权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
2227488
负责人：
Yasin Silva
金额：
$ 50万
依托单位：
Loyola University of Chicago
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227488&HistoricalAwards=false
关键词：
SaTC CORE Small Interdisciplinary Models

项目摘要

Cyberbullying, a common form of online misbehavior, can do serious psychological and physical harm. This project integrates computer science and psychology frameworks to advance our understanding of how cyberbullying can be identified and prevented. The core objective of this interdisciplinary project is to design and develop models and apps to prevent and identify instances of cyberbullying in social networks. It uses psychological theory to guide investigations of the nature of cyberbullying and the adoption of automated anti-bullying tools, as well as carefully-designed data collection processes and evaluation frameworks. The project's impact includes sharing research and educational resources, raising cyberbullying awareness with policy-makers, and providing graduate and undergraduate students with the scientific scaffolding to develop into recognized interdisciplinary scholars. The intellectual merit of this project stems from its synergistic integration of computer and psychological sciences to address a major social problem. This project studies automated models for cyberbullying detection that leverage a large body of relevant empirical work in psychology and seeks to develop evidence-based tools for identifying and preventing cyberbullying. It integrates innovative machine learning models for cyberbullying identification (including personalized, temporal-analysis-based, multi-modal, and privacy-preserving models) with the development of evidence-based apps for identifying and addressing cyberbullying (e.g., apps that strengthen parent-teen relationships within the context of cyberbullying and social media). The project also uses systematically-annotated datasets and survey methods to identify mechanisms to enhance the use and usability of anti-cyberbullying technologies and investigate key aspects of the nature of cyberbullying, including temporal characteristics of cyberbullying and connections between social media use and psychological correlates of cyberbullying.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

网络欺凌是一种常见的在线不当行为，可以造成严重的心理和身体伤害。该项目整合了计算机科学和心理学框架，以促进我们对如何识别和预防网络欺凌的理解。这个跨学科项目的核心目标是设计和开发模型和应用程序，以防止和识别社交网络中的网络欺凌事件。它使用心理学理论来指导对网络欺凌性质的调查，并采用自动化的反欺凌工具，以及精心设计的数据收集过程和评估框架。该项目的影响包括共享研究和教育资源，提高政策制定者对网络欺凌的认识，并为研究生和本科生提供科学支架，使其发展成为公认的跨学科学者。这个项目的智力价值源于它的计算机和心理科学的协同整合，以解决一个重大的社会问题。该项目研究了网络欺凌检测的自动化模型，这些模型利用了大量心理学方面的相关实证工作，并寻求开发基于证据的工具来识别和预防网络欺凌。它将用于网络欺凌识别的创新机器学习模型（包括个性化，基于时间分析，多模式和隐私保护模型）与用于识别和解决网络欺凌的循证应用程序的开发（例如，在网络欺凌和社交媒体的背景下加强父母与青少年关系的应用程序）。该项目还使用系统注释的数据集和调查方法来确定机制，以加强反网络欺凌技术的使用和可用性，并调查网络欺凌性质的关键方面，包括网络欺凌的时间特征以及社交媒体使用与网络欺凌的心理相关性之间的联系。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响审查标准。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Fairness through Aleatoric Uncertainty

DOI：
10.1145/3583780.3614875
发表时间：
2023-04
期刊：
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
影响因子：
0
作者：
Anique Tahir;Lu Cheng;Huan Liu
通讯作者：
Anique Tahir;Lu Cheng;Huan Liu

A Labeled Dataset for Investigating Cyberbullying Content Patterns in Instagram