Analysing CSR reporting over the years, company size, region, and sector through dictionary-based text mining

Abstract: As Corporate Social Responsibility (CSR) reports become more prevalent and systematised, there is a strong need to develop approaches that seek to analyse the contents of these reports. In this thesis, we present two valuable contributions. Firstly, we share a rule-based approach that can be a foundation for future supervised learning methods to examine CSR reports and generate predictions. Secondly, we focus on analysing CSR reports topic distributions across developing regions which are hardly covered in the existing literature. The analysis was conducted on a large corpus of 500+ million words over a sample of ~2500 CSR reports gathered from the Global Reporting Initiative (GRI) database for 2012-17. Using reliable CSR business dictionaries, we determined the absolute and relative frequencies for four topics – Employee, Social Community, Environment and Human Rights. We noticed that the four topics studied had a declining trend in the percentage frequencies by 2017. In most cases, the Employee topic was reported the highest among the four topics, followed by Social Community, Environment and Human Rights. This trend was primarily maintained, barring a few exceptions, even when analysed from different dimensions based on company sizes, regions, and sectors. We also compared our derived results with the works of a few previous studies. To know if the reports became easy or hard to understand, we checked the readability through two indices but could not get any clear trend. In the end, we investigated that though there was more attention in the media on the Environment topic around 2016, we did not observe any heightened frequency percentage in the CSR reporting. We believe dictionary-based text mining on CSR reports can be a powerful way to generate insights for different stakeholders. Companies and their management can use this approach to review their CSR communication strategies. Many Government and Non-Government agencies can utilise this approach to check on their policies' effectiveness and future decision-making.