CybercrimeNLP (CC-NLP): A natural language processing toolkit for the interdisciplinary analysis of underground online forums

在我们有良好受害数据的所有国家中，网络和电子犯罪现在约占所有财产犯罪的一半。包括骚扰在内的大量其他违法行为也发生在网上。因此，对于犯罪学家、律师、社会科学家、心理学家和其他人来说，能够研究网络犯罪并找出发生了什么是至关重要的。我们开始有一些非常好的数据来源，包括从CrimeBB数据库中的地下犯罪论坛收集的7000多万条消息。这些论坛是网络恶棍聚会、交易工具和技术、相互出售服务的地方。它们是犯罪学家研究年轻人如何卷入犯罪的金矿；社会科学家研究政治意识形态、种族主义和同性恋恐惧症的演变；律师对犯罪商业模式和他们如何应对警方干预感兴趣；以及其他许多人。目前缺失的一环是：人文和社会科学的学者目前没有工具来处理如此庞大的文本。在互联网时代之前，研究人员可能会采访几十名罪犯，手工对采访进行编码，并使用统计软件包进行分析；但处理数百万条消息需要新的方法。这个项目将利用自然语言处理学科来构建工具，使人文和社会科学的学者能够使用现代人工智能和机器学习(AI/ML)技术来处理这些海量文本。它们将帮助研究人员找到感兴趣的主题，确定正在讨论的犯罪类型，搜索在各种方式上与已确定的相似的消息，跟踪趋势，并在论坛上匹配用户。用户将能够寻找识别刚刚开始(因此可能成为初级预防方法的目标)和正在产生影响(因此可能值得采取更积极干预措施)的用户的指标。我们的工具还将使研究人员能够衡量预防犯罪举措和治安行动的效果，以便政策制定者能够收集哪些有效，哪些无效的证据。我们建立的工具将开始用于研究来自犯罪论坛的大型文本语料库，即搜索引擎为互联网做了什么--即让既没有技术技能也没有技术援助的研究人员可以访问这些资源。因此，它们将使现有数据资源得到更多利用，首先是CrimeBB数据库(由ESRC和EPSRC资助的前一个项目提供资金)，但不限于此。不同学科的研究人员对它们的使用也将使我们能够了解NLP工具，更广泛地说，AI/ML工具是如何被强有力地使用的。考虑到当前急于使用AI/ML技术，以及担心其中一些技术可能只是反映出他们训练数据中的偏差，导致天真的研究人员只衡量自己的尺子，这一点具有独立的重要性。仅仅发明新的工具是不够的；我们还必须弄清楚如何正确地使用它们，为此，至关重要的是与来自人文和社会科学多个学科的学者社区合作，解决一个共同的问题，使用共享的数据，在那里我们最终有机会获得一些事实。

Online and electronic crime now account for about half of all property crime, in all countries for which we have good victimisation data. A significant number of other offences, including harassment, also happen online. It is therefore essential for criminologists, lawyers, social scientists, psychologists and others to be able to study online crime and work out what's going on.We are starting to have some really good sources of data, including more than 70 million messages scraped from underground crime forums in the CrimeBB database. There forums are where cyber-crooks meet up, trade tools and techniques, and sell each other services. They are a gold mine for criminologists studying how young people get drawn into crime; social scientists studying the evolution of political ideology, racism and homophobia; lawyers interested in criminal business models and how they respond to police interventions; and many others.The missing link at present is this: that scholars in the humanities and social sciences do not at present have the tools to deal with such large bodies of text. In the pre-Internet era, researchers might have interviewed a few dozen criminals, coded up the interviews by hand and analysed them using a statistics package; but dealing with millions of messages requires new approaches.This project will draw upon the discipline of natural-language processing to build tools that will enable scholars in the humanities and social sciences deal with these large volumes of text using modern techniques of artificial intelligence and machine learning (AI/ML). They will help researchers find topics of interest, identify the types of crime being discussed, search for messages that are similar in various ways to those already identified, track trends, and match users across forums. Users will be able to look for indicators that identify users who are just starting out (and might therefore be targeted with primary prevention approaches) as well as those who are becoming influential (and might therefore be worth more aggressive interventions). Our tools will also enable researchers to measure the effect of both crime-prevention initiatives and policing action, so that policymakers can gather evidence of what works and what doesn't.The tools we build will start to do for research with large text corpora drawn from crime forums, what search engines have done for the Internet -- namely making such resources accessible to researchers who do not have either technical skills or technical assistance. They will therefore enable much more use to be made of existing data resources, starting with the CrimeBB database (which was funded in a previous project funded by ESRC and EPSRC), but not limited to it. Their use by researchers in diverse disciplines will also enable us to learn about how NLP tools, and more generally AI/ML tools, can be used robustly. This is of independent importance given the current rush to use AI/ML techniques and the concern that some of these techniques may simply reflect the bias in their training data, leading naive researchers to just measure their own ruler. It's not enough just to invent new tools; we also have to figure out how to use them properly, and for that, it's vital to work with a community of scholars from multiple disciplines in the humanities and social sciences on a shared problem, using shared data, and where we have some access eventually to ground truth.