Cyber violence caused by the disclosure of route information during the COVID-19 pandemic

Characteristics and Treatment of Cyberviolence Trauma in Children and Adolescents

Cyberviolence is a new form of violence encompassing any online activity that results in harm to the psychological, emotional, financial, or physical well-being of an individual or group. These adverse activities often lead to serious offline and long-lasting negative impact, especially on children and adolescents whose development has not matured sufficiently. Therefore, it is more important for mental health professionals to be well informed about the rapidly evolving forms of cyberviolence and its risks and to respond appropriately. This article provides an overview of the concept and unique features of cyberviolence trauma in minors in South Korea while also examining ongoing efforts to explore and implement effective treatment programs. Cyberbullying and digital sexual abuse, the most common forms of cyberviolence experienced by minors in South Korea, are explored in detail. Additionally, this review proposes directions for future research and the efforts that clinicians should focus on.

Social media users' attitudes toward cyberbullying during the COVID-19 pandemic: associations with gender and verification status

Introduction

Social media platforms such as Twitter and Weibo facilitate both positive and negative communication, including cyberbullying. Empirical evidence has revealed that cyberbullying increases when public crises occur, that such behavior is gendered, and that social media user account verification may deter it. However, the association of gender and verification status with cyberbullying is underexplored. This study aims to address this gap by examining how Weibo users' gender, verification status, and expression of affect and anger in posts influence cyberbullying attitudes. Specifically, it investigates how these factors differ between posts pro- and anti-cyberbullying of COVID-19 cases during the pandemic.

Methods

This study utilized social role theory, the Barlett and Gentile Cyberbullying Model, and general strain theory as theoretical frameworks. We applied text classification techniques to identify pro-cyberbullying and anti-cyberbullying posts on Weibo. Subsequently, we used a standardized mean difference method to compare the emotional content of these posts. Our analysis focused on the prevalence of affective and anger-related expressions, particularly examining variations across gender and verification status of the users.

Results

Our text classification identified distinct pro-cyberbullying and anti-cyberbullying posts. The standardized mean difference analysis revealed that pro-cyberbullying posts contained significantly more emotional content compared to anti-cyberbullying posts. Further, within the pro-cyberbullying category, posts by verified female users exhibited a higher frequency of anger-related words than those by other users.

Discussion

The findings from this study can enhance researchers' algorithms for identifying cyberbullying attitudes, refine the characterization of cyberbullying behavior using real-world social media data through the integration of the mentioned theories, and help government bodies improve their cyberbullying monitoring especially in the context of public health crises.

A dataset on corporate sustainability disclosure

Enterprises, as key emitters, play a vital role in promoting sustainable development. Corporate sustainability disclosure provides a key channel for stakeholders to gain insights into a company's sustainability progress. However, few studies have been conducted to measure sustainability disclosure at the firm level. In this study, we apply the machine learning techniques to listed companies' management discussion and analysis (MD&A) documents and construct a dataset on corporate sustainability disclosure, including the Corporate Sustainability Disclosure Index (CSDI), CSDI_Economic Dimension (CSDI_ECO), CSDI_Environmental Dimension (CSDI_ENV), and CSDI_Social Dimension (CSDI_SOCI). The dataset will be updated annually. To the best of our knowledge, this is the first sustainability disclosure dataset constructed at the firm level. Our dataset reflects corporate managements' sustainability attitudes and promotes the implementation of corporate sustainability strategies and subsequent sustainable economic and social outcomes.