A Non-Parametric Learning Approach to Identify Online Human Trafficking

Human trafficking is among the most challenging law enforcement problems which demands persistent fight against from all over the globe. In this study, we leverage readily available data from the website "Backpage"-- used for classified advertisement-- to discern potential patterns of human trafficking activities which manifest online and identify most likely trafficking related advertisements. Due to the lack of ground truth, we rely on two human analysts --one human trafficking victim survivor and one from law enforcement, for hand-labeling the small portion of the crawled data. We then present a semi-supervised learning approach that is trained on the available labeled and unlabeled data and evaluated on unseen data with further verification of experts.Comment: Accepted in IEEE Intelligence and Security Informatics 2016 Conference (ISI 2016

Information Extraction in Illicit Domains

Extracting useful entities and attribute values from illicit domains such as human trafficking is a challenging problem with the potential for widespread social impact. Such domains employ atypical language models, have `long tails' and suffer from the problem of concept drift. In this paper, we propose a lightweight, feature-agnostic Information Extraction (IE) paradigm specifically designed for such domains. Our approach uses raw, unlabeled text from an initial corpus, and a few (12-120) seed annotations per domain-specific attribute, to learn robust IE models for unobserved pages and websites. Empirically, we demonstrate that our approach can outperform feature-centric Conditional Random Field baselines by over 18\% F-Measure on five annotated sets of real-world human trafficking datasets in both low-supervision and high-supervision settings. We also show that our approach is demonstrably robust to concept drift, and can be efficiently bootstrapped even in a serial computing environment.Comment: 10 pages, ACM WWW 201

Improving misspelled word solving for human trafficking detection in online advertising data

Social media is used by pimps to advertise their businesses for adult services due to easy accessibility. This requires the potentially computational model for law enforcement authorities to facilitate a detection of human trafficking activities. The machine learning (ML) models used to detect these activities mostly rely on text classification and often omit the correction of misspelled words, resulting in the risk of predictions error. Therefore, an improvement data processing approach is one of strategies to enhance an efficiency of human trafficking detection. This paper presents a novel approach to solving spelling mistakes. The approach is designed to select misspelled words, the replace them with the popular words having the same meaning based on an estimation of the probability of words and context used in human trafficking advertisements. The applicability of the proposed approach was demonstrated with the labeled human trafficking dataset using three classification models: k-nearest neighbor (KNN), naive Bayes (NB), and multilayer perceptron (MLP). The achievement of higher accuracy of the model predictions using the proposed method evidences an improved alert on human trafficking outperforming than the others. The proposed approach shows the potential applicability to other datasets and domains from the online advertisements

Navigating an Interdisciplinary Approach to Cybercrime Research

The internet has created new markets and enabled alternative business models for criminal activity, such as human trafficking. Consequently, research is needed to understand the complexity, occurrence, and impact of internet-enabled crime on victims and society. Many scholars have called for interdisciplinary approaches to study and develop interventions to address a broad range of cybercrimes, but this call is challenging to implement. Therefore, we provide a confessional account of our experience associated with developing an interdisciplinary research team and conducting research related to a specific form of cybercrime, predatory crime involving deceptive or covert solicitations. Our confessional account allows us to reflect on our project and discuss the challenges we have encountered along with a discussion of how we have addressed these challenges. We offer guidance to researchers in various stages of conducting interdisciplinary research based on our experiences with a specific form of cybercrime, internet-enabled crime

A Survey of Operations Research and Analytics Literature Related to Anti-Human Trafficking

Human trafficking is a compound social, economic, and human rights issue occurring in all regions of the world. Understanding and addressing such a complex crime requires effort from multiple domains and perspectives. As of this writing, no systematic review exists of the Operations Research and Analytics literature applied to the domain of human trafficking. The purpose of this work is to fill this gap through a systematic literature review. Studies matching our search criteria were found ranging from 2010 to March 2021. These studies were gathered and analyzed to help answer the following three research questions: (i) What aspects of human trafficking are being studied by Operations Research and Analytics researchers? (ii) What Operations Research and Analytics methods are being applied in the anti-human trafficking domain? and (iii) What are the existing research gaps associated with (i) and (ii)? By answering these questions, we illuminate the extent to which these topics have been addressed in the literature, as well as inform future research opportunities in applying analytical methods to advance the fight against human trafficking.Comment: 28 pages, 6 Figures, 2 Table

Don’t Want to Get Caught? Don’t Say It: The Use of EMOJIS in Online Human Sex Trafficking Ads

Technology has dramatically changed the way criminals conduct their illicit activities. Specifically, the Internet has become a major facilitator of online human sex trafficking. Traffickers are using these technologies to market their victims which presents new challenges for efforts to combat sex trafficking. This study used knowledge management principles and natural language processing methods to develop an improved ontology of online sex trafficking ads. The language of these ads is constantly evolving; therefore, this study explored the role of a new type of indicator, emoticons, to the ontology of human trafficking indicators

A Knowledge Management Approach to Identify Victims of Human Sex Trafficking

Social media and the interactive Web have enabled human traffickers to lure victims and then sell them faster and in greater safety than ever before. However, these same tools have also enabled investigators in their search for victims and criminals. We used system development action research methodology to create and apply a prototype designed to identify victims of human sex trafficking by analyzing online ads. The prototype used a knowledge management approach of generating actionable intelligence by applying a set of strong filters based on an ontology to identify potential victims. We used the prototype to analyze a data set generated from online ads. We used the results of this process to generate a revised prototype that included the use of machine learning and text mining enhancements. We used the revised prototype to identify potential victims in a second data set. The results of applying the prototypes suggest a viable approach to identifying victims of human sex trafficking in online ads