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
• 负责人: Amit Sheth
• 金额: $ 1.32万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-03 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2013801&HistoricalAwards=false
• 关键词: TWC; SBE; Medium; Context; Aware

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时，使用社交媒体来羞辱，欺负和威胁他人的使用急剧上升，这带来了有害后果，例如情绪困扰，抑郁和自杀。 2014年10月，皮尤研究调查表明，有73％的成年互联网用户观察到在线骚扰，而40％的人已经经历过。在线骚扰的流行和严重后果既带来了社会和技术挑战。该项目通过文本分析的结合以及社交媒体中其他线索的使用（例如，发件人与接收者之间的权力关系的指示与潜在的骚扰消息之间的指示）确定了骚扰消息。建议的技术可以适应其他平台，例如Facebook，在线论坛和博客。一个计算机科学家，社会科学家，城市和公共事务专业人士，教育工作者以及大学和高中学生参与研究的跨学科团队将确保科学研究对对安全社交互动的支持的广泛影响。该项目结合了社会科学理论和人类对社交媒体的判断，包括在学校和工作场所的社交媒体中，在学校和工作场所的情况下，将其运作的信息和事业分配。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），以了解有关人员及其相互联系的关系网络的信息，并确定和评估潜在的骚扰和骚扰者。这项工作的关键创新包括：（1）识别侮辱的通用语言，其特征是亵渎和其他言语虐待的一般模式，以及对目标依赖性的进攻性语言的认识，涉及对特定个人或社交圈的敏感主题； （2）通过利用对话历史以及发件人的情绪阅读消息后，在收件人中引起了骚扰特定情绪的预测； （3）基于权力，真理（信任近似）和熟悉程度的信息背后发件人的恶意意图的认识； （4）通过融合上述语言，情感和意图因素来对骚扰信息进行有害评估； （5）从骚扰者的总体行为中检测骚扰者，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。