Social Licence to Operate Scores using Social Media Data

Funded by CSIRO/Data61 R+ (previously known as OCE) Postdoctoral Fellow Award
Partners:	C.Paris, R. Sparks, K. Moffat, S. Nepal, Judy Kay and E. Bertino
Duration:	2016 – 2019

Social Licence to Operate (SLO) is a concept that has arisen out of the mining sector.  Major impacts on SLO are company governance (such as trust), their financial risk management, and environmental, social and cultural social concerns. SLO is measured using a value tree where the overall value of the mine operating is broken down into its social value, its economic and employment value, its environmental value, its cultural/ community value, and its leadership value.  These are further broken down into attributes that influence them. For example, leadership value is linked to mine management such as the level they are trusted, their risk management, their ability to manage changes, their dispute resolution process, and how well they manage resources. Identifying SLO risk as early as possible is crucial for managing company risk.

Social Media (SM) is widely used by individuals, mining organisations, advocacy groups, and governments to disseminate and share information, offer opinions, engage in discussions, share feelings, report daily activities and form communities.  This disruptive communication is now considered a crucial part of any company’s communicative strategy, and an important information gathering tool.  SM analytics tools have been developed to feed into decision support systems, and these are now in use in many domains. For our purposes here, the real-time aspect of the information on SM and its use to express opinions are attractive for monitoring a (mining) company’s SLO over time.  As more and more companies are sharing their operational plans and strategies on social media, SM provides rich information about a company and how it is perceived by the public. Such SM communications thus offer rich data about the company’s SLO.  The data, both in terms of content and interactions, is vast (e.g., 6 thousands messages (tweets) per minute — Twitter Statistics, 2015).  The aim of the project is to develop algorithms that mine social media to estimate SLO.

This project models information flow and influence in SM, identifying who are the influential people on what topic, and quantify their influence on SLO scores.  In doing so, the project develops a theoretical model for information flow and influence in SM, with a specific focus on two questions: what features of SM data need to be considered to model SLO and how individuals influence SLO over time?