EAGER: BullyBlocker - Identifying Cyberbullying in Social Networking Sites
EAGER:BullyBlocker - 识别社交网站中的网络欺凌行为
基本信息
- 批准号:1719722
- 负责人:
- 金额:$ 30万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2017
- 资助国家:美国
- 起止时间:2017-09-01 至 2020-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Cyberbullying is the most common online risk for adolescents, yet the majority of young people who are bullied online do not tell their parents when it occurs. The goal of the BullyBlocker project is to advance the understanding of how cyberbullying, in particular, and behavioral issues, more broadly, can be effectively identified on social networking sites. This interdisciplinary project integrates advances in computer sciences and key findings from psychological research on cyberbullying to (i) design and implement models for identifying cyberbullying on social networking platforms, and (ii) study usage patterns of automated tools based on these models and their utility for devising and testing new hypotheses about cyberbullying risk factors. The intellectual merit of this project stems from its synergistic integration of computer and psychological science to address a social problem that negatively impacts adolescents, their families, and society. The project draws on psychological research on cyberbullying risk factors and advances in computer science to enable the development of automated identification models and the integration of these models into an app (BullyBlocker) that notifies parents about their adolescents' risk of cyberbullying. The project also entails the design of mechanisms to generate customized anti-bullying resources for parents and victims, investigation of app usage patterns and parents' attitudes towards automated identification tools, the use of automated tools such as BullyBlocker to devise and test new hypotheses about cyberbullying, and a unique training opportunity that provides graduate and undergraduate students with the scientific scaffolding to develop into recognized interdisciplinary scholars.
网络欺凌是青少年最常见的在线风险,但大多数在网上受到欺凌的年轻人在发生时不会告诉他们的父母。BullyBlocker项目的目标是促进对如何在社交网站上有效识别网络欺凌(特别是行为问题)的理解。这个跨学科的项目整合了计算机科学的进步和网络欺凌心理学研究的主要发现,以(i)设计和实施社交网络平台上识别网络欺凌的模型,以及(ii)研究基于这些模型的自动化工具的使用模式及其实用性,以设计和测试有关网络欺凌风险因素的新假设。该项目的智力价值源于其计算机和心理科学的协同整合,以解决对青少年,他们的家庭和社会产生负面影响的社会问题。该项目借鉴了关于网络欺凌风险因素的心理学研究和计算机科学的进步,以开发自动识别模型,并将这些模型整合到一个应用程序(BullyBlocker)中,通知父母青少年的网络欺凌风险。该项目还需要设计机制,为家长和受害者生成定制的反欺凌资源,调查应用程序使用模式和家长对自动识别工具的态度,使用BullyBlocker等自动化工具来设计和测试有关网络欺凌的新假设,和一个独特的培训机会,为研究生和本科生提供科学的脚手架,以发展成为公认的跨学科学者
项目成果
期刊论文数量(12)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
BullyBlocker: toward an interdisciplinary approach to identify cyberbullying
BullyBlocker:采用跨学科方法来识别网络欺凌
- DOI:10.1007/s13278-018-0496-z
- 发表时间:2018
- 期刊:
- 影响因子:2.8
- 作者:Silva, Yasin N.;Hall, Deborah L.;Rich, Christopher
- 通讯作者:Rich, Christopher
Personalized Learning for Cyberbullying Detection
网络欺凌检测的个性化学习
- DOI:
- 发表时间:2018
- 期刊:
- 影响因子:0
- 作者:Cheng, Lu;Silva, Yasin N.;Hall, Deborah L.;Liu, Huan
- 通讯作者:Liu, Huan
PI-Bully: Personalized Cyberbullying Detection with Peer Influence
- DOI:10.24963/ijcai.2019/808
- 发表时间:2019-08
- 期刊:
- 影响因子:0
- 作者:Lu Cheng;Jundong Li;Yasin N. Silva;Deborah L. Hall;Huan Liu
- 通讯作者:Lu Cheng;Jundong Li;Yasin N. Silva;Deborah L. Hall;Huan Liu
Modeling Temporal Patterns of Cyberbullying Detection with Hierarchical Attention Networks
- DOI:10.1145/3441141
- 发表时间:2021-04
- 期刊:
- 影响因子:0
- 作者:Lu Cheng;Ruocheng Guo;Yasin N. Silva;Deborah L. Hall;Huan Liu
- 通讯作者:Lu Cheng;Ruocheng Guo;Yasin N. Silva;Deborah L. Hall;Huan Liu
Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach
减少基于会话的网络欺凌检测中的偏差:一种不妥协的方法
- DOI:10.18653/v1/2021.acl-long.168
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Cheng, L.;Mosallanezhad, A.;Silva, Y. N.;Hall, D. L.;Liu, H.
- 通讯作者:Liu, H.
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{{ truncateString('Yasin Silva', 18)}}的其他基金
SaTC: CORE: Small: Interdisciplinary Models to Identify and Understand Cyberbullying
SaTC:核心:小型:识别和理解网络欺凌的跨学科模型
- 批准号:
2227488 - 财政年份:2022
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
SaTC: CORE: Small: Interdisciplinary Models to Identify and Understand Cyberbullying
SaTC:核心:小型:识别和理解网络欺凌的跨学科模型
- 批准号:
2036127 - 财政年份:2021
- 资助金额:
$ 30万 - 项目类别:
Standard Grant