دانلود مقاله ISI انگلیسی شماره 151550
ترجمه فارسی عنوان مقاله

ذهن جمعی پروژه: باز کردن شبکه های گفتگو پروژه

عنوان انگلیسی
Project collective mind: Unlocking project discussion networks
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
151550 2017 20 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Automation in Construction, Volume 84, December 2017, Pages 50-69

ترجمه کلمات کلیدی
تجزیه و تحلیل شبکه شبکه، توییتر، تجزیه و تحلیل معنایی، مدیریت و نظارت بر پروژه، نقشه برداری مشارکت کننده، مشارکت جوامع،
کلمات کلیدی انگلیسی
Social network analysis; Twitter; Semantic analysis; Project management and monitoring; Stakeholder mapping; Community engagement;
پیش نمایش مقاله
پیش نمایش مقاله  ذهن جمعی پروژه: باز کردن شبکه های گفتگو پروژه

چکیده انگلیسی

A project discussion network is a space where project stakeholders form relationships among each other and share information about the project. Virtual discussion networks may refer to networks of e-mails, document exchange and social media (such as Twitter, Facebook, YouTube, etc.). As such, both social linkages and semantics of the exchanged content must be considered in analysis of such networks. The proposed framework in this study aims to analyze both the social and semantic aspects of these networks. We developed the framework through analysis of the social networks formed around Twitter accounts of infrastructure megaprojects. To assure relevance to construction research and practices, three objectives guided our analysis: relaying on a large and diversified data corpus from construction projects; testing the applicability and usage of a set of relevant algorithms to the context of construction project management; and linking the results of data analysis and algorithm evaluation to the conditions of construction projects at hand. In examining algorithms for detecting sub-communities, the Louvain fast unfolding modularity maximization was more suitable in detecting project relevant sub-groups. For assessing the relative influence of actors, PageRank algorithm performed better than centrality measures. For extracting key terms, we found that modifying the term frequency-inverse document frequency (TF-IDF) measure to incorporate the relative importance of the source nodes enhances the relevance of extracted terms. Obliviously, Twitter networks are only one type of project networks that can cover a limited/biased sample of participants. Their analysis should be one component of the overall project network analysis. We believe that the proposed framework has the same level of applicability to internal networks of project teams as well as non-Twitter networks.