Does ownership type matter for corporate social responsibility disclosure: Evidence from China

The evidence of the effect of ownership structure on corporate social responsibility (CSR) is relatively sparse especially in the emerging economies. This paper seeks to address this situation to comprehensively examine the link between different types of shareholders and CSR disclosure in the context of China. Our findings reveal that different owners have differential impact on the CSR. The firms controlled by the state are more likely to disclose CSR information and their CSR reports’ quality is better compared with non-SOEs. Interestingly, firms with more shares held by mutual funds, foreign investors or other corporations are significantly better at CSR disclosure. The study also discloses that firm size, profitability, and leverage affect CSR in China. Overall the study contributes to the literature on CSR practices in emerging countries and point to some policy suggestions

Ownership influence and CSR disclosure in China

© 2018, Emerald Publishing Limited. Purpose: This paper aims to examine the relationship between ownership type and the likelihood of publication of a corporate social responsibility (CSR) report. Design/methodology/approach: Drawing on stakeholder salience theory, the probit model is used for a sample of 1,839 Chinese listed firms to study how different types of owners influence firm CSR engagement. Findings: The analysis reveals that the Chinese stock exchanges exert a positive influence on the likelihood of a firm producing a CSR report, an effect which is more significant in state-owned enterprises (SOEs). Foreign investors lead to a greater likelihood of publication of a CSR report, though this effect is weaker in SOEs. In contrast, the holdings of state and domestic institutional investors are broadly neutral. Practical implications: The study helps corporate managers to recognise how particular types of shareholders will value their efforts regarding CSR activities and disclosure and also assists policymakers in improving the level of CSR disclosure through the development of new policy. Social implications: Apposite CSR disclosure enhances trust and facilitates the shared values on which to build a more cohesive society. Originality/value: The novelty of this study is that it addresses the effect of institutional investors on Chinese firm CSR engagement and thus provides an important insight for firms, investors and other stakeholders into the interplay of portfolio investment and CSR

Mining the Relationship between Emoji Usage Patterns and Personality

Emojis have been widely used in textual communications as a new way to convey nonverbal cues. An interesting observation is the various emoji usage patterns among different users. In this paper, we investigate the correlation between user personality traits and their emoji usage patterns, particularly on overall amounts and specific preferences. To achieve this goal, we build a large Twitter dataset which includes 352,245 users and over 1.13 billion tweets associated with calculated personality traits and emoji usage patterns. Our correlation and emoji prediction results provide insights into the power of diverse personalities that lead to varies emoji usage patterns as well as its potential in emoji recommendation tasks.Comment: To appear at The International AAAI Conference on Web and Social Media (ICWSM) 201

Discrete Point-wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face Recognition

Classical adversarial attacks for Face Recognition (FR) models typically generate discrete examples for target identity with a single state image. However, such paradigm of point-wise attack exhibits poor generalization against numerous unknown states of identity and can be easily defended. In this paper, by rethinking the inherent relationship between the face of target identity and its variants, we introduce a new pipeline of Generalized Manifold Adversarial Attack (GMAA) to achieve a better attack performance by expanding the attack range. Specifically, this expansion lies on two aspects - GMAA not only expands the target to be attacked from one to many to encourage a good generalization ability for the generated adversarial examples, but it also expands the latter from discrete points to manifold by leveraging the domain knowledge that face expression change can be continuous, which enhances the attack effect as a data augmentation mechanism did. Moreover, we further design a dual supervision with local and global constraints as a minor contribution to improve the visual quality of the generated adversarial examples. We demonstrate the effectiveness of our method based on extensive experiments, and reveal that GMAA promises a semantic continuous adversarial space with a higher generalization ability and visual qualityComment: Accepted by CVPR202