Sustainability indicators are metrics that are used to assess and track sustainable development, such as the number of people living in poverty or the conservation status of endangered species. Defining sustainability indicators is challenging because the studies are often expensive and time consuming, the resulting indicators are difficult to track, and they usually have limited social input and acceptance, which is a critical element of the social component of sustainability. The central premise of this work is to explore the feasibility of identifying, tracking and reporting sustainability indicators by analyzing unstructured digital news articles with text mining methods. Using San Mateo County, California, as a case study, a non-mutually exclusive supervised classification algorithm with natural language processing techniques is applied to analyze sustainability content in news articles and compare the results with annual sustainability indicator reports created by Sustainable San Mateo County (SSMC) using traditional methods. The results showed that the text mining approach could identify all of the indicators highlighted as important in the SSMC reports and the method has potential for identifying region-specific sustainability indicators, as well as providing insights on the underlying causes of sustainability problems. Some chronic problems that are considered less newsworthy proved more difficult to track with news articles and may be better monitored using other types of online media, such as blogs and reports. Such use of online media can improve the incorporation of society’s values in the process of selecting and tracking the indicators, a component that had been missing in previous approaches.
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Advancing sustainability indicators through text mining: a feasibility demonstration
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