TWC SBE: Medium: Context-Aware Harassment Detection on Social Media

TWC SBE:媒介:社交媒体上的情境感知骚扰检测

基本信息

  • 批准号:
    1513721
  • 负责人:
  • 金额:
    $ 92.51万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2015
  • 资助国家:
    美国
  • 起止时间:
    2015-09-01 至 2020-06-30
  • 项目状态:
    已结题

项目摘要

As social media permeates our daily life, there has been a sharp rise in the use of social media to humiliate, bully, and threaten others, which has come with harmful consequences such as emotional distress, depression, and suicide. The October 2014 Pew Research survey shows that 73% of adult Internet users have observed online harassment and 40% have experienced it. The prevalence and serious consequences of online harassment present both social and technological challenges. This project identifies harassing messages in social media, through a combination of text analysis and the use of other clues in the social media (e.g., indications of power relationships between sender and receiver of a potentially harassing message.) The project will develop prototypes to detect harassing messages in Twitter; the proposed techniques can be adapted to other platforms, such as Facebook, online forums, and blogs. An interdisciplinary team of computer scientists, social scientists, urban and public affairs professionals, educators, and the participation of college and high schools students in the research will ensure wide impact of scientific research on the support for safe social interactions.This project combines social science theory and human judgment of potential harassment examples from social media, in both school and workplace contexts, to operationalize the detection of harassing messages and offenders. It develops comprehensive and reliable context-aware techniques (using machine learning, text mining, natural language processing, and social network analysis) to glean information about the people involved and their interconnected network of relationships, and to determine and evaluate potential harassment and harassers. The key innovations of this work include: (1) identification of the generic language of insult, characterized by profanities and other general patterns of verbal abuse, and recognition of target-dependent offensive language involving sensitive topics that are personal to a specific individual or social circle; (2) prediction of harassment-specific emotion evoked in a recipient after reading messages by leveraging conversation history as well as sender's emotions; (3) recognition of a sender's malicious intent behind messages based on the aspects of power, truth (approximated by trust), and familiarity; (4) a harmfulness assessment of harassing messages by fusing aforementioned language, emotion, and intent factors; and (5) detection of harassers from their aggregated behaviors, such as harassment frequency, duration, and coverage measures, for effective prevention and intervention.
随着社交媒体渗透到我们的日常生活中,使用社交媒体羞辱、欺凌和威胁他人的情况急剧增加,这带来了情绪困扰、抑郁和自杀等有害后果。皮尤研究中心2014年10月的调查显示,73%的成年互联网用户观察到网络骚扰,40%的人经历过。网络骚扰的普遍性和严重后果带来了社会和技术挑战。该项目通过文本分析和使用社交媒体中的其他线索(例如,潜在骚扰消息的发送者和接收者之间的权力关系的指示。该项目将开发原型来检测Twitter中的骚扰消息;所提出的技术可以适用于其他平台,如Facebook、在线论坛和博客。一个由计算机科学家、社会科学家、城市和公共事务专业人士、教育工作者组成的跨学科团队,以及大学和高中学生的参与,将确保科学研究对支持安全的社会互动产生广泛影响。该项目结合了社会科学理论和人类对社交媒体中潜在骚扰案例的判断,在学校和工作场所环境中,使侦测骚扰信息和骚扰者的工作切实可行。它开发了全面可靠的上下文感知技术(使用机器学习,文本挖掘,自然语言处理和社交网络分析),以收集有关相关人员及其相互关联的关系网络的信息,并确定和评估潜在的骚扰和骚扰者。这项工作的主要创新包括:(1)识别侮辱性语言的通用语言,其特征是亵渎和其他语言虐待的一般模式,并识别涉及特定个人或社交圈的敏感话题的目标依赖性攻击性语言;(2)通过利用会话历史以及发送者的情绪来预测接收者在阅读消息之后引起的骚扰特定情绪;(3)基于权力、真相(近似于信任)和熟悉度等方面识别发送者背后的恶意意图;(4)通过融合上述语言、情感和意图因素对骚扰消息进行危害性评估;(5)从骚扰者的聚集行为中检测骚扰者,例如骚扰频率、持续时间和覆盖措施,以进行有效的预防和干预。

项目成果

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Amit Sheth其他文献

Grounding From an AI and Cognitive Science Lens
从人工智能和认知科学的角度出发
  • DOI:
    10.1109/mis.2024.3366669
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    6.4
  • 作者:
    Goonmeet Bajaj;V. Shalin;Srinivasan Parthasarathy;Amit Sheth;Amit Sheth
  • 通讯作者:
    Amit Sheth
Causal Event Graph-Guided Language-based Spatiotemporal Question Answering
因果事件图引导的基于语言的时空问答
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Kaushik Roy;Alessandro Oltramari;Yuxin Zi;Chathurangi Shyalika;Vignesh Narayanan;Amit Sheth
  • 通讯作者:
    Amit Sheth
Cognitive manufacturing: definition and current trends
  • DOI:
    10.1007/s10845-024-02429-9
  • 发表时间:
    2024-06-20
  • 期刊:
  • 影响因子:
    7.400
  • 作者:
    Fadi El Kalach;Ibrahim Yousif;Thorsten Wuest;Amit Sheth;Ramy Harik
  • 通讯作者:
    Ramy Harik
Ki-Cook: Clustering Multimodal Cooking Representations Through Ki-Cook: Clustering Multimodal Cooking Representations Through Knowledge-infused Learning Knowledge-infused Learning
Ki-Cook:通过知识注入学习对多模态烹饪表示进行聚类 Ki-Cook:通过知识注入学习对多模态烹饪表示进行聚类 知识注入学习
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Thommen Karimpanal George;R. Venkataramanan;Swati Padhee;Saini Rohan;Rao Ronak;Anirudh Kaoshik 4;Sundara Rajan;Amit Sheth
  • 通讯作者:
    Amit Sheth
RDR: the Recap, Deliberate, and Respond Method for Enhanced Language Understanding
RDR:增强语言理解的回顾、深思熟虑和回应方法
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Yuxin Zi;Hariram Veeramani;Kaushik Roy;Amit Sheth
  • 通讯作者:
    Amit Sheth

Amit Sheth的其他文献

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{{ truncateString('Amit Sheth', 18)}}的其他基金

EAGER: Knowledge-guided neurosymbolic AI with guardrails for safe virtual health assistants
EAGER:知识引导的神经符号人工智能,带有安全虚拟健康助手的护栏
  • 批准号:
    2335967
  • 财政年份:
    2023
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Standard Grant
EAGER: Advancing Neuro-symbolic AI with Deep Knowledge-infused Learning
EAGER:通过深度知识注入学习推进神经符号人工智能
  • 批准号:
    2133842
  • 财政年份:
    2021
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Standard Grant
NSF Convergence Accelerator: Symposium on Big Data and AI-Driven Disaster Management for Planning, Response, Recovery, and Resiliency
NSF 融合加速器:大数据和人工智能驱动的灾害管理规划、响应、恢复和复原力研讨会
  • 批准号:
    1956285
  • 财政年份:
    2020
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Standard Grant
TWC SBE: Medium: Context-Aware Harassment Detection on Social Media
TWC SBE:媒介:社交媒体上的情境感知骚扰检测
  • 批准号:
    2013801
  • 财政年份:
    2019
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Standard Grant
Spokes: MEDIUM: MIDWEST: Collaborative: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest
辐条:媒介:中西部:协作:社区驱动的数据工程,用于中西部农村地区的药物滥用预防
  • 批准号:
    1956009
  • 财政年份:
    2019
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Standard Grant
Spokes: MEDIUM: MIDWEST: Collaborative: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest
辐条:媒介:中西部:协作:社区驱动的数据工程,用于中西部农村地区的药物滥用预防
  • 批准号:
    1761931
  • 财政年份:
    2018
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Standard Grant
III: Travel Fellowships for Students from U.S. Universities to Attend ISWC 2016
三:美国大学学生参加 ISWC 2016 的旅费奖学金
  • 批准号:
    1622628
  • 财政年份:
    2016
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Standard Grant
PFI:AIR - TT: Market Driven Innovations and Scaling up of Twitris- A System for Collective Social Intelligence
PFI:AIR - TT:市场驱动的创新和 Twitris 的扩展——集体社交智能系统
  • 批准号:
    1542911
  • 财政年份:
    2015
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Standard Grant
I-Corps: Towards Commercialization of Twitris- a system for collective intelligence
I-Corps:迈向 Twitris 的商业化——集体智慧系统
  • 批准号:
    1343041
  • 财政年份:
    2013
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Standard Grant
SoCS: Collaborative Research: Social Media Enhanced Organizational Sensemaking in Emergency Response
SoCS:协作研究:社交媒体增强应急响应中的组织意识
  • 批准号:
    1111182
  • 财政年份:
    2011
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Standard Grant

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转基因水稻中不同反义Sbe基因结构对抑制胚乳支链淀粉合成效果的比较
  • 批准号:
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  • 批准年份:
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相似海外基金

TWC SBE: Medium: Context-Aware Harassment Detection on Social Media
TWC SBE:媒介:社交媒体上的情境感知骚扰检测
  • 批准号:
    2013801
  • 财政年份:
    2019
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Standard Grant
TWC SBE: Medium: Collaborative: Brain Hacking: Assessing Psychological and Computational Vulnerabilities in Brain-based Biometrics
TWC SBE:媒介:协作:大脑黑客:评估基于大脑的生物识别技术中的心理和计算漏洞
  • 批准号:
    1840790
  • 财政年份:
    2018
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Continuing Grant
TWC SBE: Medium: Collaborative: Building a Privacy-Preserving Social Networking Platform from a Technological and Sociological Perspective
TWC SBE:媒介:协作:从技术和社会学角度构建保护隐私的社交网络平台
  • 批准号:
    1855391
  • 财政年份:
    2018
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Standard Grant
TWC SBE: Medium: Collaborative: Dollars for Hertz: Making Trustworthy Spectrum Sharing Technically and Economically Viable
TWC SBE:媒介:协作:赫兹美元:使值得信赖的频谱共享在技术上和经济上可行
  • 批准号:
    1801986
  • 财政年份:
    2017
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Standard Grant
TWC SBE: Medium: Collaborative: Brain Hacking: Assessing Psychological and Computational Vulnerabilities in Brain-based Biometrics
TWC SBE:媒介:协作:大脑黑客:评估基于大脑的生物识别技术中的心理和计算漏洞
  • 批准号:
    1564104
  • 财政年份:
    2016
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Continuing Grant
TWC SBE: TTP Option: Medium: Collaborative: EPICA: Empowering People to Overcome Information Controls and Attacks
TWC SBE:TTP 选项:中:协作:EPICA:赋予人们克服信息控制和攻击的能力
  • 批准号:
    1664786
  • 财政年份:
    2016
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Standard Grant
TWC SBE: Medium: Collaborative: Building a Privacy-Preserving Social Networking Platform from a Technological and Sociological Perspective
TWC SBE:媒介:协作:从技术和社会学角度构建保护隐私的社交网络平台
  • 批准号:
    1564101
  • 财政年份:
    2016
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Standard Grant
TWC SBE: Medium: Collaborative: Building a Privacy-Preserving Social Networking Platform from a Technological and Sociological Perspective
TWC SBE:媒介:协作:从技术和社会学角度构建保护隐私的社交网络平台
  • 批准号:
    1564034
  • 财政年份:
    2016
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Standard Grant
TWC SBE: Medium: Collaborative: Brain Hacking: Assessing Psychological and Computational Vulnerabilities in Brain-based Biometrics
TWC SBE:媒介:协作:大脑黑客:评估基于大脑的生物识别技术中的心理和计算漏洞
  • 批准号:
    1564046
  • 财政年份:
    2016
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Continuing Grant
TWC SBE: TTP Option: Medium: Collaborative: EPICA: Empowering People to Overcome Information Controls and Attacks
TWC SBE:TTP 选项:中:协作:EPICA:赋予人们克服信息控制和攻击的能力
  • 批准号:
    1409758
  • 财政年份:
    2014
  • 资助金额:
    $ 92.51万
  • 项目类别:
    Standard Grant
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