Contents lists available at ScienceDirect Advanced Engineering Informatics journal homepage: www.elsevier.com/locate/aei Full length article Game-based crowdsourcing to support collaborative customization of the definition of sustainability Mazdak Nik Bakht a , Tamer E. El-Diraby b, ⁎ , Moein Hossaini c a Dept. of Building, Civil and Environmental Engineering, Concordia University, Montreal, QC, Canada b Dept. of Civil and Mineral Engineering, University of Toronto, Toronto, ON, Canada c Northwestern University, United States ARTICLE INFO Keywords: Sustainability Social media Urban infrastructure Computational linguistic Machine learning Crowdsourcing ABSTRACT Successful adoption and management of sustainable urban systems hinges on the community embracing these systems. Capturing citizens’ ideas, views, and assessments of the built environment will be essential to this goal. In collaborative city planning, these are qualified and valued forms of partial knowledge that should be col- lectively used to shape the decision making process of urban planning. Among other tools, social media and online social network analytics can provide means to capture elements of such a distributed knowledge. While a structured definition of sustainability (normally dictated in a top-down fashion) may not sufficiently respond well to the pluralist nature of such knowledge acquisition; dealing with the unstructured community inputs, assessments and contributions on social media can be confusing. We can detect fully relevant topics/ideas in community discussions; but they typically suffer from lack of coherence. In this paper, we advocate the use of a semi-structured approach for capturing, analyzing, and interpreting citizens’ inputs. Public officials and professionals can develop the main elements (topical aspects) of sustain- ability, which can act as the skeleton of a taxonomy. It is however, the community inputs/ideas (in our case collected via social media and parsed), that can shape-up that skeleton and augment those topical aspects with adding the required semantic depth. In more specific terms, we collected tweets for four urban infrastructure mega-projects in North America. Then we used a game-with-a-purpose to crowdsource the identification of topics for a training set of tweets. This was then used to train machine learning algorithms to cluster the rest of collected tweets. We studied the semantic (finding the topics) of tweets as well as their sentiment (in terms of being opposing or supportive of a project). Our classification tested different decision trees with different topic hierarchies. We considered/extracted eight different linguistic features in studying contents of a tweet. Finally, we examined the accuracy of three algorithms in classifying tweets according to the sequence in the tree, and based on the extracted features. These are: K-nearest neighbors, Naïve Bayes classifiers and Support Vector Machines (SVM). Respective to our data set, SVM outperformed other algorithms. Semantic analysis was insensitive to the depth/number of linguistic features considered. In contrast, sentiment analysis was enhanced when part of speech (PoS) was tracked. Interestingly, our work shows that considering the topic (semantic) of a tweet helped enhance the accuracy of sentiment analysis: including topical class as a feature in conducting sentiment analysis results in higher accuracies. This could be used as means to detect the evolution of community opinion: that topic-based social networks are evolving within the communities tweeting about urban projects. It could also be used to identify the topics of top priority to the community or the ones that have the widest spread of views. In our case, these were mainly the impacts of the design and engineering features on social issues. 1. Introduction Decision making in green and smart cities is predicated on assuring sustainability on the one hand, and embracing more democratic project evaluation processes on the other hand. This gives rise to the formation of extended networks of decision makers/decision collaborators which involves the public communities actively and on an equal footing with official decision makers. The existence of informal sub-networks (of https://doi.org/10.1016/j.aei.2018.08.019 Received 21 March 2018; Received in revised form 14 June 2018; Accepted 31 August 2018 ⁎ Corresponding author. E-mail addresses: mazdak.nikbakht@concordia.ca (M. Nik Bakht), tamer@ecf.utoronto.ca (T.E. El-Diraby), moeinhosseini2020@u.northwestern.edu (M. Hossaini). Advanced Engineering Informatics 38 (2018) 501–513 1474-0346/ © 2018 Elsevier Ltd. All rights reserved. T