Digital information and communication platforms like social media are implicated in the spread of digital polarization, mis-and dis-information and other types of anti-social online behavior that together increase inequality. These problems stem from a complex interplay of networks, content and social structures; however, current interdisciplinary work - for example work on virality and infodemics do not fully account for the nuances of these issues. This presentation will show how medical metaphors like infodemic get close, but do not capture, the full picture of how inequalities are enforced through networked affordances. Then, using an epidemiological and social determinants of health model, it shows how a related concept from the public health literature: syndemic, better accounts for the complexity. Finally this presentation shows how problems furthering inequality (like polarization, misinformation or online abuse) can be best understood using the metaphor of Information Syndemic. The Information Syndemic framework thus provides a transdisciplinary and fruitful way to understand how technologies, structures, individual behaviors and social networks deepen inequality.

Misinformation spreads rapidly on social media, reemerging even after debunking. Fact-checkers face major challenges when old claims surface in new forms or different languages. To address this, our research integrates two complementary strategies for detecting and managing repeated misinformation.

First, a brute force approach compares every new claim against an archive of debunked claims, converting statements into numerical vectors using Sentence-BERT. A high similarity threshold (for example, 0.9) ensures fact-checkers focus on the most critical matches, reducing unnecessary workload.

Second, a network-based approach creates a weighted network of debunked claims, with edges reflecting similarity scores. By applying community detection and identifying the most central claims, this method significantly limits the number of comparisons needed.

We evaluate both strategies using a large multilingual dataset of global fact-checking records and social media data from Spain, Poland, and Greece. Our findings reveal that spikes in misinformation coincide with major global events, underscoring the need for robust, adaptive systems. The brute force approach provides full coverage but can be resource-intensive, whereas the network-based method prioritizes efficiency by clustering claims and focusing on the most influential nodes.

Together, these approaches help fact-checkers, researchers, and policymakers more quickly identify repeated false information, preserving the integrity of public discourse. By leveraging past debunked claims and applying network science, this framework offers a scalable solution to the persistent challenges posed by misinformation on social media.

In the digital era, information consumption is predominantly channeled through online news media and disseminated on social media platforms. Understanding the complex dynamics of

the news media environment and users’ habits within the digital ecosystem is a challenging task that requires, at the same time, large databases and accurate methodological approaches. This study contributes to this expanding research landscape by employing network science method-

ologies and entropic measures to analyze the behavioral patterns of social media users sharing news pieces and dig into the diverse news consumption habits within different online social me-

dia user groups. Our analyses reveal that users are more inclined to share news classified as fake when they have previously posted conspiracy or junk science content and vice versa, creating

a series of “misinformation hot streaks”. To better understand these dynamics, we used three different measures of entropy to gain insights into the news media habits of each user, find-

ing that the patterns of news consumption significantly differ among users when focusing on disinformation spreaders as opposed to accounts sharing reliable or low-risk content. Thanks

to these entropic measures, we quantify the variety and the regularity of the news media diet, finding that those disseminating unreliable content exhibit a more varied and, at the same time, a more regular choice of web-domains. This quantitative insight into the nuances of news con-

sumption behaviors exhibited by disinformation spreaders holds the potential to significantly inform the strategic formulation of more robust and adaptive social media moderation policies.

Misleading newsletters can shape individuals' perceptions and pose a threat to societies; as we witnessed, for instance, by lowering the severity of follow-up stay-at-home orders burdening a significant challenge to the fight against COVID-19. In this research, we study (mis)information diffusion by reanalyzing behavioral data on online sharing (from Pennycook and Rand, 2019) and analyzing decision-making mechanisms using the Drift-Diffusion Model (DDM). We use a hierarchical Bayesian parameter estimation to obtain the DDM-free parameters that characterize each dynamic, disaggregating the data by age range and veracity, and obtaining a solid accuracy (in the response times probability functions) between the model and the data. We find that subjects display an increased instinctive inclination towards sharing misleading news, but rational thinking significantly curbs this reaction, especially for more cautious and older individuals. On top of network structures with similar characteristics as Twitter (now X), Mastodon, and Facebook, we use an agent-based model based on the well-known Susceptible-Infected-Recover (SIR) epidemic model to diffuse this individual knowledge to a large scale where individuals are exposed to (mis)information through friends and share (or not) content with probabilities driven by DDM. We found that the natural shape of these social online networks provides a fertile ground for any news to become viral rapidly. Yet we have found that, for the case of Twitter (X), limiting the number of followers of the most connected users proves to be an appropriate and feasible containment strategy.