Project collective mind: Unlocking project discussion networks

- Projects Discussion Networks (PDNs) are social networks formed through complex interactions among the project team in different phases of a project lifecycle.
- Analysis of PDN combines answers to the four questions about the people and groups having interest in the project (Who, How influential, Which topics, and What sentiment)
- The social network of knower agents (people) is overlaid by (semantic) networks of exchanged content encapsulating not only their views but also their knowledge/experience.
- The paper presents an approach to combines analysis of ideas and users' connectivity to provide a map of collective opinion and opinion dynamics
- Analysis of the social and semantic networks provides insight regarding team formation, project governance, information flow and their impacts on the opinion dynamics.

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.