Criminal Behavior Identification Using Social Media Forensics

Abstract

Human needs consist of five levels, which are: physiological needs, safety needs, love needs, esteem needs and self-actualization. All these needs lead to human behavior. If the environment of a person is positive, healthy behavior is developed. However, if the environment of the person is not healthy, it can be reflected in his/her behavior. Machines are intelligent enough to mimic human intelligence by using machine learning and artificial intelligence techniques. In the modern era, people tend to post their everyday life events on social media in the form of comments, pictures, videos, etc. Therefore, social media is a significant way of knowing certain behaviors of people such as abusive, aggressive, frustrated and offensive behaviors. Behavior detection by crawling the social media profile of a person is a crucial and important idea. The challenge of behavior detection can be sorted out by applying social media forensics on social media profiles, which involves NLP and deep learning techniques. This paper is based on the study of state of the art work on behavior detection, and based on the research, a model is proposed for behavior detection. The proposed model outperformed with an F1 score of 87% in the unigram + bigram class, and in the bigram + trigram class, it gave an F1 score of 88% when compared with models applied on state of the art work. This study is a great benefit to cybercrime and cyber-security agencies in shortlisting the profiles containing certain behaviors to prevent crimes in the future.

Exploring the Integration of a Nuptial Bond Between Neuroprediction and AI in Criminal Justice: A Review Study Conducted for Indian Judiciary

“…Supposed to be a reliable approach to anomaly detection in this field of criminal investigation. Additionally, other deep learning mechanisms, such as the DBN 25 and clustering-based methods (Ashraf et al, 2022), provide novel approaches for anomaly identification in digital forensics. Additionally, DNN 26 exist using a feature-level data fusion method (Kang HW, Kang HB, 2017) that can efficiently fuse multi-model data from several domains within related environmental contexts.…”

Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data

“…It has been suggested that a Deep Belief Network (DBN) [ 24 ] and a clustering-based methodology can provide a novel approach to anomaly identification in digital forensic data [ 25 ]. Data were collected from several channels, including electronic mail, online history, and chat logs, and the DBN was utilized to extract features from this information.…”