Recognizing author identity from digital footprints without having a large corpus of documents from an individual is of keen interest to security researchers and government agencies. Users reveal aspects of their personality via the content they share with their social media followers and through the patterns in their interactions on online networking platforms. This study examines the potency of emerging natural language processing (NLP) methods in analyzing social network activity. A linguostylistic personality traits assessment (LPTA) system is developed to estimate Twitter users’ personality traits based on their tweets using the Myers-Briggs-type indicator (MBTI) and big-five personality scales. A novel input representation mechanism is proposed to process tweets by converting them into real-valued vectors using frequency, co-occurrence, and context (FCC) measures. Other prevalent text representation schemes, such as one-hot encoding, count-based vectorization, and pretrained language model representations are used as comparators. A genetic algorithm (GA) approach is proposed to reduce the feature set and increase the efficacy of the features extracted. The developed system outperforms the state-of-the-art research by reliably estimating the user’s latent personality traits while using 50 or fewer tweets per user.

Biometric de-identification is an emerging topic of research within the information security domain that integrates privacy considerations with biometric system development. A comprehensive overview of research in the context of authentication applications spanning physiological, behavioral, and social-behavioral biometric systems and their privacy considerations is discussed. Three categories of biometric de-identification are introduced, namely complete de-identification, auxiliary biometric preserving de-identification, and traditional biometric preserving de-identification. An overview of biometric de-identification in emerging domains such as sensor-based biometrics, social behavioral biometrics, psychological user profile identification, and aesthetic-based biometrics is presented. The article concludes with open questions and provides a rich avenue for subsequent explorations of biometric de-identification in the context of information privacy.

Personality traits have been shown to have strong influences on important aspects of life such as success in the workplace, political temperament, and general emotional stability. Computer-based personality assessments using information from social networking platforms have shown to be more accurate than judgments made by people close to the subject. This paper presents a personality traits classification system that incorporates language-based features, based on count-based vectorization (TF-IDF) and the GloVe word embedding technique, with an ensemble prediction system consisting of gradient-boosted decision trees and an SVM classifier. This combination allows to reliably estimate certain personality traits using only the latest 50 tweets from a user's profile. The performance of the proposed system is validated on a large, publicly available dataset and compares favourably with other state-of-the-art methods.

Smart societies of the future will increasingly rely on harvesting rich information generated by day-to-day activities and interactions of its inhabitants. Among the multitude of such interactions, web-based social networking activities became an integral part of everyday human communication. Flickr, Facebook, Twitter, and LinkedIn are currently used by millions of users worldwide as a source of information, which is growing exponentially over time. In addition to idiosyncratic personal characteristics, web-based social data include person-to-person communication, online activity patterns, and temporal information, among others. However, analysis of social interaction-based data has been studied from the perspective of person identification only recently. In this chapter, the authors elaborate on the concept of using interaction-based features from online social networking platforms as a part of social behavioral biometrics research domain. They place this research in the context of smart societies and discuss novel social biometric features and their potential use in various applications.