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As climate change, environmental, social, and governance (ESG), along with sustainability, become increasingly crucial for businesses and society, there is a noticeable scarcity of information and transparency regarding corporate practices. Often, government agency enforcement actions lead to litigation and are ultimately resolved by court decisions. Moreover, in instances when there is perceived inadequacy in government enforcement, citizens frequently turn to the courts for preventive judgments against businesses or agencies. In an effort to shed light on the multifaceted aspects of climate change, we adopted a novel, exploratory approach to analyze climate change-related litigation cases. Utilizing a blend of machine learning-based text analytics, we have extracted key insights from individual case narratives. Our analysis encompassed over four hundred cases from the Westlaw database through various keyword searches. The emergent topics from our case dataset revolved around four critical environmental themes: forest, land, water, and air emissions. Our findings provide insight into the nature and dimensions of climate change and also carry significant policy implications, laying the groundwork for future research in this domain. [ABSTRACT FROM AUTHOR]

While data breaches are a frequent and universal phenomenon, the characteristics and dimensions of data breaches are unexplored. In this novel exploratory research, we apply machine learning (ML) and text analytics to a comprehensive collection of data breach litigation cases to extract insights from the narratives contained within these cases. Our analysis shows stakeholders (e.g., litigants) are concerned about major topics related to identity theft, hacker, negligence, FCRA (Fair Credit Reporting Act), cybersecurity, insurance, phone device, TCPA (Telephone Consumer Protection Act), credit card, merchant, privacy, and others. The topics fall into four major clusters: "phone scams", "cybersecurity", "identity theft", and "business data breach". By utilizing ML, text analytics, and descriptive data visualizations, our study serves as a foundational piece for comprehensively analyzing large textual datasets. The findings hold significant implications for both researchers and practitioners in cybersecurity, especially those grappling with the challenges of data breaches. [ABSTRACT FROM AUTHOR]

Corporations have embraced the idea of corporate environmental, social, and governance (ESG) under the general framework of sustainability. Studies have measured and analyzed the impact of internal sustainability efforts on the performance of individual companies, policies, and projects. This exploratory study attempts to extract useful insight from shareholder sustainability resolutions using machine learning-based text analytics. Prior research has studied corporate sustainability disclosures from public reports. By studying shareholder resolutions, we gain insight into the shareholders' perspectives and objectives. The primary source for this study is the Ceres sustainability shareholder resolution database, with 1737 records spanning 2009–2019. The study utilizes a combination of text analytic approaches (i.e., word cloud, co-occurrence, row-similarities, clustering, classification, etc.) to extract insights. These are novel methods of transforming textual data into useful knowledge about corporate sustainability endeavors. This study demonstrates that stakeholders, such as shareholders, can influence corporate sustainability via resolutions. The incorporation of text analytic techniques offers insight to researchers who study vast collections of unstructured bodies of text, improving the understanding of shareholder resolutions and reaching a wider audience. [ABSTRACT FROM AUTHOR]