SaTC: CORE: Medium: Collaborative: BaitBuster 2.0: Keeping Users Away From Clickbait

SaTC:核心:媒介:协作:BaitBuster 2.0:让用户远离点击诱饵

基本信息

  • 批准号:
    1949694
  • 负责人:
  • 金额:
    $ 35.63万
  • 依托单位:
  • 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:
    2020-07-01 至 2025-06-30
  • 项目状态:
    未结题

项目摘要

Social media sites such as Facebook are popular platforms for spreading clickbait, links with misleading titles that do not deliver on their promises. Not only does clickbait waste users' time, it often directs users to phishing sites and sites containing spyware and malware. A large number of users fall victim to scams on social media, including those spread through clickbait, due to both a lack of awareness and a lack of appropriate warnings on social media platforms. These users are vulnerable to identity theft, online hacking, and the exposure of sensitive information to adversaries. Thus, it is critical to limit the impact of clickbait on users' security. This project is developing novel techniques to detect various forms of clickbait, especially video-based clickbait, and study user behavior on social media to design effective warning systems. The findings from this research are being incorporated into an open-source browser extension called Baitbuster 2.0, building on the original Baitbuster tool for detecting text-based clickbait. To enhance the impact of this tool, the researchers will design new training methods to raise security awareness and help users avoid clickbait in social media. The project also aims to engage underrepresented groups via outreach efforts and through developing videos to encourage women to consider cybersecurity as a career.Detecting clickbait is a major challenge, particularly as video becomes a more prominent form of media online, undermining efforts to detect misleading text. To address this challenge, the research team will take an integrated approach examining the effects of techniques used to attract clicks from users, presentation and distribution of clickbait, personalization of clickbait through crawling users’ personal information (i.e., targeted clickbait), automatic generation of face-swapping clickbait, and risk perceptions and security awareness of users. As a first step, the researchers are collecting and analyzing clickbait datasets to explore ways of identifying clickbait on social media. Using these datasets, they are developing novel applications of state-of-the-art machine learning techniques such as optical character recognition and video understanding to automatically identify video clickbait. In another thrust of this project, the researchers are studying users' clicking behavior and corresponding security mental models to better understand their vulnerability to clickbait and examine the effects of a wide range of social engineering techniques used to attract clicks from users. The findings are being used to design warning systems, which will be integrated into BaitBuster 2.0, to warn users intelligently and effectively to avoid clickbait. Finally, the usability and efficacy of the warning system and BaitBuster 2.0 are being evaluated through in-depth user studies.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.
Facebook等社交媒体网站是传播clickbait的流行平台,clickbait是带有误导性标题的链接,无法兑现承诺。clickbait不仅浪费用户的时间,而且经常将用户引导到钓鱼网站和包含间谍软件和恶意软件的网站。由于缺乏意识和社交媒体平台上缺乏适当的警告,大量用户成为社交媒体上诈骗的受害者,包括通过点击诱饵传播的诈骗。这些用户很容易受到身份盗窃、在线黑客攻击以及将敏感信息暴露给对手的攻击。因此,限制点击诱饵对用户安全的影响至关重要。该项目正在开发新技术来检测各种形式的点击诱饵,特别是基于视频的点击诱饵,并研究社交媒体上的用户行为,以设计有效的警告系统。这项研究的结果正在被整合到一个名为Baitbuster 2.0的开源浏览器扩展中,该扩展基于最初的Baitbuster工具,用于检测基于文本的点击诱饵。为了增强该工具的影响力,研究人员将设计新的培训方法,以提高安全意识,并帮助用户避免社交媒体中的点击诱饵。该项目还旨在通过外联工作和制作视频,鼓励代表性不足的群体参与进来,以鼓励妇女将网络安全视为一项职业。检测点击诱饵是一项重大挑战,特别是随着视频成为一种更突出的在线媒体形式,破坏了检测误导性文本的努力。为了应对这一挑战,研究小组将采取综合方法,研究用于吸引用户点击的技术的效果,clickbait的呈现和分发,通过抓取用户的个人信息(即,有针对性的点击诱饵)、自动生成面部交换点击诱饵以及用户的风险感知和安全意识。作为第一步,研究人员正在收集和分析点击诱饵数据集,以探索识别社交媒体上点击诱饵的方法。使用这些数据集,他们正在开发最先进的机器学习技术的新应用,如光学字符识别和视频理解,以自动识别视频点击诱饵。在该项目的另一个重点中,研究人员正在研究用户的点击行为和相应的安全心理模型,以更好地了解他们对点击诱饵的脆弱性,并研究用于吸引用户点击的各种社会工程技术的影响。这些发现正在被用于设计警告系统,该系统将被集成到BaitBuster 2.0中,以智能有效地警告用户避免点击诱饵。最后,警告系统和BaitBuster 2.0的可用性和有效性正在通过深入的用户研究进行评估。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知识价值和更广泛的影响审查标准进行评估。

项目成果

期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A Look into User Privacy and Third-party Applications in Facebook
Facebook 中的用户隐私和第三方应用程序研究
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    1.4
  • 作者:
    Seng, Sovantharith;Al-Ameen, Mahdi Nasrullah;Wright, Matthew
  • 通讯作者:
    Wright, Matthew
Explainable Video Entailment with Grounded Visual Evidence
具有扎实视觉证据的可解释视频蕴涵
A first look into users’ perceptions of facial recognition in the physical world
初步了解用户对现实世界中面部识别的看法
  • DOI:
    10.1016/j.cose.2021.102227
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    5.6
  • 作者:
    Seng, Sovantharith;Al-Ameen, Mahdi Nasrullah;Wright, Matthew
  • 通讯作者:
    Wright, Matthew
GateHUB: Gated History Unit with Background Suppression for Online Action Detection
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Matthew Wright其他文献

Towards Machine Learning of Expressive Microtiming in Brazilian Drumming
巴西鼓乐中富有表现力的微计时的机器学习
Unravelling the prognostic effect of <em>IKZF1</em> deletions and <em>IGH@-CRLF2</em> in adult acute lymphoblastic leukaemia
  • DOI:
    10.1097/pat.0b013e3283653bd1
  • 发表时间:
    2013-10-01
  • 期刊:
  • 影响因子:
  • 作者:
    John O’Reilly;Lisa J. Russell;Julian Cooney;Hannah M. Ensor;Duncan Purtill;Matthew Wright;Anthony V. Moorman
  • 通讯作者:
    Anthony V. Moorman
Strike three for the 8p11 – an illustrative case of variable clinical presentations in a patient with myeloid/lymphoid neoplasms with FGFR1 rearrangement
  • DOI:
    10.1016/j.pathol.2023.12.353
  • 发表时间:
    2024-02-01
  • 期刊:
  • 影响因子:
  • 作者:
    Caitlin Rice;Paul Cannell;Rebecca De Kraa;Matthew Wright;Hun Chuah
  • 通讯作者:
    Hun Chuah
229 Epicardial VT Ablation: A Multicentre Safety Study
  • DOI:
    10.1016/s1878-6480(10)70231-7
  • 发表时间:
    2010-01-01
  • 期刊:
  • 影响因子:
  • 作者:
    Frederic Sacher;Philippe Maury;Usha Tedrow;Isabelle Nault;Antoine Deplagne;Pierre Bordachar;Nicolas Derval;Meleze Hocini;Philippe Ritter;Sylvain Ploux;Alexandre Duparc;Matthew Wright;Jacques Clémenty;Michel Haissaguerre;William Stevenson;Pierre Jais
  • 通讯作者:
    Pierre Jais
Robotics, Digital Twins and AI: Connecting the Dot Matrix
机器人、数字孪生和人工智能:连接点阵
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Kris Kydd;Dervla Brennan;Neil Kirkpatrick;Matthew Wright
  • 通讯作者:
    Matthew Wright

Matthew Wright的其他文献

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{{ truncateString('Matthew Wright', 18)}}的其他基金

Developing Nanoscale Passivation Layers for Tandem Solar Cell Interfaces: Towards Terawatt-Scale Solar PV
开发串联太阳能电池接口的纳米级钝化层:迈向太瓦级太阳能光伏
  • 批准号:
    EP/Y027884/1
  • 财政年份:
    2023
  • 资助金额:
    $ 35.63万
  • 项目类别:
    Fellowship
Collaborative Research: SaTC: TTP: Small: DeFake: Deploying a Tool for Robust Deepfake Detection
协作研究:SaTC:TTP:小型:DeFake:部署强大的 Deepfake 检测工具
  • 批准号:
    2040209
  • 财政年份:
    2021
  • 资助金额:
    $ 35.63万
  • 项目类别:
    Standard Grant
RUI: Atomic Physics with Rapidly Frequency Chirped Laser Light
RUI:使用快速频率啁啾激光的原子物理学
  • 批准号:
    1803837
  • 财政年份:
    2018
  • 资助金额:
    $ 35.63万
  • 项目类别:
    Continuing Grant
SaTC: CORE: Small: Adversarial ML in Traffic Analysis
SaTC:核心:小型:流量分析中的对抗性机器学习
  • 批准号:
    1816851
  • 财政年份:
    2018
  • 资助金额:
    $ 35.63万
  • 项目类别:
    Standard Grant
TTP: Small: Collaborative: Defending Against Website Fingerprinting in Tor
TTP:小:协作:防御 Tor 中的网站指纹识别
  • 批准号:
    1619067
  • 财政年份:
    2016
  • 资助金额:
    $ 35.63万
  • 项目类别:
    Standard Grant
TTP: Small: Collaborative: Defending Against Website Fingerprinting in Tor
TTP:小:协作:防御 Tor 中的网站指纹识别
  • 批准号:
    1722743
  • 财政年份:
    2016
  • 资助金额:
    $ 35.63万
  • 项目类别:
    Standard Grant
Computation and Visualization of Multi-Parameter Topological Invariants of Data
数据多参数拓扑不变量的计算和可视化
  • 批准号:
    1606967
  • 财政年份:
    2015
  • 资助金额:
    $ 35.63万
  • 项目类别:
    Standard Grant
Computation and Visualization of Multi-Parameter Topological Invariants of Data
数据多参数拓扑不变量的计算和可视化
  • 批准号:
    1521552
  • 财政年份:
    2015
  • 资助金额:
    $ 35.63万
  • 项目类别:
    Standard Grant
NeTS: Small: Collaborative Research: ReDS: Reputation for Directory Services in P2P Systems
NetS:小型:协作研究:ReDS:P2P 系统中目录服务的声誉
  • 批准号:
    1117866
  • 财政年份:
    2011
  • 资助金额:
    $ 35.63万
  • 项目类别:
    Standard Grant
CAREER: anon.next: Privacy-Enabled Routing in the Next-Generation Internet
职业:anon.next:下一代互联网中的隐私路由
  • 批准号:
    0954133
  • 财政年份:
    2010
  • 资助金额:
    $ 35.63万
  • 项目类别:
    Continuing Grant

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