EAGER: Automated Content-Based Detection of Public Online Harrassment

公开的网络骚扰有多种形式，但其核心是攻击性、威胁性和恐吓性的帖子。这不是一个孤立的问题。皮尤研究中心（Pew Research Center）发现，73%的人目睹过网络骚扰，40%的人直接经历过骚扰。这项研究开发了一种方法来分析人们在网上发布的东西，并自动检测哪些帖子属于严重的公共在线骚扰类别-发布的信息只是为了扰乱，冒犯或威胁他人。这有助于网站更好地限制发布的信息，减少人们在网上遭受的骚扰。研究人员开发了一个来自许多媒体和社交媒体平台的在线评论语料库，其中每个帖子都被标记为骚扰或非骚扰。然后，他们应用一套计算语言技术来描述信息的特征，包括单词类型和语言结构，这些信息被传递给基于规则和机器学习的人工智能系统进行分类。目标是开发能够自动检测公开在线骚扰信息的模型，并具有较高的准确性。

Public, online harassment takes many forms, but at its core are posts that are offensive, threatening, and intimidating. It is not an isolated problem. The Pew Research Center found 73% of people had witnessed harassment online, and a full 40% of people had experienced harassment directly. This research develops a method for analyzing the things people post online, and automatically detecting which posts fall into the category of severe public online harassment -- messages posted simply to disrupt, offend, or threaten others. This helps websites better limit what messages are posted and reduce the amount of harassment people experience online.The researchers develop a corpus of online comments from a number of media outlets and social media platforms where each post is labeled as harassing or non-harassing. Then, they apply a set of computational linguistic techniques that describe features of the message, including types of words and language structure, which is passed to rule-based and machine learning artificial intelligence systems for classification. The goal is to develop models that can automatically detect the public online harassment messages with high accuracy.