Misinformation has experienced increased online diffusion, leveraging strategies, such as emotional manipulation, to influence users' opinions. Efforts are underway to develop tools to mitigate its effects, such as misinformation propagation models used to simulate the diffusion of information. There are different approaches within these models, although, they show a significant limitation by disregarding the content of the information shared, crucial to the diffusion. We consider it the central aspect of modeling information dissemination. To this end, we focus on Agent-Based Modeling due to its suitability to simulate the complex interactions and heterogeneous behaviors observed on social media. We base our approach on a state-of-the-art Agent-Based Model that we modify and extend to account for the texts of the messages shared, focusing on two aspects that influence agents' decisions: i) the novelty of the content and; ii) its diffusion and behavior over time. To determine whether this content proves informative, we conduct an empirical evaluation using social media data from Twitter. Based on our experimental results, we observe that our textual-based approach reflects information diffusion more realistically than the state of the art, reducing the error regarding real diffusion.

Reputation systems have been showed as effective mechanisms for capturing and extracting the global view a society has about some entities. Traditionally, these systems are based on capturing users' opinions through quantitative evaluations given by numerical ratings. However, it has been demonstrated that mapping opinions to numerical values might entail biased problems, skewing the reputation of some entities. In this work, we present our proposal for dealing with such problems, based on capturing opinions through comparative evaluations. Besides, we state that this mechanism can be successfully applied in MOOCs, in order to estimate the reputation of learning resources, allowing us to provide students/users with better resources.

In this paper we focus on the achievements of eMadrid initiative in some fields of technology-enhanced learning, mainly involving the improvement of the mechanisms for open educational content retrieval from Internet, considering Internet resources as potential learning objects. Also we facilitate the integration of remote laboratories and external tools in virtual campuses architectures supporting enriched capabilities and describe a way to cluster and identify learner weaknesses using a learning analytics approach in combination with the item response theory.

This work presents an adaptive sanctioning mechanism that can be applied in open multi-agent systems that are regulated through norms. This mechanism tries to identify the attributes of the environment that have some influence on agents decision making and uses such attributes to define sanctions that may prevent norm violations. Our approach adapts sanctions to particular agents and particular environmental states. The mechanism is deployed by using an infrastructure of institutional agents in charge of adapting and applying sanctions to external agents. The proposal is evaluated empirically in a p2p scenario, comparing it with a traditional sanctioning approach for normative systems.

This work presents an extension of the Environment Dimension of the Virtual Organization Model, which is an Organization Modeling Language to define Organization-Centered Multi-Agent Systems. This extension allows this model to regulate the environment by supporting artifacts for organizational mechanisms, an approach based on the Agents & Artifacts conceptual framework. The three main entities of this framework are agents, artifacts and workspaces, which have been integrated in this work inside the Virtual Organization Model. Additionally, this paper presents an application to the health care setting and an analysis of the related work on this topic.

Organizational mechanisms can be introduced in a multi-agent system with the aim of influencing the behavior of agents to achieve their objectives in a proper way. Thus, they are designed to achieve a better coordination between agents. In this paper, we propose to model organizational mechanisms by means of artifacts, which were presented within the Agents & Artifacts approach, and that present good advantages for coordinating agents environments. We claim that artifacts, as reactive entities located into the environment of a Multi-agent System, can help agents to reach their goals, seem to be a suitable abstraction for modeling organizational mechanisms. We also give some examples of possible uses.