LDA2Net Digging under the surface of COVID-19 scientific literature topics via a network-based approach.

During the COVID-19 pandemic, the scientific literature related to SARS-COV-2 has been growing dramatically. These literary items encompass a varied set of topics, ranging from vaccination to protective equipment efficacy as well as lockdown policy evaluations. As a result, the development of automatic methods that allow an in-depth exploration of this growing literature has become a relevant issue, both to identify the topical trends of COVID-related research and to zoom-in on its sub-themes.

The architecture of partisan debates: The online controversy on the no-deal Brexit.

We propose a framework to analyse partisan debates that involves extracting, classifying and exploring the latent argumentation structure and dynamics of online societal controversies. In this paper, the focus is placed on causal arguments, and the proposed framework is applied to the Twitter debate on the consequences of a hard Brexit scenario. Regular expressions based on causative verbs, structural topic modelling, and dynamic time warping techniques were used to identify partisan faction arguments, as well as their relations, and to infer agenda-setting dynamics.

Representation of Jews and Anti-Jewish Bias in 19th Century French Public Discourse: Distant and Close Reading.

We explore through the lens of distant reading the evolution of discourse on Jews in France during the XIX century. We analyze a large textual corpus including heterogeneous sources-literary works, periodicals, songs, essays, historical narratives-to trace how Jews are associated to different semantic domains, and how such associations shift over time. Our analysis deals with three key aspects of such changes: the overall transformation of embedding spaces, the trajectories of word associations, and the comparative projection of different religious groups over different, historically relevant semantic dimensions or streams of discourse.

Hierarchical decision-making produces persistent differences in learning performance.

Human organizations are commonly characterized by a hierarchical chain of command that facilitates division of labor and integration of effort. Higher-level employees set the strategic frame that constrains lower-level employees who carry out the detailed operations serving to implement the strategy. Typically, strategy and operational decisions are carried out by different individuals that act over different timescales and rely on different kinds of information.

From Actions to Effects: Three Constraints on Event Mappings.

Events can be modeled through a geometric approach, representing event structures in terms of spaces and mappings between spaces. At least two spaces are needed to describe an event, an action space and a result space. In this article, we invoke general mathematical structures in order to develop this geometric perspective.

Neural network models of learning and categorization in multigame experiments.

Previous research has shown that regret-driven neural networks predict behavior in repeated completely mixed games remarkably well, substantially equating the performance of the most accurate established models of learning. This result prompts the question of what is the added value of modeling learning through neural networks. We submit that this modeling approach allows for models that are able to distinguish among and respond differently to different payoff structures.

Predicting human interactive learning by regret-driven neural networks.

Much of human learning in a social context has an interactive nature: What an individual learns is affected by what other individuals are learning at the same time. Games represent a widely accepted paradigm for representing interactive decision-making. We explored the potential value of neural networks for modeling and predicting human interactive learning in repeated games.

The emergence of simple languages in an experimental coordination game.

We investigate in a series of laboratory experiments how costs and benefits of linguistic communication affect the emergence of simple languages in a coordination task when no common language is available in the beginning. The experiment involved pairwise computerized communication between 152 subjects involved in at least 60 rounds. The subjects had to develop a common code referring to items in varying lists of geometrical figures distinguished by up to three features.