A survey of machine learning-based author profiling from texts analysis in social networks

• Published in: Multimedia tools and applications 2023-10, Vol.82 (24), p.36653-36686
• Main Authors: Ouni, Sarra, Fkih, Fethi, Omri, Mohamed Nazih
• Format: Article
• Language: English
• Subjects: Computer Communication Networks; Computer Science; Data Structures and Information Theory; Deep learning; Forensic computing; Machine learning; Multimedia Information Systems; Psychological factors; Social networks; Special Purpose and Application-Based Systems; Taxonomy
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-14711-8

Recently, online social networks, such as Twitter, Facebook, LinkedIn, etc., have grown exponentially with a large amount of information. These social networks have huge volumes of data, especially in textual form, which are unstructured and anonymous. This type of data usually leads to cybercrimes like cyberbullying, cyberterrorism, etc. and their analysis has nowadays become a serious challenge. From this perspective and to remedy this topical issue, various techniques have been proposed in the literature. Among the proposed solutions, author profiling represents the newest and most adopted technique by most researchers to discover hidden textual information. The objective of this technique is to identify the demographic or psychological aspects (age, sex, personality, mother tongue, etc.) of an author by examining the text that he has published. In recent years, this area of research has attracted many researchers who seek solutions for potential applications in various fields like marketing, computer forensics, security, etc. Within the scope of this article, we describe the author profiling task. Then, we present a brief thematic taxonomy and an illustration of some profiling solutions from the literature. In particular, different machine and deep learning techniques are detailed and discussed. This work also provides an overview of the main approaches, which we have studied in the literature, highlights the weak points and the strong points of each of these approaches. At the end of this study, a discussion of some research questions is presented and some future directions to circumvent the weaknesses detected in the approaches studied are presented in order to motivate academics and practitioners, who are interested in this problem that we assume essential, to advance solutions for profiling perpetrators on social networks.