Social question and answer sites: the story so far

Khusro, Shah; Alam, Aftab; Khalid, Shah

doi:10.1108/prog-11-2015-0073

Cited by 8 publications

(6 citation statements)

References 60 publications

(49 reference statements)

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“…Many recent studies on the quality of answers have focused on generic social Q&A sites, especially Yahoo! Answers (Blooma et al, 2012;Chua and Banerjee, 2013;Fichman, 2011;John et al, 2011;Khusro et al, 2017;Kim and Oh, 2009). They have used peer judgments to identify the characteristics of high-quality answers (Chua and Banerjee, 2013;Fichman, 2011;John et al, 2011;Wang et al, 2017;Yi, 2018).…”

Section: Quality Of Information On Social Mediamentioning

confidence: 99%

Researchers' judgment criteria of high-quality answers on academic social Q&A platforms

Zhang

et al. 2020

OIR

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PurposeThrough a two-stage survey, this paper examines how researchers judge the quality of answers on ResearchGate Q&A, an academic social networking site.Design/methodology/approachIn the first-stage survey, 15 researchers from Library and Information Science (LIS) judged the quality of 157 answers to 15 questions and reported the criteria that they had used. The content of their reports was analyzed, and the results were merged with relevant criteria from the literature to form the second-stage survey questionnaire. This questionnaire was then completed by researchers recognized as accomplished at identifying high-quality LIS answers on ResearchGate Q&A.FindingsMost of the identified quality criteria for academic answers—such as relevance, completeness, and verifiability—have previously been found applicable to generic answers. The authors also found other criteria, such as comprehensiveness, the answerer's scholarship, and value-added. Providing opinions was found to be the most important criterion, followed by completeness and value-added.Originality/valueThe findings here show the importance of studying the quality of answers on academic social Q&A platforms and reveal unique considerations for the design of such systems.

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Section: Quality Of Information On Social Mediamentioning

confidence: 99%

Researchers' judgment criteria of high-quality answers on academic social Q&A platforms

Zhang

et al. 2020

OIR

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“…In literature, the word 'trust' is commonly used as a general term for 'security' and 'privacy' [402]. Trust is a social phenomenon where the user has expectations from the IVA service provider and willing to take action (subscription) on the belief based on evidence that the expected behavior occurs [403], [404]. In the cloud environment, security and privacy are playing an active role in the trust-building.…”

Section: Privacy Security and Trustmentioning

confidence: 99%

Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues

2020

Self Cite

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The proliferation of multimedia devices over the Internet of Things (IoT) generates an unprecedented amount of data. Consequently, the world has stepped into the era of big data. Recently, on the rise of distributed computing technologies, video big data analytics in the cloud has attracted the attention of researchers and practitioners. The current technology and market trends demand an efficient framework for video big data analytics. However, the current work is too limited to provide a complete survey of recent research work on video big data analytics in the cloud, including the management and analysis of a large amount of video data, the challenges, opportunities, and promising research directions. To serve this purpose, we present this study, which conducts a broad overview of the state-of-the-art literature on video big data analytics in the cloud. It also aims to bridge the gap among large-scale video analytics challenges, big data solutions, and cloud computing. In this study, we clarify the basic nomenclatures that govern the video analytics domain and the characteristics of video big data while establishing its relationship with cloud computing. We propose a service-oriented layered reference architecture for intelligent video big data analytics in the cloud. Then, a comprehensive and keen review has been conducted to examine cutting-edge research trends in video big data analytics. Finally, we identify and articulate several open research issues and challenges, which have been raised by the deployment of big data technologies in the cloud for video big data analytics. To the best of our knowledge, this is the first study that presents the generalized view of the video big data analytics in the cloud. This paper provides the research studies and technologies advancing the video analyses in the era of big data and cloud computing. INDEX TERMS big data, intelligent video analytics, cloud-based video analytics system, video analytics survey, deep learning, distributed computing, intermediate results orchestration, cloud computing.

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“…From the perspective of incentive mechanisms, if the answerer is rewarded for answering questions, he or she will prefer to answer more questions positively (Cavusoglu et al , 2015). Awarding points (Farzan et al , 2008; Harper et al , 2007), social comparisons (Harper et al , 2007), level titles (Khusro et al , 2017), money (Khusro et al , 2017) and granting site privileges (Khusro et al , 2017) are used to motivate long-term participation of community members (Khusro et al , 2017).…”

Section: Key Research Issues Of Knowledge Payment Platformmentioning

confidence: 99%

Knowledge payment research: status quo and key issues

Tian-mei

et al. 2019

IJCS

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Purpose Knowledge sharing has entered the stage of knowledge payment with the typical models of paid Q&A, live session, paid subscription, course column and community service. Numerous knowledge suppliers have begun to pour into the knowledge payment market, and users' willingness to pay for premium content has increased. However, the academic research on knowledge payment has just begun. Design/methodology/approach In this paper, the authors searched several bibliographic databases using keywords such as “knowledge payment”, “paid Q&A”, “pay for answer”, “social Q&A”, “paywall” and “online health consultation” and selected papers from aspects of research scenes, research topics, etc. Finally, a total of 116 articles were identified for combing studies. Findings This study found that in the early research, scholars paid attention to the definition of knowledge payment concept and the discrimination of typical models. With the continuous enrichment of research literature, the research direction has gradually been refined into three main branches from the perspective of research objects, i.e. knowledge provider, knowledge demander and knowledge payment platform. Originality/value This paper focuses on discussing and sorting out the key research issues from these three research genres. Finally, the authors found out conflicting and contradictory research results and research gaps in the existing research and then put forward the urgent research topics.

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Social question and answer sites: the story so far

Cited by 8 publications

References 60 publications

Researchers' judgment criteria of high-quality answers on academic social Q&A platforms

Researchers' judgment criteria of high-quality answers on academic social Q&A platforms

Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues

Knowledge payment research: status quo and key issues

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