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This study investigated how corporate social responsibility (CSR) impacts financial performance (FP) and examined the moderated role of corporate governance (CG). In particular, this paper aims to empirically examine the impact of CG on the relationship between CSR and FP.

This study was based on a sample of 200 firms over 2010/2021. The direct and moderating effects were tested by using multiple regression techniques.

The empirical findings indicated that companies with higher levels of CSR reporting invested more effectively than companies with lower CSR reporting levels. The empirical analysis suggested two main findings: CSR has a significant effect on FP, and this relationship depends on CG practices. This research presents new evidence that improves the discussion around CSR involvement and FP in French firms. Then, this research shows that CG positively moderates the impact of CSR on corporate FP.

These findings may be of interest to academic researchers, practitioners and regulators interested in discovering dividend policies, FP and CSR. The findings may interest different stakeholders, policymakers and regulatory bodies interested in enhancing CG initiatives to strengthen CSR because it suggests implementing a broadly accepted framework of good CG practices to meet the demand for greater transparency and accountability.

This study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.

This study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.

Through the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.

This study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.

This study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.

This study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.