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）基于力量、真实性（近似于信任）和熟悉度等方面来识别发送者在消息背后的恶意意图;（4）通过融合上述语言、情感和意图因素来对骚扰消息进行危害性评估;以及（5）从骚扰者的聚合行为（例如骚扰频率、持续时间和覆盖措施）中检测骚扰者，以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.