We employ computer-based textual analysis to examine disclosure patterns for a sample of US corporate social responsibility (CSR) reports from the period 2002-2016. Starting from 466 features commonly used in computational linguistics, our results show that the linguistics or disclosure patterns in CSR reports can be used to accurately predict the actual CSR performance type of CSR reporters. Specifically, we find that the two most commonly used disclosure characteristics, number of words and number of sentences, alone can be used to predict reporting firms' CSR performance type with 81% accuracy. The accuracy of prediction increases to 96% when the top 50 linguistics features most relevant to firms' CSR performance are included in the prediction model. In addition, we find that the linguistic features of CSR disclosure identified by our study are incrementally value relevant to investors even after controlling for the actual CSR performance score from the professional CSR rating agencies. This finding suggests that the linguistic features of CSR disclosure can be an important venue for capital market participants in evaluating firms' CSR performance type, especially when professional CSR performance ratings are not available.

中文翻译：

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美国企业社会责任报告文本分析

我们采用基于计算机的文本分析来检查 2002 年至 2016 年期间美国企业社会责任 (CSR) 报告样本的披露模式。从计算语言学中常用的 466 个特征开始，我们的结果表明，CSR 报告中的语言学或披露模式可用于准确预测 CSR 记者的实际 CSR 表现类型。具体而言，我们发现单独使用两个最常用的披露特征，即词数和句子数，可以以 81% 的准确率预测报告公司的 CSR 绩效类型。当预测模型中包含与企业 CSR 绩效最相关的前 50 名语言学特征时，预测的准确度提高到 96%。此外，我们发现，即使在控制了专业 CSR 评级机构的实际 CSR 绩效评分后，我们的研究确定的 CSR 披露的语言特征与投资者的价值相关性也是递增的。这一发现表明，企业社会责任披露的语言特征可以成为资本市场参与者评估企业企业社会责任绩效类型的重要场所，尤其是在没有专业的企业社会责任绩效评级时。