RAPID: Cyber-Hostility and COVID-19

RAPID：网络敌意和 COVID-19

• 批准号: 2031002
• 负责人: Matthew Costello
• 金额: $ 20万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-15 至 2022-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031002&HistoricalAwards=false
• 关键词: RAPID; Cyber; Hostility; COVID; 19

In this project, the new wave of cyber-hostility occasioned by COVID-19 is investigated through exploration and analysis of data from prominent social media sites over a twelve-month period. COVID-19-related cyber-hostility targeting people based on race/ethnicity, age, social class, immigrant status, and political ideology has emerged on social media. Through this project, insights are provided into COVID-19-related cyber-hostility and, more broadly, light is shed on how people respond online to unexpected societal calamities. Findings from this project offer cues on how to communicate with the public during destabilizing times and how to ease tension during crises and stop further escalation of outbursts of cyber-hostility.Data for this project are collected from prominent social media sites, including Twitter, Facebook, Instagram, Snapchat, and Reddit, over a 12-month period. These data allow exploration of how quickly and broadly cyber-hostility related to COVID-19 spreads, both within and between social networks. Machine learning methods are developed to provide a timely and necessary understanding of the sources of COVID-19-related cyber-hostility, how and where it circulates online, and individual and situational factors associated with COVID-19-related cyber-hostility. This analysis allows insight into whether COVID-19-related cyber-hostility develops into a persistent fixture in cyberspace, or becomes less prevalent as the severity of the pandemic diminishes. This project is jointly funded by Sociology, the Established Program to Stimulate Competitive Research (EPSCoR), and Secure and Trustworthy Cyberspace (SaTC).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.

在这个项目中，通过探索和分析来自知名社交媒体网站的12个月期间的数据，调查了COVID-19引发的新一波网络敌意。 社交媒体上出现了基于种族/民族、年龄、社会阶层、移民身份和政治意识形态的针对人群的与COVID-19相关的网络敌意。 通过这个项目，我们深入了解了与COVID-19相关的网络敌意，更广泛地说，我们了解了人们如何在线应对意想不到的社会灾难。 该项目的研究结果提供了在不稳定时期如何与公众沟通以及如何在危机期间缓解紧张局势并阻止网络敌对情绪进一步升级的线索。该项目的数据收集自知名社交媒体网站，包括Twitter，Facebook，Instagram，Snapchat和Reddit，为期12个月。这些数据有助于探索与COVID-19相关的网络敌意在社交网络内部和之间传播的速度和范围。 机器学习方法的开发旨在及时和必要地了解COVID-19相关网络敌意的来源、其在线传播的方式和地点，以及与COVID-19相关网络敌意相关的个人和情境因素。该分析可让我们深入了解与COVID-19相关的网络敌意是否会在网络空间中持续存在，或随着疫情的严重程度降低而变得不那么普遍。 该项目由社会学、刺激竞争研究的既定计划（EPSCoR）和安全可信的网络空间（SaTC）共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Towards Understanding and Detecting Cyberbullying in Real-world Images

理解和检测现实世界图像中的网络欺凌

• DOI: 10.14722/ndss.2021.24260
• 发表时间: 2021
• 期刊: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA
• 作者: Vishwamitra, Nishant;Hu, Hongxin;Luo, Feng;Cheng, Long
• 通讯作者: Cheng, Long

BYOZ: Protecting BYOD Through Zero Trust Network Security

BYOZ：通过零信任网络安全保护 BYOD

• 发表时间: 2022
• 期刊: and Storage
• 作者: Anderson, John;Huang, Qiqing;Cheng, Long;Hu, Hongxin
• 通讯作者: Hu, Hongxin

Understanding the Generalizability of Hateful Memes Detection Models Against COVID-19-related Hateful Memes

了解针对 COVID-19 相关仇恨模因的仇恨模因检测模型的普遍性

• 发表时间: 2022
• 期刊: International Conference on Machine Learning and Applications
• 作者: Cuo, Keyan;Zhao, Wentai;Mu Jaden;Vishwamitra, Vishant;Zhao, Ziming;Hu, Hongxin
• 通讯作者: Hu, Hongxin

Measuring the Effectiveness of Privacy Policies for Voice Assistant Applications

• DOI: 10.1145/3427228.3427250
• 发表时间: 2020-07
• 期刊: Proceedings of the 36th Annual Computer Security Applications Conference
• 作者: Song Liao;Christin Wilson;Long Cheng;Hongxin Hu;Huixing Deng

Towards Automated Content-based Photo Privacy Control in User-Centered Social Networks

在以用户为中心的社交网络中实现基于内容的自动照片隐私控制

• DOI: 10.1145/3508398.3511517
• 发表时间: 2022
• 期刊: Proceedings of the 12th ACM Conference on Data and Application Security and Privacy (CODASPY
• 作者: Vishwamitra, Nishant;Li, Yifang;Hu, Hongxin;Caine, Kelly;Cheng, Long;Zhao, Ziming;Ahn, Gail-Joon
• 通讯作者: Ahn, Gail-Joon