IDTraffickers: An Authorship Attribution Dataset to link and connect
  Potential Human-Trafficking Operations on Text Escort Advertisements
Vageesh Saxena Benjamin Bashpole Gijs Van Dijck Gerasimos Spanakis
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.