arXiv subjects

Mirta Galesic

Publications and source records attributed to Mirta Galesic.

12 recordsLinked to original sources

arXiv2026

Dynamics of collective minds in online communities

Collective discourse and action are driven by collective minds. These shared semantic representations and related processes shape societal responses to critical societal challenges such as climate change and political upheavals. In online communities, collective minds are susceptible to the influences of editorial practices and community dynamics, making them vulnerable to manipulation. However, understanding these influences is difficult because of the limits of experimenting with and predicting complex social systems. Here, we develop a computational model of collective minds, calibrated and validated with data from 400 million comments across five U.S. online news platforms and a survey. Our model enables us to quantitatively describe and experiment with different editorial agenda-setting practices and aspects of community dynamics to understand how they shape the collective mind. We find that some editorial influences can be reversed relatively rapidly, but others, such as amplification and reframing of certain topics, as well as community influences such as trolling and counterspeech, tend to persist and durably change the collective mind. These findings illuminate ways collective minds can avoid manipulation and pathways for communities to maintain healthy and authentic collective discourse amid ongoing societal challenges.

cs.SI↗

arXiv2026

Interplay between social contact and media exposure in the overestimation of racial diversity in the U.S

The general population systematically overestimates the size of minority groups, yet how these misperceptions vary across racial groups and geographical scales remains poorly understood. Using a purpose-built survey of the U.S. population, we examine overestimation of people of color (PoC) communities across four nested geographical scales: neighborhood, city, state, and nation. Our results demonstrate that overestimation is both scale- and group-dependent: the probability of overestimation increases progressively from local to national levels, and people of color overestimate their own group size more frequently than white people do at both the neighborhood and national levels. Among white respondents, we identify a scale-dependent divide in exposure mechanisms: direct interethnic social contact is the primary correlate of overestimation at local levels, whereas perceived frequency of coverage of people of color in news dominates at the national level. Furthermore, across both groups, frequent news consumption is associated with reduced rates of overestimation, while frequent social media use is associated with higher rates. These findings suggest that overestimation is real and present across scales and groups. This in turn can foster an `illusion of diversity', potentially undermining support for equity-promoting policies by creating the erroneous belief that representation goals have already been achieved.

physics.soc-ph↗

arXiv2025

Collective moderation of hate, toxicity, and extremity in online discussions

In the digital age, hate speech poses a threat to the functioning of social media platforms as spaces for public discourse. Top-down approaches to moderate hate speech encounter difficulties due to conflicts with freedom of expression and issues of scalability. Counter speech, a form of collective moderation by citizens, has emerged as a potential remedy. Here, we aim to investigate which counter speech strategies are most effective in reducing the prevalence of hate, toxicity, and extremity on online platforms. We analyze more than 130,000 discussions on German Twitter starting at the peak of the migrant crisis in 2015 and extending over four years. We use human annotation and machine learning classifiers to identify argumentation strategies, ingroup and outgroup references, emotional tone, and different measures of discourse quality. Using matching and time-series analyses we discern the effectiveness of naturally observed counter speech strategies on the micro-level (individual tweet pairs), meso-level (entire discussions) and macro-level (over days). We find that expressing straightforward opinions, even if not factual but devoid of insults, results in the least subsequent hate, toxicity, and extremity over all levels of analyses. This strategy complements currently recommended counter speech strategies and is easy for citizens to engage in. Sarcasm can also be effective in improving discourse quality, especially in the presence of organized extreme groups. Going beyond one-shot analyses on smaller samples prevalent in most prior studies, our findings have implications for the successful management of public online spaces through collective civic moderation.

cs.CY↗

arXiv2025

One pathogen does not an epidemic make: A review of interacting contagions, diseases, beliefs, and stories

From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences. Despite their success and breadth of adoption, the approach and structure of these models remain surprisingly siloed by field. Given the siloed nature of their development and widespread use, one persistent assumption is that a given contagion can be studied in isolation, independently from what else might be spreading in the population. In reality, countless contagions of biological and social nature interact within hosts (interacting with existing beliefs, or the immune system) and across hosts (interacting in the environment, or affecting transmission mechanisms). Additionally, from a modeling perspective, we know that relaxing these assumptions has profound effects on the physics and translational implications of the models. Here, we review mechanisms for interactions in social and biological contagions, as well as the models and frameworks developed to include these interactions in the study of the contagions. We highlight existing problems related to the inference of interactions and to the scalability of mathematical models and identify promising avenues of future inquiries. In doing so, we highlight the need for interdisciplinary efforts under a unified science of contagions and for removing a common dichotomy between social and biological contagions.

physics.soc-ph↗

arXiv2025

Cultural tightness and social cohesion under evolving norms

Successful collective action on issues from climate change to the maintenance of democracy depends on societal properties such as cultural tightness and social cohesion. How these properties evolve is not well understood because they emerge from a complex interplay between beliefs and behaviors that are usually modeled separately. Here we address this challenge by developing a game-theoretical framework incorporating norm-utility models to study the coevolutionary dynamics of cooperative action, expressed belief, and norm-utility preferences. We show that the introduction of evolving beliefs and preferences into the Snowdrift game and Prisoner's Dilemma leads to a proliferation of evolutionary stable equilibria, each with different societal properties. In particular, we find that a declining material environment can simultaneously be associated with increased cultural tightness (defined as the degree to which individuals behave in accordance with widely held beliefs) and reduced social cohesion (defined as the degree of social homogeneity i.e. the extent to which individuals belong to a single well-defined group). Loss of social homogeneity occurs via a process of evolutionary branching, in which a population fragments into two distinct social groups with strikingly different characteristics. The groups that emerge differ not only in their willingness to cooperate, but also in their beliefs about cooperation and in their preferences for conformity and coherence of their actions and beliefs. These results have implications for our understanding of the resilience of cooperation and collective action in times of crisis.

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arXiv2023

Group Threat, Political Extremity, and Collective Dynamics in Online Discussions

Collectives adapt their network structure to the challenges they face. It has been hypothesized that collectives experiencing a real or imagined threat from an outgroup tend to consolidate behind a few influential group members, and that network structures in which a few members have a very strong influence are more likely in politically extreme groups. These hypotheses have not been tested in large-scale real-world settings. We reconstruct networks of tens of thousands of commenters participating in comment sections of high-profile U.S. political news websites spanning the political spectrum from left to right, including Mother Jones, The Atlantic, The Hill, and Breitbart. We investigate the relationship between different indices of inequality of influence in commenters' networks and perceived group threat associated with significant societal events, from elections and political rallies to mass shootings. Our findings support the hypotheses that groups facing a real or imagined outgroup threat and groups that are more politically extreme are more likely to include disproportionately influential commenters. These results provide an extensive real-world test of theoretical accounts of collective adaptation to outgroup threats.

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arXiv2021

Impact and dynamics of hate and counter speech online

Citizen-generated counter speech is a promising way to fight hate speech and promote peaceful, non-polarized discourse. However, there is a lack of large-scale longitudinal studies of its effectiveness for reducing hate speech. To this end, we perform an exploratory analysis of the effectiveness of counter speech using several different macro- and micro-level measures to analyze 180,000 political conversations that took place on German Twitter over four years. We report on the dynamic interactions of hate and counter speech over time and provide insights into whether, as in `classic' bullying situations, organized efforts are more effective than independent individuals in steering online discourse. Taken together, our results build a multifaceted picture of the dynamics of hate and counter speech online. While we make no causal claims due to the complexity of discourse dynamics, our findings suggest that organized hate speech is associated with changes in public discourse and that counter speech -- especially when organized -- may help curb hateful rhetoric in online discourse.

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arXiv2021

Dynamical-System Model Predicts When Social Learners Impair Collective Performance

A key question concerning collective decisions is whether a social system can settle on the best available option when some members learn from others instead of evaluating the options on their own. This question is challenging to study, and previous research has reached mixed conclusions, because collective decision outcomes depend on the insufficiently understood complex system of cognitive strategies, task properties, and social influence processes. This study integrates these complex interactions together in one general yet partially analytically tractable mathematical framework using a dynamical system model. In particular, it investigates how the interplay of the proportion of social learners, the relative merit of options, and the type of conformity response affect collective decision outcomes in a binary choice. The model predicts that when the proportion of social learners exceeds a critical threshold, a bi-stable state appears in which the majority can end up favoring either the higher- or lower-merit option, depending on fluctuations and initial conditions. Below this threshold, the high-merit option is chosen by the majority. The critical threshold is determined by the conformity response function and the relative merits of the two options. The study helps reconcile disagreements about the effect of social learners on collective performance and proposes a mathematical framework that can be readily adapted to extensions investigating a wider variety of dynamics.

physics.soc-ph↗

arXiv2020

Countering hate on social media: Large scale classification of hate and counter speech

Hateful rhetoric is plaguing online discourse, fostering extreme societal movements and possibly giving rise to real-world violence. A potential solution to this growing global problem is citizen-generated counter speech where citizens actively engage in hate-filled conversations to attempt to restore civil non-polarized discourse. However, its actual effectiveness in curbing the spread of hatred is unknown and hard to quantify. One major obstacle to researching this question is a lack of large labeled data sets for training automated classifiers to identify counter speech. Here we made use of a unique situation in Germany where self-labeling groups engaged in organized online hate and counter speech. We used an ensemble learning algorithm which pairs a variety of paragraph embeddings with regularized logistic regression functions to classify both hate and counter speech in a corpus of millions of relevant tweets from these two groups. Our pipeline achieved macro F1 scores on out of sample balanced test sets ranging from 0.76 to 0.97---accuracy in line and even exceeding the state of the art. On thousands of tweets, we used crowdsourcing to verify that the judgments made by the classifier are in close alignment with human judgment. We then used the classifier to discover hate and counter speech in more than 135,000 fully-resolved Twitter conversations occurring from 2013 to 2018 and study their frequency and interaction. Altogether, our results highlight the potential of automated methods to evaluate the impact of coordinated counter speech in stabilizing conversations on social media.

cs.CY↗

arXiv2019

Homophily and minority size explain perception biases in social networks

People's perceptions about the size of minority groups in social networks can be biased, often showing systematic over- or underestimation. These social perception biases are often attributed to biased cognitive or motivational processes. Here we show that both over- and underestimation of the size of a minority group can emerge solely from structural properties of social networks. Using a generative network model, we show analytically that these biases depend on the level of homophily and its asymmetric nature, as well as on the size of the minority group. Our model predictions correspond well with empirical data from a cross-cultural survey and with numerical calculations on six real-world networks. We also show under what circumstances individuals can reduce their biases by relying on perceptions of their neighbors. This work advances our understanding of the impact of network structure on social perception biases and offers a quantitative approach for addressing related issues in society.

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arXiv2018

Statistical Physics Models of Belief Dynamics: Theory and Empirical Tests

We build simple computational models of belief dynamics within the framework of discrete-spin statistical physics models, and explore how suitable they are for understanding and predicting real-world belief change on both the individual and group levels. We find that accurate modeling of real-world patterns requires attending to social interaction rules that people use, network structures in which they are embedded, distributions of initial beliefs and intrinsic preferences, and the relative importance of social information and intrinsic preferences. We demonstrate that these model parameters can be constrained by empirical measurement, and the resulting models can be used to investigate the mechanisms underlying belief dynamics in actual societies. We use data from two longitudinal studies of belief change, one on 80~individuals living in an MIT dorm during the 2008 presidential election season, and another on 94~participants recruited from Mechanical Turk during the 2016 presidential election primary season. We find that simple statistical physics-based models contain predictive value for real-world belief dynamics and enable empirical tests of different assumptions about the underlying network structure and the social interaction rules.

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arXiv2016

Social learning strategies modify the effect of network structure on group performance

The structure of communication networks is an important determinant of the capacity of teams, organizations and societies to solve policy, business and science problems. Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both well-connected efficient and poorly connected inefficient network structures. Here we argue that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members. We show that efficient networks outperform inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts. However, inefficient networks are superior when individuals follow the best member by copying the group member with the highest payoff. In addition, groups relying on conformity based on a small sample of others excel at complex tasks, while groups following the best member achieve greatest performance for simple tasks. Our findings reconcile contradictory results in the literature and have broad implications for the study of social learning across disciplines.

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