The Internet Yellow Pages (IYP) aggregates information from multiple sources about Internet routing into a unified, graph-based knowledge base. However, querying it requires knowledge of the Cypher language and the exact IYP schema, thus limiting usability for non-experts. In this paper, we propose ChatIYP, a domain-specific Retrieval-Augmented Generation (RAG) system that enables users to query IYP through natural language questions. Our evaluation demonstrates solid performance on simple queries, as well as directions for improvement, and provides insights for selecting evaluation metrics that are better fit for IYP querying AI agents.

Cancer prevalence, particularly among older individuals, imposes a significant burden on healthcare systems. However, care for older cancer patients is often insufficient and fails to address their specific needs. The LifeChamps H2020 EU project aims to develop a patient-centered digital platform to improve monitoring, anticipation, and support for complications that can deteriorate the health-related quality of life (HRQoL) of older cancer survivors. By facilitating comprehensive clinical assessments and new integrated care models, it addresses the challenges and gaps in clinical practice, promoting patient-centered care through remote digital monitoring tools that collect HRQoL data not typically captured in routine clinical practice.

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.

Popular microblogging platforms (such as Twitter) offer a fertile ground for open communication among humans, however, they also attract many bots and automated accounts "disguised" as human users. Typically, such accounts favor malicious activities such as phishing, public opinion manipulation and hate speech spreading, to name a few. Although several AI driven bot detection methods have been implemented, the justification of bot classification and characterization remains quite opaque and AI decisions lack in ethical responsibility. Most of these approaches operate with AI black-boxed algorithms and their efficiency is often questionable. In this work we propose Bot-Detective, a web service that takes into account both the efficient detection of bot users and the interpretability of the results as well. Our main contributions are summarized as follows: i) we propose a novel explainable bot-detection approach, which, to the best of authors' knowledge, is the first one to offer interpretable, responsible, and AI driven bot identification in Twitter, ii) we deploy a publicly available bot detection Web service which integrates an explainable ML framework along with users feedback functionality under an effective crowdsourcing mechanism; iii) we build the proposed service under a newly created annotated dataset by exploiting Twitter's rules and existing tools. This dataset is publicly shared for further use. In situ experimentation has showcased that Bot-Detective produces comprehensive and accurate results, with a promising service take up at scale.

This demo showcases some of the capabilities of Diligent, a platform for collecting and analysing data from Online Social Networks and is still under development. Diligent relies on microservices and reactive streams, which optimize the time spent (t), to the resources used (r), ratio (t/r). The proposed demo will present: - The vast hardware utilization margins produced by using both blocking and reactive I/O approaches. - The performance gap between using blocking I/O and Reactive I/O clients. Both experiments highlight the added benefits of using reactive approaches in online social network data processing systems.

Social media dominance largely affects multi-store brands success potential. How can a brand choose the optimal place to locate its stores, given the social media pulse? Which are the suitable metrics to guide such challenging decisions? This work addresses such crucial problems by a novel location casting approach which extracts and integrates knowledge from open data and social media, providing specific indicators and a systematic pipeline for effective locations casting. Emphasis is placed on how the derived knowledge will assess the particular characteristics of accessibility, interest, and centrality to identify fine grained urban indicators. The proposed pipeline predicts the success potential of a chain store's location, under individual or combined such indicators individually. The experimentation under qualitative tests, indicates that the proposed approach provides reliable estimations of brand's locations suitability, and also outperforms existing similar state-of-the-art approaches.