Emotion is a natural intrinsic state of mind that drives human behavior, social interaction, and decision-making. Due to the rapid expansion in the current era of the Internet, online social media (OSM) platforms have become popular means of expressing opinions and communicating emotions. With the emergence of natural language processing (NLP) techniques powered by artificial intelligence (AI) algorithms, emotion detection (ED) from user-generated OSM data has become a prolific research domain. However, it is challenging to extract meaningful features for identifying discernible patterns from the short, informal, and unstructured texts that are common on micro-blogging platforms like Twitter. In this paper, we introduce a novel representation of features extracted from user-generated Twitter data that can capture users’ emotional states. An advanced approach based on Genetic Algorithm (GA) is used to construct the input representation which is composed of stylistic, sentiment, and linguistic features extracted from tweets. A voting ensemble classifier with weights optimized by a GA is introduced to increase the accuracy of emotion detection using the novel feature representation. The experimental results demonstrate that the proposed approach outperforms the state-of-the-art classical machine learning-based emotion detection techniques, achieving the highest level of precision (96.49%), recall (96.49%), F1-score (96.49%), and accuracy (96.49%).

Responsible, ethical, and trustworthy decision-making powered by the new generation of artificial intelligence (AI) and deep learning (DL) recently emerged as one of the key societal challenges. This chapter provides a comprehensive review of state-of-the-art methods that emerged very recently in the domain of trustworthiness, fairness, and authenticity of online social media. Furthermore, this chapter discusses open problems, provides examples of other application domains in the realm of computer vision and intelligent computing, recommends bias mitigation strategies, and provides insights on the future developments in this key research domain.

The reliance on Online Social Networks (OSN) for both formal and informal social interactions has dramatically changed the way people communicate. In this paper, a novel Social Behavioral Biometric (SBB), human micro-expression, is introduced for person identification. An emotion detection model is developed to extract emotion probability scores from person’s writing samples posted on Twitter. The corresponding emotion-progression features are extracted using an original technique that turns users’ microblogs into emotion-progression signals. Finally, a novel social behavioral biometric system that leverages rank-level weighted majority voting to achieve an accurate person identification is implemented. The proposed system is validated on a proprietary benchmark dataset consisting of 250 Twitter users. The experimental results convincingly demonstrate that the proposed social behavioral biometric, human micro-expression, possesses a strong distinguishable ability and can be used for person identification. The study further reveals that the proposed social behavioral biometric outperforms all the original SBB traits.