The objective of this text is to describe the three categories that the Drug Policy Program at the Center for Teaching and Research in Economics (CIDE-PPD) database comprises, their limitations, and their main features. Additionally, we explain what we believe to be the source of the database we originally received and analyze its accuracy by comparing it with public records. We describe the validation and codification processes the database was subjected to, as well as the main biases and limitations the database may have. Additionally, we offer a preliminary analysis of the type of research that the CIDE-PPD Database can support. This analysis is not only relevant to those interested in studying the “war on drugs” in Mexico but also to those studying conflict in other countries involved in illegal drug production and trafficking, as well as countries experiencing conflicts related to organized crime.

Microblogging social networks are easily subverted by automated fake identities that amass disproportionately large influence. In this paper we present an effort to profile and screen such kind of accounts from existing and original ground truth obtained from the Twitter platform. Seventy-one explanatory properties solely extracted from profile and timeline information are evaluated and used to compare the efficacy of common supervised machine learning methods at this classification task. Results confirm that feasible and largely effective detection devices can be constructed for the problem at hand.

A novel multidisciplinary system for the automatic driving risk level classification is presented. The data considered involves the three basic traffic safety elements (driver, road, and vehicle), as well as knowledge from traffic experts. The driving experiments were conducted in a truck cabin simulator handled by a professional driver, considering the most common real-world enviroments. Each traffic expert evaluate the driving risk on a 0 to 100 visual analogue scale. The driver, road and vehicle information was used to train five different data mining algorithms in order to predict the driving risk level. The benefits of the completeness of the data considered in our system are presented and discussed.