III: Small: Advancing the Scientific Understanding of Bullying Through the Lens of Social Media

III：小：通过社交媒体的视角促进对欺凌行为的科学理解

• 批准号: 1216758
• 负责人: Xiaojin Zhu
• 金额: $ 49.99万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1216758&HistoricalAwards=false
• 关键词: III; Small; Advancing; Scientific; Understanding

Bullying has been recognized as a serious national health issue. Traditional approaches to the scientific study of bullying are hindered by data acquisition. For example, the standard approach has been to conduct personal surveys in schools. Due to its relatively small sample size and low temporal resolution, neither the true frequency of bullying over the population nor the evolution of bullying roles can be satisfactorily studied. The traditional approaches are also very labor intensive. Social media has developed to the point where it contains enough signal about bullying. This project develops novel machine learning models that automatically monitor and analyze publicly available social media data to understand bullying. These machine learning models reconstruct hidden bullying episodes from a sequence of social media posts. They automatically determine who participated in which bullying episode as what role. In addition, this project conducts human studies on bullying in school and in social media in parallel, by collecting self-report surveys by school-aged children and their social media posts simultaneously. Such studies correlate the traditional psychological approach and social media data on bullying. Taken together, the project will provide significant new scientific data toward understanding, intervention, and helping policy-making regarding bullying.

欺凌已被认为是一个严重的国家健康问题。欺凌科学研究的传统方法受到数据采集的阻碍。例如，标准方法是在学校进行个人调查。由于样本量相对较小且时间分辨率较低，无论是针对人群的欺凌行为的真实频率还是欺凌角色的演变都无法得到令人满意的研究。 传统方法也是劳动密集型的。社交媒体已经发展到包含足够的有关欺凌的信号的程度。 该项目开发了新颖的机器学习模型，可以自动监控和分析公开的社交媒体数据以了解欺凌行为。 这些机器学习模型从一系列社交媒体帖子中重建隐藏的欺凌事件。 他们自动确定谁以什么角色参与了哪个欺凌事件。 此外，该项目通过同时收集学龄儿童的自我报告调查及其社交媒体帖子，同时对学校和社交媒体中的欺凌行为进行人类研究。 此类研究将传统的心理学方法和有关欺凌的社交媒体数据联系起来。总而言之，该项目将为理解、干预和帮助制定有关欺凌的政策提供重要的新科学数据。