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

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、欺凌和威胁他人的行为急剧增加，这带来了情绪困扰、抑郁和自杀等有害后果。皮尤研究中心（Pew Research） 2014年10月的调查显示，73%的成年互联网用户看到过网络骚扰，40%的人经历过。网络骚扰的流行和严重后果给社会和技术带来了挑战。本项目通过结合文本分析和使用社交媒体中的其他线索（例如，潜在骚扰信息的发送者和接收者之间的权力关系的迹象）来识别社交媒体中的骚扰信息。该项目将开发原型来检测Twitter上的骚扰信息；建议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、教育工作者以及大学和高中学生参与的跨学科团队将确保科学研究对安全社会互动的支持产生广泛影响。该项目将社会科学理论与人类对学校和工作场所社交媒体中潜在骚扰案例的判断相结合，以实现对骚扰信息和罪犯的检测。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、自然语言处理和社会网络分析），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。这项工作的关键创新包括：(1)识别以亵渎和其他一般语言虐待模式为特征的通用侮辱语言，识别涉及特定个人或社交圈个人敏感话题的目标依赖的攻击性语言；(2)利用对话记录和发送者情绪预测接收人阅读短信后产生的骚扰特定情绪；(3)基于权力、真相（近似于信任）和熟悉度来识别信息背后发送者的恶意意图；(4)融合上述语言、情感和意图因素对骚扰信息进行危害性评估；(5)从骚扰者的聚合行为中发现骚扰者，如骚扰频率、持续时间、覆盖措施等，进行有效的预防和干预。

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.