Users in Online Social Networks (OSNs,) leave traces that reflect their personality characteristics. The study of these traces is important for several fields, such as social science, psychology, marketing, and others. Despite a marked increase in research on personality prediction based on online behavior, the focus has been heavily on individual personality traits, and by doing so, largely neglects relational facets of personality. This study aims to address this gap by providing a prediction model for holistic personality profiling in OSNs that includes socio-relational traits (attachment orientations) in combination with standard personality traits. Specifically, we first designed a feature engineering methodology that extracts a wide range of features (accounting for behavior, language, and emotions) from the OSN accounts of users. Subsequently, we designed a machine learning model that predicts trait scores of users based on the extracted features. The proposed model architecture is inspired by characteristics embedded in psychology; i.e, it utilizes interrelations among personality facets and leads to increased accuracy in comparison with other state-of-the-art approaches. To demonstrate the usefulness of this approach, we applied our model on two datasets, namely regular OSN users and opinion leaders on social media, and contrast both samples’ psychological profiles. Our findings demonstrate that the two groups can be clearly separated by focusing on both Big Five personality traits and attachment orientations. The presented research provides a promising avenue for future research on OSN user characterization and classification.

OSN platforms are under attack by intruders born and raised within their own ecosystems. These attacks have multiple scopes from mild critiques to violent offences targeting individual or community rights and opinions. Negative publicity on microblogging platforms, such as Twitter, is due to the infamous Twitter bots which highly impact posts’ circulation and virality. A wide and ongoing research effort has been devoted to develop appropriate countermeasures against emerging “armies of bots”. However, the battle against bots is still intense and unfortunately, it seems to lean on the bot-side. Since, in an effort to win any war, it is critical to know your enemy, this work aims to demystify, reveal, and widen inherent characteristics of Twitter bots such that multiple types of bots are recognized and spotted early. More specifically in this work we: (i) extensively analyze the importance and the type of data and features used to generate ML models for bot classification, (ii) address the open problem of multi-class bot detection, identifying new types of bots, and share two new datasets towards this objective, (iii) provide new individual ML models for binary and multi-class bot classification and (iv) utilize explainable methods and provide comprehensive visualizations to clearly demonstrate interpretable results. Finally, we utilize all of the above in an effort to improve the so called Bot-Detective online service. Our experiments demonstrate high accuracy, explainability and scalability, comparable with the state of the art, despite multi-class classification challenges.

The need for a more energy-efficient future is now more evident than ever. Energy disagreggation (NILM) methodologies have been proposed as an effective solution for the reduction in energy consumption. However, there is a wide range of challenges that NILM faces that still have not been addressed. Herein, we propose HeartDIS, a generalizable energy disaggregation pipeline backed by an extensive set of experiments, whose aim is to tackle the performance and efficiency of NILM models with respect to the available data. Our research (i) shows that personalized machine learning models can outperform more generic models; (ii) evaluates the generalization capabilities of these models through a wide range of experiments, highlighting the fact that the combination of synthetic data, the decreased volume of real data, and fine-tuning can provide comparable results; (iii) introduces a more realistic synthetic data generation pipeline based on other state-of-the-art methods; and, finally, (iv) facilitates further research in the field by publicly sharing synthetic and real data for the energy consumption of two households and their appliances.