Topic modeling for infrastructure-related discussions in online social media Nik-Bakht, M.; El-Diraby, T. E.
Decision making for construction of modern civil infrastructure not only involves internal stakeholders, but also aims to include interests of as many external stakeholders as possible. In mega-projects, complexity and diversity of stakeholders call for more advanced communication tools and channels. Extensive prevalence of social web as a two-way communication channel during the last decade has caused a paradigm shift in communication among the e-society, and this has attracted the attention of decision makers in the domain of urban infrastructure among other domains. Although having a wide public outreach, the open and unstructured nature of inputs from the e-society results in chaos and makes it difficult to distil knowledge from the contents communicated by the public. This paper presents tools from topic modeling to process such an unstructured data collected from online social media into information which can be plugged into the process of decision making. We use k-means clustering to cluster followers of an infrastructure project on micro-blogging website Twitter based on semantic similarity among their user profile descriptions. This helps profiling the main groups of followers of the infrastructure project and can provide decision makers with valuable hints regarding typology of external stakeholders. We also extend our analysis to project-related tweets through Latent Semantic Indexing, and find the main topics discussed. The latter guide help decision makers understand the public’s major vested interests in the project. We have applied the proposed method to a Light Rail Transit (LRT) mega-project in Toronto, Ontario and have discussed the results.
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