Crime data mining: A general framework and some examples

A general framework for crime data mining that draws on experience gained with the Coplink project at the University of Arizona is presented. By increasing efficiency and reducing errors, this scheme facilitates police work and enables investigators to allocate their time to other valuable tasks.published_or_final_versio

Lying to identity: analysis of latencies from interviews.

openDetecting liars of personal identities is becoming an increasingly important goal. However, an obstacle to this endeavor is that deceivers can prepare a "lie script" prior to investigative interviews, producing narratives that are indistinguishable from those of truth tellers. To overcome this limitation, specific interview techniques have been developed that pose cognitive disadvantages for deceivers, such as including unexpected questions alongside control and expected questions. Unexpected questions can be considered a "rehearsal averting strategy" since liars cannot anticipate and prepare responses in advance. Consequently, when confronted with unexpected questions, liars are compelled to generate an immediate deceptive statement, inhibit the truth, and replace it with a fabricated narrative, while ensuring that the deception remains undetectable to the interviewer. This process of information reconstruction leads to increased response times and error rates for unexpected questions. Even truth tellers will experience an increase in cognitive load when responding to unexpected questions, but their responses, based on genuine memory traces, will be more comparable. The purpose of this study is to assess whether it is possible to discriminate between identity liars and truth tellers by analyzing response times and errors obtained from face-to-face interviews that implement unexpected questions.Detecting liars of personal identities is becoming an increasingly important goal. However, an obstacle to this endeavor is that deceivers can prepare a "lie script" prior to investigative interviews, producing narratives that are indistinguishable from those of truth tellers. To overcome this limitation, specific interview techniques have been developed that pose cognitive disadvantages for deceivers, such as including unexpected questions alongside control and expected questions. Unexpected questions can be considered a "rehearsal averting strategy" since liars cannot anticipate and prepare responses in advance. Consequently, when confronted with unexpected questions, liars are compelled to generate an immediate deceptive statement, inhibit the truth, and replace it with a fabricated narrative, while ensuring that the deception remains undetectable to the interviewer. This process of information reconstruction leads to increased response times and error rates for unexpected questions. Even truth tellers will experience an increase in cognitive load when responding to unexpected questions, but their responses, based on genuine memory traces, will be more comparable. The purpose of this study is to assess whether it is possible to discriminate between identity liars and truth tellers by analyzing response times and errors obtained from face-to-face interviews that implement unexpected questions

A Multi-Layer Graphical Model for Approximate Identity Matching

Many organizations maintain identity information for their customers, vendors, and employees, etc. However, identities being compromised cannot be retrieved effectively. In this paper we first present a case study on identity problems existing in a local police department. The study show that more than half of the sampled suspects have altered identities existing in the police information system due to deception and errors. We build a taxonomy of identity problems based on our findings. The decision to determine matching identities involves some uncertainty because of the problems identified. We propose a probability-based multi-layer graphical model to capture the uncertainty. Experiments show that the proposed model performs significantly better than the searching technique based on exact-match. With 20% of training data labeled, the model with semi-supervised learning achieved performance comparable to that of fully supervised learning

Ethical and Social Challenges with developing Automated Methods to Detect and Warn potential victims of Mass-marketing Fraud (MMF)

Mass-marketing frauds (MMFs) are on the increase. Given the amount of monies lost and the psychological impact of MMFs there is an urgent need to develop new and effective methods to prevent more of these crimes. This paper reports the early planning of automated methods our interdisciplinary team are developing to prevent and detect MMF. Importantly, the paper presents the ethical and social constraints involved in such a model and suggests concerns others might also consider when developing automated systems

Multiple Account Identity Deception Detection in Social Media Using Nonverbal Behavior

Identity deception has become an increasingly important issue in the social media environment. The case of blocked users initiating new accounts, often called sockpuppetry, is widely known and past efforts, which have attempted to detect such users, have been primarily based on verbal behavior (e.g., using profile data or lexic al features in text). Although these methods yield a high detection accuracy rate, they are computationally inefficient for the social media environment, which often involves databases with large volumes of data. To date, little attention has been paid to detecting online decep- tion using nonverbal behavior. We present a detection method based on nonverbal behavior for identity deception, which can be applied to many types of social media. Using Wikipedia as an experimental case, we demonstrate that our proposed method results in high detection accuracy over previous methods pro- posed while being computationally efficient for the social media environment. We also demonstrate the potential of nonverbal behavior data that exists in social media and how designers and developers can leverage such nonverbal information in detecting deception to safeguard their online communities

A Rule and Graph-Based Approach for Targeted Identity Resolution on Policing Data

In criminal records, intentional manipulation of data is prevalent to create ambiguous identity and mislead authorities. Registering data electronically can result in misspelled data, variations in naming order, case sensitive data and inconsistencies in abbreviations and terminology. Therefore, trying to obtain the true identity (or identities) of a suspect can be a challenge for law enforcement agencies. We have developed a targeted approach to identity resolution which uses a rule-based scoring system on physical and official identity attributes and a graph-based analysis on social identity attributes to interrogate policing data and resolve whether a specific target is using multiple identities. The approach has been tested on an anonymized policing dataset, used in the SPIRIT project, funded by the European Union’s Horizon 2020. The dataset contains four ‘known’ identities using a total of five false identities. 23 targets were inputted into the methodology with no knowledge of how many or which had false identities. The rule-based scoring system ranked four of the five false identities with the joint highest score for the relevant target name with the remaining false identity holding the joint second highest score for its target. Moreover, when using graph analysis, 51 suspected false identities were found for the 23 targets with four of the five false identities linked through the crimes they had been involved in. Therefore, an identity resolution approach using both a rule-based scoring system and graph analysis, could be effective in facilitating the investigation process for law enforcement agencies and assisting them in finding criminals using false identities