IDT raffickers: An Authorship Attribution Dataset to link and connect Potential Human-Trafficking Operations on Text Escort Advertisements (2023)

Abstract: Human trafficking (HT) is a pervasive global issue affecting vulnerable individuals, violating their fundamental human rights. Investigations reveal that a significant number of HT cases are associated with online advertisements (ads), particularly in escort markets. Consequently, identifying and connecting HT vendors has become increasingly challenging for Law Enforcement Agencies (LEAs). To address this issue, we introduce IDTraffickers, an extensive dataset consisting of 87,595 text ads and 5,244 vendor labels to enable the verification and identification of potential HT vendors on online escort markets. To establish a benchmark for authorship identification, we train a DeCLUTR-small model, achieving a macro-F1 score of 0.8656 in a closed-set classification environment. Next, we leverage the style representations extracted from the trained classifier to conduct authorship verification, resulting in a mean r-precision score of 0.8852 in an open-set ranking environment. Finally, to encourage further research and ensure responsible data sharing, we plan to release IDTraffickers for the authorship attribution task to researchers under specific conditions, considering the sensitive nature of the data. We believe that the availability of our dataset and benchmarks will empower future researchers to utilize our findings, thereby facilitating the effective linkage of escort ads and the development of more robust approaches for identifying HT indicators.

## introduction human trafficking (ht) is a global crime that exploits vulnerable individuals for profit, affecting people of all ages and genders (europol, 2020; undoc, 2020). sex trafficking, a form of ht, involves controlling victims through violence, threats, deception, and debt bondage to force them into commercial sex @xcite . these operations occur in various locations such as massage businesses, brothels, strip clubs, and hotels (europol, 2020). women and girls comprise a significant portion of ht victims, particularly in the commercial sex industry @xcite . despite being advertised online, many victims have no control over the content of the advertisements (ads). around 65% of ht victims are advertised online for escort services in the united states @xcite . however, the large number of online escort ads makes manual detection of ht cases infeasible, leading to numerous unidentified instances @xcite . researchers and law enforcement agencies (leas) rely on sex trafficking indicators @xcite @xcite to identify ht ads. however, these investigations require linking ads to individuals or trafficking rings, often using phone numbers or email addresses to connect them. our research reveals that only 37% (202,439 out of 513,705) of collected ads have such contact information. moreover, manual detection of ht cases is time-consuming and resource-intensive due to the high volume of online escort ads. to address this, researchers and leas are exploring automated systems leveraging data analysis @xcite , knowledge graphs @xcite , network theory @xcite @xcite , and machine learning @xcite @xcite @xcite @xcite . a recent literature review by @xcite highlights current trends on various research fronts for combating ht. ## dataset the data in this research is collected from online posted escort ads between december 2015 and april 2016 on the backpage market, a classified ads website similar to craigslist on the surface web foot_1 . although the market listing hosted everything from apartments to escorts, a report by @xcite suggested that 90% of backpage's revenue came from adult ads. another report by @xcite suggests that backpage hosted escort listings concerned with the sex trafficking operations of women and children across 943 locations, 97 countries, and 17 languages. in this research, we accumulated 513,705 advertisements spread across 14 states and 41 cities in the united states. preprocessing: first, we begin by merging the title and description of the text ads using the "[sep]" token, as illustrated in figure 1[i] . figure 2(a) and figure 2(b) show that most ads in our dataset (approximately 99%) have a sentence length below 512 tokens and 2,000 characters. to generate ground truth, i.e., vendor labels, we employ the tjbatchextractor @xcite and cnn-lstm-crf classifier @xcite to extract phone numbers from the ads. subsequently, we utilize networkx @xcite to create vendor communities based on these phone numbers. each community is assigned a label id, which forms the vendor labels. for evaluation purposes, ads without phone numbers are discarded, resulting in a remaining dataset of 202,439 ads. following the findings of @xcite , which indicate that the average vendor of escort ads has 4-6 victims, we remove entries from vendors with fewer than five (average of 4-6) ads. the overall outcome of this process is a dataset comprising 87,595 unique ads and 5,244 vendor labels. most of these vendors have an ad frequency of under 1,000 foot_2 . after generating the vendor labels, we took measures to safeguard privacy by masking sensitive information within the ad descriptions. this included masking phone numbers, email addresses, age details, post ids, dates, and links to ensure that none of these details could be reverse-engineered, thereby minimizing the potential misuse of our dataset. despite our efforts to extract escort names and location information using bert-ner, roberta-ner-based entity recognition techniques, and the approach described by @xcite , we encountered a significant number of false positives. unfortunately, our attempts to mask this information resulted in further noise in our data. consequently, we decided to forgo this approach. ## conclusion in this research, we attempt to bridge the gap between connecting escort ads to potential ht vendors by introducing idtraffickers, an authorship attribution dataset collected from the backpage escort market advertisements (ads) within the united states geography. the dataset contains 87,595 ads with a text sequence of title and description and 5,244 vendor labels. since these ads lack ground truth for the authorship task, we generate the labels by connecting the phone numbers. first, we establish a benchmark for the authorship identification task by training a declutr-small classifier with a macro-f1 of 0.8656 in a closed-set classification environment. then, we utilize the style representations from the trained classifier to perform an open-set ranking task and establish a benchmark with an r-precision score of 0.8852 for the authorship verification task. our experiments reveal a massive difference between the language in id-traffickers and existing authorship datasets. by performing the authorship identification task, we allow our classifier to adapt and benefit from this domain knowledge for the authorship verification task. furthermore, we utilize the local and global feature attribution techniques to perform qualitative analysis on the trained classifier. finally, our analysis reveals that most misclassifications in the trained classifier occur due to the possibility of multiple labels attributing to the same vendor. however, despite the lower performance, the classifier succeeds in generating style representations that allow us to identify these vendors through the ranking task. we believe that the availability of our dataset, benchmarks, and analyses will empower future researchers and leas to utilize the findings, aiding in the effective linkage of escort ads and developing more robust approaches to identifying ht indicators.