SGER: Multilingual Online Stylometric Authorship Identification: An Exploratory Study

SGER：多语言在线风格作者身份识别：一项探索性研究

• 批准号: 0646942
• 负责人: Hsinchun Chen
• 金额: --
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2008-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0646942&HistoricalAwards=false
• 关键词: SGER; Multilingual; Online; Stylometric; Authorship

Online communication mediums such as email, web sites, newsgroups, online forums, and chat rooms have been ubiquitously integrated into our everyday lives. Unfortunately, online channels are also being misused for distribution of unsolicited and inappropriate information (e.g., extremist propaganda, spam, online gambling, etc.). The anonymous nature of these channels makes them an ideal source of communication for criminal groups and extremist organizations. Additionally, the evolution of the internet as a major international communication medium has spawned the advent of a multilingual dimension. Authorship analysis has been used to analyze long, precise English texts such as plays of Shakespeare (authorship identification) or student's class papers (plagiarism detection). Few past studies have addressed the multilingual issues of short online communications. The language-specific stylistic characteristics and the informal nature of online communications present unique research challenges. This exploratory project aims to develop a comprehensive framework and associated text mining techniques for multilingual online stylometric feature extraction and authorship classification, initially focusing on two languages, English and Arabic. The linguistic differences between these two languages will allow evaluation of common stylistic representations and explore other language-specific problems. The goal is to develop scalable online authorship analysis techniques that can be used to analyze 100s to 1000s of anonymous authors (a common scenario for web communications). Novel feature (subset) selection techniques will help reduce the high dimensionality of online writing features. The primary intellectual contribution of this research is expected to yield: (a) development and evaluation of new text mining techniques that may be suitable for identity tracing in cyberspace; (b) creation of new representations of people's identities using online "Writeprints" (i.e., the representation of people's key online writing style features); and (c) evaluation of the effectiveness of different multilingual stylistic features and classification techniques for improving identification scalability and robustness. The anticipated broader impact of this research include: building foundation for further cyber trust research; improving intelligence and law enforcement agencies' abilities to detect, prevent, and respond to cyber crimes and terrorist events via the Internet; and providing a large-scale research corpus and feature extraction resources for information scientists, political and social scientists, and terrorism researchers. The project web site (http://ai.arizona.edu/authorship) will be used for broad dissemination of project results.

在线通信媒介，如电子邮件，网站，新闻组，在线论坛和聊天室已经无处不在地融入我们的日常生活。不幸的是，在线渠道也被滥用于分发未经请求的和不适当的信息（例如，极端主义宣传、垃圾邮件、在线赌博等）。这些渠道的匿名性使其成为犯罪集团和极端主义组织理想的通信来源。此外，互联网作为一种主要的国际通信媒介的发展也催生了多语言层面的出现。作者身份分析已被用于分析长而精确的英语文本，例如莎士比亚的戏剧（作者身份识别）或学生的课堂论文（剽窃检测）。过去很少有研究涉及短的在线通信的多语言问题。语言特有的文体特征和在线交流的非正式性质提出了独特的研究挑战。这一探索性项目旨在为多语种在线文体特征提取和作者身份分类开发一个综合框架和相关的文本挖掘技术，最初侧重于英语和阿拉伯语两种语言。这两种语言之间的语言差异将允许共同的风格表示的评价，并探讨其他语言特有的问题。目标是开发可扩展的在线作者身份分析技术，可用于分析100到1000名匿名作者（Web通信的常见场景）。 新颖的特征（子集）选择技术将有助于降低在线写作特征的高维性。这项研究的主要智力贡献预计将产生：（a）开发和评估可能适用于网络空间身份追踪的新文本挖掘技术;（B）使用在线“笔迹”（即，（c）评估不同的多语言风格特征和分类技术对提高识别的可扩展性和鲁棒性的有效性。这项研究的预期更广泛的影响包括：为进一步的网络信任研究奠定基础;提高情报和执法机构通过互联网发现、预防和应对网络犯罪和恐怖主义事件的能力;为信息科学家、政治和社会科学家以及恐怖主义研究人员提供大规模的研究语料库和特征提取资源。项目网站（http：//ai.arizona.edu/authorship）将用于广泛传播项目成果。