Confluence of social network, social question and answering community, and user reputation model for information seeking and experts generation

Alam, Aftab; Khusro, Shah; Ullah, Irfan; Karim, M. Shuaib

doi:10.1177/0165551516637322

Cited by 13 publications

(8 citation statements)

References 20 publications

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“…It adds to prior work that has been executed for a wide variety of purposes. These range from, for example, the analysis of a system designed to assess contributor reputations on an online question and answering site (Alam et al ., 2017) to the generation of egocentric SNAs for a set of young job-seekers as part of a study of information-seeking behaviours and use (Mowbray et al , 2018). Theoretical papers that demonstrate the modelling of network features have also appeared in the LIS academic press (e.g.…”

Section: Literature Reviewmentioning

confidence: 99%

Long-term community development within a researcher network

Hall

Cruickshank

Ryan

2018

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Purpose The purpose of this paper is to report the results from a study that investigated the extent to which an intervention to develop a community of library and information science (LIS) researchers – the Developing Research Excellence and Methods (DREaM) project – was successful in meeting its main objective three years after its implementation. Of particular interest are factors that support or hinder network longevity. Design/methodology/approach Data were collected by online survey/telephone and focus group. From quantitative data, a social network analysis (SNA) and network diagrams were generated. Focus group discussions were recorded and transcribed, and data from these were analysed manually. Findings Three years after the end of its formal funding period, DREaM endured as a loose but persistent network. Social ties were more important than work ties, and network members with the highest network centrality held roles in academic institutions. Physical proximity between members was important to the maintenance of network ties. Actor status did not appear to have a bearing on network centrality. Research limitations/implications Discussion is limited to consideration of community development amongst core members of the network only. The “manufactured” nature of the DREaM network and unique context in which it was formed have implications for the generalisibility of the findings reported. Practical implications Social infrastructure is key to the long-term health of a network initiative. Continued ad hoc support would strengthen it further. Originality/value The findings add to understanding of factors important to the development of scholarly and learning communities. They extend contributions of earlier work that has deployed SNA techniques in LIS research and research in other fields.

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Section: Literature Reviewmentioning

confidence: 99%

Long-term community development within a researcher network

Hall

Cruickshank

Ryan

2018

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“…Lou et al (2013) argue that the rewards of the reputation system of online Q&A communities could affect the amount of knowledge sharing, but have no significant impact on quality. Other studies have undertaken similar research (Chiu, Hsu, & Wang, 2006;Rafaeli, Raban, & Ravid, 2007;Kim & Oh, 2009;Alam et al, 2017).…”

Section: Literature Reviewmentioning

confidence: 90%

Study on Knowledge Sharing Behavior of Users in Social Q&A Community

Gao¹,

Sun²,

Chen³

et al. 2020

JSSM

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Social Q&A community provides a new way for users to acquire, exchange and share information, so it is of great significance to study user's knowledge sharing behavior in social Q&A community. Based on the theory of social exchange, this paper takes Zhihu community as the research object, analyzes the factors that affect user's knowledge sharing behavior in the social Q&A community, and conducts quantitative research through negative binomial regression. The results show that in Zhihu community, self-display, user recognition, social learning and other dimensions have a significant positive impact on user's knowledge sharing behavior, but the degree of impact is different. Finally, according to the empirical results, the paper puts forward relevant suggestions and measures to better promote user's knowledge sharing behavior.

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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

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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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Confluence of social network, social question and answering community, and user reputation model for information seeking and experts generation

Cited by 13 publications

References 20 publications

Long-term community development within a researcher network

Long-term community development within a researcher network

Study on Knowledge Sharing Behavior of Users in Social Q&A Community

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

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Confluence of social network, social question and answering community, and user reputation model for information seeking and experts generation

Cited by 13 publications

References 20 publications

Long-term community development within a researcher network

Long-term community development within a researcher network

Study on Knowledge Sharing Behavior of Users in Social Q&amp;A Community

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

Contact Info

Product

Resources

About

Study on Knowledge Sharing Behavior of Users in Social Q&A Community