A Natural Language Processing Approach to Social License Management

Robert G. Boutilier and Kyle Bahr
Additional contact information
Robert G. Boutilier: Stakeholder360, Cuernavaca 62810, Mexico
Kyle Bahr: Graduate School of Environmental Studies, Tohoku University, Sendai 980-0845, Japan

Sustainability, 2020, vol. 12, issue 20, 1-12

Abstract: Dealing with the social and political impacts of large complex projects requires monitoring and responding to concerns from an ever-evolving network of stakeholders. This paper describes the use of text analysis algorithms to identify stakeholders’ concerns across the project life cycle. The social license (SL) concept has been used to monitor the level of social acceptance of a project. That acceptance can be assessed from the texts produced by stakeholders on sources ranging from social media to personal interviews. The same texts also contain information on the substance of stakeholders’ concerns. Until recently, extracting that information necessitated manual coding by humans, which is a method that takes too long to be useful in time-sensitive projects. Using natural language processing algorithms, we designed a program that assesses the SL level and identifies stakeholders’ concerns in a few hours. To validate the program, we compared it to human coding of interview texts from a Bolivian mining project from 2009 to 2018. The program’s estimation of the annual average SL was significantly correlated with rating scale measures. The topics of concern identified by the program matched the most mentioned categories defined by human coders and identified the same temporal trends.

Keywords: social license; social acceptance; stakeholder; text analysis; natural language processing; sentiment analysis; risk management; sociopolitical risk; bag-of-words (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:20:p:8441-:d:427417

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