Today's digital society creates an environment potentially conducive to the exchange of deceptive information. The dissemination of misleading information can have severe consequences on society. This research investigates the possibility of using shared characteristics among reviews, news articles, and emails to detect deception in text-based communication using machine learning techniques. The experiment discussed in this paper examines the use of Bag of Words and Part of Speech tag features to detect deception on the aforementioned types of communication using Neural Networks, Support Vector Machine, Naïve Bayesian, Random Forest, Logistic Regression, and Decision Tree. The contribution of this paper is twofold. First, it provides initial insight into the identification of text communication cues useful in detecting deception across different types of text-based communication. Second, it provides a foundation for future research involving the application of machine learning algorithms to detect deception on different types of text communication.

Realistic case studies are essential to training successful digital forensics examiners. However, the generation of realistic datasets is time‐consuming and resource taxing. This paper presents a technical solution that populates Android emulators with realistic mobile forensic data. The emulator's data can be extracted into a raw disk image that is usable in mobile forensic training scenarios. In addition, the tool allows a user to populate the Android emulators with custom text messages, phone contacts, phone calls, and files. This population task is achieved by utilizing the Android Debug Bridge, Android Content Providers, SQLite databases, and the NodeJS runtime environment. This paper presents the software design and development, the requirements and limitations, and the testing process implemented in this research. The contribution of this paper is twofold. First, it identifies potential data and mechanisms to generate Android mobile forensic datasets using customized data population. Second, it creates a foundation for future research on the topic of mobile forensic emulators for training purposes. This article is categorized under: Digital and Multimedia Science > Mobile Forensics Crime Scene Investigation > Education and Formation