NSF Award: Using NLP to Identify Suspicious Transactions in Omnichannel Online C2C Marketplaces

Baylor University has been awarded funding under the SaTC program for Enabling Interdisciplinary Collaboration; a grant led by Principal Investigator Dr. Pablo Rivas and an amazing group of multidisciplinary researchers formed by:

  • Dr. Gissella Bichler from California State University San Bernardino, Center for Criminal Justice Research, School of Criminology and Criminal Justice.
  • Dr. Tomas Cerny is at Baylor University in the Computer Science Department, leading software engineering research.
  • Dr. Laurie Giddens from the University of North Texas, a faculty member at the G. Brint Ryan College of Business.
  • Dr. Stacy Petter is at Wake Forest University in the School of Business. She and Dr. Giddens have extensive research and funding in human trafficking research.
  • Dr. Javier Turek, a Research Scientist in Machine Learning at Intel Labs, is our collaborator in matters related to machine learning for natural language processing.

We also have two Ph.D. students working on this project: Alejandro Rodriguez and Korn Sooksatra.

This project was motivated by the increasing pattern of people buying and selling goods and services directly from other people via online marketplaces. While many online marketplaces enable transactions among reputable buyers and sellers, some platforms are vulnerable to suspicious transactions. This project investigates whether it is possible to automate the detection of illegal goods or services within online marketplaces. First, the project team will analyze the text of online advertisements and marketplace policies to identify indicators of suspicious activity. Then, the team will adapt the findings to a specific context to locate stolen motor vehicle parts advertised via online marketplaces. Together, the work will lead to general ways to identify signals of illegal online sales that can be used to help people choose trustworthy marketplaces and avoid illicit actors. This project will also provide law enforcement agencies and online marketplaces with insights to gather evidence on illicit goods or services on those marketplaces.

This research assesses the feasibility of modeling illegal activity in online consumer-to-consumer (C2C) platforms, using platform characteristics, seller profiles, and advertisements to prioritize investigations using actionable intelligence extracted from open-source information. The project is organized around three main steps. First, the research team will combine knowledge from computer science, criminology, and information systems to analyze online marketplace technology platform policies and identify platform features, policies, and terms of service that make platforms more vulnerable to criminal activity. Second, building on the understanding of platform vulnerabilities developed in the first step, the researchers will generate and train deep learning-based language models to detect illicit online commerce. Finally, to assess the generalizability of the identified markers, the investigators will apply the models to markets for motor vehicle parts, a licit marketplace that sometimes includes sellers offering stolen goods. This project establishes a cross-disciplinary partnership among a diverse group of researchers from different institutions and academic disciplines with collaborators from law enforcement and industry to develop practical, actionable insights.

Self-supervised modeling. After providing a corpus associated with a C2C domain of interest and ontologies, we will extract features followed by attention mechanisms for self-supervised and supervised tasks. The self-supervised models include the completion of missing information and domain-specific text encoding for learning representations. Then supervised tasks will leverage these representations to learn the relationships with targets.

Dr. Bejarano’s work is Recognized by Amazon

According to the World Federation of the Deaf, more than 70 million deaf people exist worldwide. More than 80% of them live in developing countries. Recent research by Dr. Gissella Bejarano, our very own postdoctoral research scientist, has been recognized for its impact on computer vision and speech recognition, providing opportunities to help individuals with disabilities. With support from AWS, Dr. Bejarano is finding better ways to translate Peruvian Sign Language using computer vision and natural language processing.

Read more about this in this release by AWS.

How High can you Fly? LCC Passenger Dissatisfaction

This study provides a better understanding of the LCC passenger dissatisfaction phenomenon, as we now have an idea of which themes are important and require urgent attention. The findings show that over 10 years, the LCC passenger dissatisfaction criteria evolved, meaning that LCCs should be strongly aware of areas of concern in order to maintain passenger satisfaction. Based on classic data analytics, four themes – flight delay, ground staff attitude, luggage handling, and seat comfort – were identified as playing a crucial role in passenger dissatisfaction. Interestingly, LCC passengers were not found to have a problem with cabin crew attitude. Two possible reasons for the major themes of ground staff dissatisfaction may simply be that LCC ground staff lack training and that passengers expect ground staff to have the authority to make decisions and to be aware of passengers’ needs. Overall, when ground staff is not able to deal with passengers’ demands, passengers feel dissatisfied. In addition, the study found that the check-in counter, food, airline ground announcements, airline responses, cleanliness and additional/personal costs are secondary themes in passenger dissatisfaction. This study, therefore, clearly shows that LCCs should prioritize their efforts to minimize passenger dissatisfaction by firstly dealing with the primary themes of passenger dissatisfaction. [pdf, bib]

The Final Themes of LCC Passenger Dissatisfaction