# Identifying Persons of Interest in Digital Forensics Using NLP-Based AI
## 1 Introduction  
Digital forensics is an interdisciplinary domain of research encompassing contributions from computer science, criminology, and law. It involves the collection, analysis, interpretation, and presentation of evidence sourced from various digital devices. This evidence includes data from smartphones, desktop and laptop computers, gaming consoles, and a wide range of Internet of Things (IoT) devices, such as smart home systems, wearable technology, and network-connected sensors [1]. Much of the data analyzed in digital forensics investigations is user-generated [2]. According to a report by EdgeDelta in 2023, the average consumer of digital devices generates approximately 16 terabytes of data per day [3]. While many digital forensics practitioners utilize automated tools to assist in analyzing this vast amount of data, the process of parsing digital device contents for relevant evidentiary data often remains largely manual [4]. For example, a forensic investigator may employ tools like Cellebrite to search for specific evidence on a suspect's smartphone; however, they must often rely on search features within the tool to identify pertinent information [5]. Natural Language Processing (NLP) is a subdomain of computer science focused on spoken and written human languages [6]. It aims to enhance the efficiency of human-computer interaction [7]. NLP enables computer systems to process human input and provide meaningful output [8]. Numerous algorithms exist to process textual input and produce applications in various domains. Notable NLP algorithms include Latent DirichletAllocation, word embedding models, pairwise correlation, n-gram analysis, named entity recognition, and sentiment analysis [9]. One of AI's most prominent features is its ability to identify patterns in large data streams that are otherwise not easily discernible through manual analysis [10]. Healthcare information systems use pattern recognition to identify potential health risks in patients, while cybersecurity organizations utilize similar algorithms to detect malware and other threats to critical infrastructure [11]. Studies have attempted to predict human behavior using statistics and NLP [12]. For example, one study employed log files, user logins, email communications, and other data to predict insider threats within an organization [13]. Developing a machine learning model that can accurately predict human behavior or decision-making is challenging due to the difficulty of quantifying psychological variables, which often suffer from bias and insufficient information [14]. Research has shown that ongoing antisocial behavior can be identified in social media using machine learning classifiers and semantic analysis [15]. Additionally, sentiment data can be used as a variable in machine learning models to predict criminal behavior [16; 17]. Considering these data points, we propose a technique that employs pre-trained deep learning emotion classifiers along with other NLP tools to identify persons of interest within a large corpus of text [18]. The corpus could be an aggregated dataset of emails and text messages or hours of transcribed audio footage. In our research, the corpus consists of a dialog between nine fictitious characters over a thirty-day period. The contribution of this research is a practical criminological application of NLP techniques to the domain of digital forensics that has not been explored in previous studies. Furthermore, this research will demonstrate how our emotions and use of language can betray us under scientific scrutiny. This hypothesis will be demonstrated through the creation of a sociolinguistic chronology that (through inference) ties a speaker to an event as a Person of Interest (PoI). A PoI can either be eliminated as a suspect through further investigation or validated by linguistically tying them to other suspects and to key themes in a case. The remainder of this paper is organized as follows: Section 2 presents a review of relevant literature.