question and answer (Q&A) documents make manual retrieval difficult. This paper aims to alleviate these issues for CQA websites.

Design/methodology/approach

In the paper, an algorithm for recommending explanatory Q&A documents is proposed. Q&A documents are modeled with the biterm topic model (BTM) (Yan et al., 2013). Then, the growing neural gas (GNG) algorithm (Fritzke, 1995) is used to cluster Q&A documents. To train multiple classifiers, three features are extracted from the Q&A categories. Thereafter, an ensemble classification model is constructed to identify the explanatory relationships. Finally, the explanatory Q&A documents are recommended.

Findings

The GNG algorithm shows good clustering performance. The ensemble classification model performs better than other classifiers. The both effect and quality scores of explanatory Q&A recommendations are high. These scores indicate the practicality and good performance of the proposed recommendation algorithm.

Research limitations/implications

The proposed algorithm alleviates information overload in CQA from the new perspective of recommending explanatory knowledge. It provides new insight into research on recommendations in CQA. Moreover, in practice, CQA websites can use it to help retrieve Q&A documents and facilitate understanding of their contents. However, the algorithm is for the general recommendation of Q&A documents which does not consider individual personalized characteristics. In future work, personalized recommendations will be evaluated.

Originality/value

A novel explanatory Q&A recommendation algorithm is proposed for CQA to alleviate the burden of manual retrieval and Q&A overload. The novel GNG clustering algorithm and ensemble classification model provide a more accurate way to identify explanatory Q&A documents. The method of ranking the explanatory Q&A documents improves the effectiveness and quality of the recommendation. The proposed algorithm improves the accuracy and efficiency of retrieving explanatory Q&A documents. It assists users in grasping answers easily.

Details

Data Technologies and Applications, vol. 54 no. 4
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 24 April 2018

Abhishek Kumar Singh, Naresh Kumar Nagwani and Sudhakar Pandey

Recently, with a high volume of users and user’s content in Community Question Answering (CQA) sites, the quality of answers provided by users has raised a big concern. Finding…

Abstract

Purpose

Recently, with a high volume of users and user’s content in Community Question Answering (CQA) sites, the quality of answers provided by users has raised a big concern. Finding the expert users can be a method to address this problem, which aims to find the suitable users (answerers) who can provide high-quality relevant answers. The purpose of this paper is to find the expert users for the newly posted questions of the CQA sites.

Design/methodology/approach

In this paper, a new algorithm, RANKuser, is proposed for identifying the expert users of CQA sites. The proposed RANKuser algorithm consists of three major stages. In the first stage, folksonomy relation between users, tags, and queries is established. User profile attributes, namely, reputation, tags, and badges, are also considered in folksonomy. In the second stage, expertise scores of the user are calculated based on reputation, badges, and tags. Finally, in the third stage, the expert users are identified by extracting top N users based on expertise score.

Findings

In this work, with the help of proposed ranking algorithm, expert users are identified for newly posted questions. In this paper, comparison of proposed user ranking algorithm (RANKuser) is also performed with other existing ranking algorithms, namely, ML-KNN, rankSVM, LDA, STM CQARank, and EV-based model using performance parameters such as hamming loss, accuracy, average precision, one error, F-measure, and normalized discounted cumulative gain. The proposed ranking method is also compared to the original ranking of CQA sites using the paired t-test. The experimental results demonstrate the effectiveness of the proposed RANKuser algorithm in comparison with the existing ranking algorithms.

Originality/value

This paper proposes and implements a new algorithm for expert user identification in CQA sites. By utilizing the folksonomy in CQA sites and information of user profile, this algorithm identifies the experts.

Details

Data Technologies and Applications, vol. 52 no. 3
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 9 February 2015

Namjoo Choi and Kwan Yi

The purpose of this paper is to examine the general public’s information needs concerning open source software (OSS) and OSS answerers’ motivations for sharing their knowledge of…

Abstract

Purpose

The purpose of this paper is to examine the general public’s information needs concerning open source software (OSS) and OSS answerers’ motivations for sharing their knowledge of OSS in social Q&A.

Design/methodology/approach

Two studies were carried out. In Study 1, a content analysis classifying OSS-related questions posted during December 2005-December 2012 in Yahoo! Answers was employed to investigate the general public’s information needs regarding OSS. In Study 2, an online survey was conducted with OSS answerers in Yahoo! Answers in order to examine what motivates them to share and continue to share their knowledge of OSS in social Q&A. In total, 1,463 invitations were sent out via Yahoo! Answers’ internal e-mail function to those who provided answers to OSS-related questions during September 2009-September 2012. In total, 150 usable surveys were returned and used for data analysis.

Findings

The findings from Study 1 indicate that the general public is most interested in finding out if there is OSS that meets their software need in a certain category (51.4 percent). Other popular question categories include the general description of OSS (15.6 percent), technical issues that they have with OSS (9.8 percent), and the advantages/disadvantages of using OSS (7.0 percent). Results on OSS answerers’ motivations from Study 2 support that all seven motivations identified (i.e. altruism, enjoyment, ideology, learning, reputation, reciprocity, and self-efficacy) are important, with the smallest mean value being 4.42 out of seven (i.e. reciprocity). However, only altruism, ideology, self-efficacy, and enjoyment were found to significantly influence contribution continuance intention.

Practical implications

With social Q&A growing in popularity, OSS communities that look for ways to draw in more users from the general public are recommended to increase their presence in social Q&A. The findings with regard to OSS answerers’ motivations can also help OSS community leaders attract and guide more members who are interested in sharing their OSS knowledge in social Q&A.

Originality/value

By classifying OSS-related questions that are publicly available in Yahoo! Answers, this study offers a breakdown of the general public’s information needs regarding OSS. In addition, results on OSS answerers’ motivations suggest that in order to sustain their member contributions in social Q&A, OSS community leaders should pay more attention to nurturing the motivations that are intrinsic (i.e. altruism, self-efficacy, enjoyment) and integrated (i.e. ideology).

Details

Online Information Review, vol. 39 no. 1
Type: Research Article
ISSN: 1468-4527

Keywords

Article
Publication date: 22 May 2023

Mi Zhou, Bo Meng and Weiguo Fan

The current study aims to investigate the factors that impact the feedback received on answers to questions in social Q&A communities and whether the expertise-required question

Abstract

Purpose

The current study aims to investigate the factors that impact the feedback received on answers to questions in social Q&A communities and whether the expertise-required question influences the role of these factors on the feedback.

Design/methodology/approach

To understand the antecedents and consequences that influence the feedback received on answers to online community questions, the elaboration likelihood model (ELM) is applied in this study. The authors use web data crawling methods and a combination of quantitative analyses. The data for this study came from Zhihu; in total, 353,775 responses were obtained to 1,531 questions, ranging from 49 to 23,681 responses per question. Each answer received 0 to 113,892 likes and 0 to 6,250 comments.

Findings

The answers' cognitive and emotional components and the answerer's influence positively affect user feedback behavior. In addition, the expertise-required question moderates the effects of the answer's cognitive component and emotional component on the user feedback, moderating the effects of the answerer's influence on the user approval feedback.

Originality/value

This study builds upon a limited yet growing body of literature on a theme of great relevance to scholars, practitioners and social media users concerning the effects of the connotation of answers (i.e. their cognitive and emotional components) and the answerer's influence on user feedback (i.e. approval and collaborative feedback) in social Q&A communities. The authors further consider the moderating role of the domain expertise required by the question (expertise-required question). The ELM model is applied to explore the relationships between questions, answers and feedback. The findings of this study add a new perspective to the research on user feedback and have implications for the management of social Q&A communities.

Details

Information Technology & People, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0959-3845

Keywords

Article
Publication date: 29 November 2022

Yung-Ting Chuang and Ching-Hsien Wang

The purpose of this paper is to propose a mobile and social-based question-and-answer (Q&A) system that analyzes users' social relationships and past answering behavior, considers…

Abstract

Purpose

The purpose of this paper is to propose a mobile and social-based question-and-answer (Q&A) system that analyzes users' social relationships and past answering behavior, considers users' interest similarity and answer quality to infer suitable respondents and forwards the questions to users that are willing to give high quality answers.

Design/methodology/approach

This research applies first-order logic (FOL) inference calculation to generate question/interest ID that combines a users' social information, interests and social network intimacy to choose the nodes that can provide high-quality answers. After receiving a question, a friend can answer it, forward it to their friends according to the number of TTL (Time-to-Live) hops, or send the answer directly to the server. This research collected data from the TripAdvisor.com website and uses it for the experiment. The authors also collected previously answered questions from TripAdvisor.com; thus, subsequent answers could be forwarded to a centralized server to improve the overall performance.

Findings

The authors have first noticed that even though the proposed system is decentralized, it can still accurately identify the appropriate respondents to provide high-quality answers. In addition, since this system can easily identify the best answerers, there is no need to implement broadcasting, thus reducing the overall execution time and network bandwidth required. Moreover, this system allows users to accurately and quickly obtain high-quality answers after comparing and calculating interest IDs. The system also encourages frequent communication and interaction among users. Lastly, the experiments demonstrate that this system achieves high accuracy, high recall rate, low overhead, low forwarding cost and low response rate in all scenarios.

Originality/value

This paper proposes a mobile and social-based Q&A system that applies FOL inference calculation to analyze users' social relationships and past answering behavior, considers users' interest similarity and answer quality to infer suitable respondents and forwards the questions to users that are willing to give high quality answers. The experiments demonstrate that this system achieves high accuracy, high recall rate, low overhead, low forwarding cost and low response rate in all scenarios.

Article
Publication date: 18 May 2023

Rongen Yan, Depeng Dang, Hu Gao, Yan Wu and Wenhui Yu

Question answering (QA) answers the questions asked by people in the form of natural language. In the QA, due to the subjectivity of users, the questions they query have different…

Abstract

Purpose

Question answering (QA) answers the questions asked by people in the form of natural language. In the QA, due to the subjectivity of users, the questions they query have different expressions, which increases the difficulty of text retrieval. Therefore, the purpose of this paper is to explore new query rewriting method for QA that integrates multiple related questions (RQs) to form an optimal question. Moreover, it is important to generate a new dataset of the original query (OQ) with multiple RQs.

Design/methodology/approach

This study collects a new dataset SQuAD_extend by crawling the QA community and uses word-graph to model the collected OQs. Next, Beam search finds the best path to get the best question. To deeply represent the features of the question, pretrained model BERT is used to model sentences.

Findings

The experimental results show three outstanding findings. (1) The quality of the answers is better after adding the RQs of the OQs. (2) The word-graph that is used to model the problem and choose the optimal path is conducive to finding the best question. (3) Finally, BERT can deeply characterize the semantics of the exact problem.

Originality/value

The proposed method can use word-graph to construct multiple questions and select the optimal path for rewriting the question, and the quality of answers is better than the baseline. In practice, the research results can help guide users to clarify their query intentions and finally achieve the best answer.

Details

Data Technologies and Applications, vol. 58 no. 1
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 22 October 2019

Ming Li, Lisheng Chen and Yingcheng Xu

A large number of questions are posted on community question answering (CQA) websites every day. Providing a set of core questions will ease the question overload problem. These…

Abstract

Purpose

A large number of questions are posted on community question answering (CQA) websites every day. Providing a set of core questions will ease the question overload problem. These core questions should cover the main content of the original question set. There should be low redundancy within the core questions and a consistent distribution with the original question set. The paper aims to discuss these issues.

Design/methodology/approach

In the paper, a method named QueExt method for extracting core questions is proposed. First, questions are modeled using a biterm topic model. Then, these questions are clustered based on particle swarm optimization (PSO). With the clustering results, the number of core questions to be extracted from each cluster can be determined. Afterwards, the multi-objective PSO algorithm is proposed to extract the core questions. Both PSO algorithms are integrated with operators in genetic algorithms to avoid the local optimum.

Findings

Extensive experiments on real data collected from the famous CQA website Zhihu have been conducted and the experimental results demonstrate the superior performance over other benchmark methods.

Research limitations/implications

The proposed method provides new insight into and enriches research on information overload in CQA. It performs better than other methods in extracting core short text documents, and thus provides a better way to extract core data. The PSO is a novel method used for selecting core questions. The research on the application of the PSO model is expanded. The study also contributes to research on PSO-based clustering. With the integration of K-means++, the key parameter number of clusters is optimized.

Originality/value

The novel core question extraction method in CQA is proposed, which provides a novel and efficient way to alleviate the question overload. The PSO model is extended and novelty used in selecting core questions. The PSO model is integrated with K-means++ method to optimize the number of clusters, which is just the key parameter in text clustering based on PSO. It provides a new way to cluster texts.

Details

Data Technologies and Applications, vol. 53 no. 4
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 4 July 2023

Lijuan Luo, Yuwei Wang, Siqi Duan, Shanshan Shang, Baojun Ma and Xiaoli Zhou

Based on the perspectives of social capital, image motivation and motivation affordances, this paper explores the direct and moderation effects of different kinds of motivations…

Abstract

Purpose

Based on the perspectives of social capital, image motivation and motivation affordances, this paper explores the direct and moderation effects of different kinds of motivations (i.e. relationship-based motivation, community-based motivation and individual-based motivation) on users' continuous knowledge contributions in social question and answer (Q&A) communities.

Design/methodology/approach

The authors collect the panel data of 10,193 users from a popular social Q&A community in China. Then, a negative binomial regression model is adopted to analyze the collected data.

Findings

The paper demonstrates that social learning, peer recognition and knowledge seeking positively affect users' continuous contribution behaviors. However, the results also show that social exposure has the opposite effect. In addition, self-presentation is found to moderate the influence of social factors on users' continuous use behaviors, while the moderation effect of motivation affordances has no significance.

Originality/value

First, this study develops a comprehensive motivation framework that helps gain deeper insights into the underlying mechanism of knowledge contribution in social Q&A communities. Second, this study conducts panel data analysis to capture the impacts of motivations over time, rather than intentions at a fixed time point. Third, the findings can help operators of social Q&A communities to optimize community norms and incentive mechanisms.

Details

Information Technology & People, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0959-3845

Keywords

Article
Publication date: 23 November 2012

Daqing He, Dan Wu, Zhen Yue, Anna Fu and Kim Thien Vo

This paper aims to identify the opinions of undergraduate students on the importance of internet‐based information sources when they undertake academic tasks.

3641

Abstract

Purpose

This paper aims to identify the opinions of undergraduate students on the importance of internet‐based information sources when they undertake academic tasks.

Design/methodology/approach

Based on a set of identified typical academic tasks for undergraduate students, three research questions were designed around the students' usage and views of information resources for completing these tasks. Web‐accessible questionnaires were used to collect data from participants in two universities in the USA and China, and the data were analyzed using quantitative methods, which included several statistic methods.

Findings

The results confirm that undergraduate students use different information resources for various academic tasks. In their tasks, online electronic resources including search engines are the most commonly used resources, particularly for complex academic tasks. Social networking sites are not used for the students' individual academic tasks, and traditional resources still play equal or more important roles in certain specific academic tasks. Students in collaborative tasks look for resources that make it easy to share documents. Participants from the two countries also exhibit interesting and important differences in their usage of information resources.

Originality/value

This study examines undergraduate students' usages and views of different information resources in their various academic tasks, and pays special attention to the impacts of being from their different countries. The study also considers both students' individual academic tasks and collaborative tasks. This study is an invaluable addition to the information seeking behaviour literature.

Article
Publication date: 16 February 2022

Jiahua Jin, Tingting Zhang and Xiangbin Yan

Online Q&A communities have been widely highlighted as an important knowledge exchange market. Although motivations for users’ initial knowledge-seeking behavior have been widely…

Abstract

Purpose

Online Q&A communities have been widely highlighted as an important knowledge exchange market. Although motivations for users’ initial knowledge-seeking behavior have been widely investigated, the factors that affect online Q&A users’ continued knowledge-seeking behavior are still vague. This study aims to investigate the factors that affect users continuously seeking knowledge from online social Q&A communities.

Design/methodology/approach

Based on social information processing theory, social capital theory, social exchange theory and social cognitive theory, this study used a negative binomial regression model to explore what would affect people’s continued knowledge-seeking behavior. Empirical data was collected from a popular Chinese online social Q&A community.

Findings

The results indicate that while previous knowledge sharing behavior, peer responses for previous seeking behavior, identity-based trust have a positive impact on knowledge-seeking behaviors, social exposure has a negative impact. In addition, self-presentation negatively moderates the relationship between social exposure and knowledge-seeking behavior.

Originality/value

This study contributed to the theoretical basis for knowledge-seeking behavior in online Q&A communities. The research findings can be used to derive guidelines for the development and operation of online social Q&A communities.

Details

Information Discovery and Delivery, vol. 51 no. 1
Type: Research Article
ISSN: 2398-6247

Keywords

11 – 20 of over 80000