PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■Twitter和微博等社交媒体平台促进了正面和负面的沟通，包括网络欺凌。经验证据表明，当公共危机发生时，网络欺凌会增加，这种行为是性别的，社交媒体用户帐户验证可能会阻止它。然而,性别和验证状态与网络欺凌的关联研究不足。这项研究旨在通过研究微博用户的性别，验证状态,帖子中情感和愤怒的表达会影响网络欺凌的态度。具体来说,它调查了在大流行期间，这些因素在支持和反对COVID-19病例的网络欺凌的帖子之间有何不同。
■这项研究利用了社会角色理论，巴利特和外邦人网络欺凌模型，和一般应变理论作为理论框架。我们应用文本分类技术来识别微博上的亲网络欺凌和反网络欺凌帖子。随后，我们使用了标准化的平均差法来比较这些帖子的情感内容。我们的分析集中在情感和愤怒相关表达的普遍性，特别是检查性别和用户验证状态的差异。
■我们的文本分类确定了不同的亲网络欺凌和反网络欺凌帖子。标准化的平均差异分析显示，与反网络欺凌帖子相比，支持网络欺凌帖子包含的情感内容明显更多。Further,在支持网络欺凌的类别中，经过验证的女性用户的帖子显示出与愤怒相关的单词的频率高于其他用户。
■这项研究的发现可以增强研究人员识别网络欺凌态度的算法，通过整合上述理论，使用现实世界的社交媒体数据来完善网络欺凌行为的表征，并帮助政府机构改善其网络欺凌监控，特别是在公共卫生危机的背景下。
■这项研究利用了社会角色理论，巴利特和外邦人网络欺凌模型，和一般应变理论作为理论框架。我们应用文本分类技术来识别微博上的亲网络欺凌和反网络欺凌帖子。随后，我们使用了标准化的平均差法来比较这些帖子的情感内容。我们的分析集中在情感和愤怒相关表达的普遍性，特别是检查性别和用户验证状态的差异。
■我们的文本分类确定了不同的亲网络欺凌和反网络欺凌帖子。标准化的平均差异分析显示，与反网络欺凌帖子相比，支持网络欺凌帖子包含的情感内容明显更多。Further,在支持网络欺凌的类别中，经过验证的女性用户的帖子显示出与愤怒相关的单词的频率高于其他用户。
■这项研究的发现可以增强研究人员识别网络欺凌态度的算法，通过整合上述理论，使用现实世界的社交媒体数据来完善网络欺凌行为的表征，并帮助政府机构改善其网络欺凌监控，特别是在公共卫生危机的背景下。
