Unpacking polarization: Antagonism and Alignment in Signed Networks of Online Interaction

Online polarization research currently focuses on studying single-issue opinion distributions or computing distance metrics of interaction network structures. Limited data availability often restricts studies to positive interaction data, which can misrepresent the reality of a discussion. We introduce a novel framework that aims at combining these three aspects, content and interactions, as well as their nature (positive or negative), while challenging the prevailing notion of polarization as an umbrella term for all forms of online conflict or opposing opinions. In our approach, built on the concepts of cleavage structures and structural balance of signed social networks, we factorize polarization into two distinct metrics: Antagonism and Alignment. Antagonism quantifies hostility in online discussions, based on the reactions of users to content. Alignment uses signed structural information encoded in long-term user-user relations on the platform to describe how well user interactions fit the global and/or traditional sides of discussion. We can analyse the change of these metrics through time, localizing both relevant trends but also sudden changes that can be mapped to specific contexts or events. We apply our methods to two distinct platforms: Birdwatch, a US crowd-based fact-checking extension of Twitter, and DerStandard, an Austrian online newspaper with discussion forums. In these two use cases, we find that our framework is capable of describing the global status of the groups of users (identification of cleavages) while also providing relevant findings on specific issues or in specific time frames. Furthermore, we show that our four metrics describe distinct phenomena, emphasizing their independent consideration for unpacking polarization complexities

Learning non-linear payoff transformations in multi-agent systems

Treball fi de màster de: Master in Intelligent Interactive SystemsTutor: Vicenç GómezThe use of Deep Reinforcement Learning methodologies has been successful in recent years in cooperative multi-agent systems. However, this success has been mostly empirical and there is a lack of theoretical understanding and solid description of the learning process of those algorithms. The discussion of whether the limitations of these algorithms can be tackled with tuning and optimization or, contrarily, are constrained by their own definition in these models can also easily be put forward. In this work, we propose a theoretical formulation to reproduce one of the claimed limitations of Value Decomposition Networks (VDN), when compared to its improved related model QMIX, regarding their representational capacity. Both of these algorithms follow the centralized-learning-decentralized-execution fashion. For this purpose, we scale down the dimensions of the system to bypass the need for deep learning structures and work with a toy model two-step game and a series of one-shot games that are randomly generated to produce non-linear payoff growth. Despite their simplicity, these settings capture multi-agent challenges such as the scalability problem and the non-unique learning goals. Based on our analytical description, we are also able to formulate a possible alternative solution to this limitation through the use of simple non-linear transformations of the payoff, which sets a possible direction of future work regarding larger scale systems