In the digital landscape, social media has emerged as a prevalent channel for global communication, connecting like-minded individuals worldwide. However, while facilitating information exchange, it is also susceptible to the dissemination of false information, posing a constant challenge to the reliability of online content. To address this issue, this paper introduces a novel methodology called TM-FID (Topic-oriented Multimodal False Information Detection), which combines false information detection and neural topic modeling within a semi-supervised multimodal approach. By jointly leveraging textual and visual information contained in online news, our approach provides insights into how false information influences specific discussion topics, thus enabling a comprehensive and fine-grained understanding of its spread and impact on social media conversation. Experimental evaluation carried out on a set of multimodal gossip-related news demonstrates the quality of the identified topics, assessed through a novel centroid-based metric, as well as the efficacy of the cross-attention mechanism used within TM-FID to accurately identify false information in multimodal news. Overall, the proposed methodology can enable effective strategies to counter the spread of false information, thereby fostering trust and confidence in the information shared on social media platforms.

Sustainable development denotes the enhancement of living standards in the present without compromising future generations’ resources. Sustainable Development Goals (SDGs) quantify the accomplishment of sustainable development and pave the way for a world worth living in for future generations. Scholars can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data, as intended by this work. We propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves (HSFCs) in order to semantically cluster new uncategorised SDG data and novel indicators, and efficiently place them in the environment of a distributed knowledge graph store. First, a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment, for retrieval of indicators and load-balancing along with an approach for data classification of entrant-indicators is described. Then, a thorough case study in a distributed knowledge graph environment experimentally evaluates our framework. The results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data, including intergovernmental organizations, government agencies and social welfare organizations. Our approach empowers SDG knowledge graphs for causal analysis, inference, and manifold interpretations of the societal implications of SDG-related actions, as data are accessed in reduced retrieval times. It facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching, as semantic cohesion of data is preserved.

This work proposes an educational scenario of game-based learning. The scenario is based on a novel serious game, that gets high school students acquainted with the Sustainable Development Goals (SDGs) and promotes the development of their computational thinking skills. Game-based learning is built upon collaborative problem solving and evolves around an interactive story augmented with small quests/inquiries. Students work together to navigate the serious game on the SDGs. As students complete SDGs-related quests/inquiries, the story evolves. The challenge is a game expansion with custom settings (backdrops/avatars) that will take place in Scratch, allowing students to differentiate their learning path. For the game, that was developed in Scratch visual programming environment, this works suggests an educational scenario and indicative student activities for hybrid (synchronous and asynchronous) education. Using the Flipped Classroom instructional strategy, the scenario starts asynchronously, unfolds synchronously, and is completed asynchronously. The proposed serious game follows the guided discovery method for the acquaintance with the SDGs and the accompanying educational scenario aims for students to acquire the required programming skills to develop a small interactive application on the SDGs. Through game expansion challenge students hone their programming skills in a purposeful and entertaining way. An evaluation by students concludes the work.

Sustainable development denotes the enhancement of living standards in the present without compromising future generations’ resources. Sustainable Development Goals (SDGs) quantify the accomplishment of sustainable development and pave the way for a world worth living in for future generations. Scholars can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data, as intended by this work. We propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves (HSFCs) in order to semantically cluster new uncategorised SDG data and novel indicators, and efficiently place them in the environment of a distributed knowledge graph store. First, a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment, for retrieval of indicators and load-balancing along with an approach for data classification of entrant-indicators is described. Then, a thorough case study in a distributed knowledge graph environment experimentally evaluates our framework. The results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data, including intergovernmental organizations, government agencies and social welfare organizations. Our approach empowers SDG knowledge graphs for causal analysis, inference, and manifold interpretations of the societal implications of SDG-related actions, as data are accessed in reduced retrieval times. It facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching, as semantic cohesion of data is preserved.

The paper studies the application of various content distribution policies for vehicular ad hoc networks (VANETs) and compares their effectiveness under various simulation scenarios. Our implementation augments the existing Veins tool, an open source framework for vehicular network simulations based on the discrete event simulator OMNET++ and SUMO, a tool that simulates traffic on road networks. The proposed solution integrates various additional features into the pre-existing Veins realizations and expands them to include the modeling and implementation of proposed caching and content distribution policies and the measurement of respective metrics. Moreover, we integrate machine learning algorithms for distribution policies into the simulation framework in order to efficiently study distribution of content to the network nodes. These algorithms are pre-trained neural network models adapted for VANETs. Using these new functions, we can specify the simulation parameters, run a plethora of experiments and proceed to evaluate metrics and policies for content distribution.