Modeling human behavior through the lens of online social networks presents both a significant opportunity and a challenge for understanding complex social phenomena, such as misinformation spread, opinion formation and polarization. While agent-based models (ABMs) are widely used for studying these social phenomena, parameter estimation remains a challenge, often relying on costly simulation-based heuristics. This work uses variational inference to estimate the parameters of an opinion dynamics ABM by transforming the estimation problem into an optimization task that can be solved directly. Our proposal relies on probabilistic generative ABMs (PGABMs): we start by synthesizing a probabilistic generative model from the ABM rules. Then, we transform the inference process into an optimization problem suitable for automatic differentiation. In particular, we use the Gumbel-Softmax reparameterization for categorical agent attributes and Stochastic Variational Inference for parameter estimation. Moreover, we explore the trade-offs of using variational distributions with different complexities: Normal distributions and Normalizing Flows. We validate our method on a bounded confidence model with agent roles (leaders and followers), by estimating both macroscopic (bounded confidence intervals and backfire thresholds) and microscopic (200 categorical agent-level roles) parameters more accurately than simulation-based and MCMC methods.

Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure.Although multiple factors contribute to the participation in activism, their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a quantitative, causal understanding of why people approach activism.In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion).Our framework, based on Stochastic Variational Inference applied to Bayesian Networks, learns the causal pathways over multiple time periods.Distinct from previous studies, our approach uses large-scale and fine-grained longitudinal data (2016 to 2022) to jointly model the roles of sociodemographic makeup, experience of extreme weather events, exposure to climate-related news, and social influence through online interactions.We find that among users interested in climate change, participation in online activist communities is indeed influenced by direct interactions with activists and largely by recent exposure to media coverage of climate protests.Among people aware of climate change, left-leaning people from lower socioeconomic backgrounds are particularly represented in online activist groups.Our findings offer empirical validation for theories of media influence and critical mass, and lay the foundations to inform interventions and future studies to foster public participation in collective action.

The foundation of my doctoral thesis is the estimation of agent-based models (ABMs) that simulate opinion dynamics using a likelihood-based method. I establish that the principles governing ABMs can be transformed into equivalent probabilistic generative models that facilitate a well-defined likelihood function. Consequently, I have incorporated these models into an automatic differentiation framework, which simplifies the process and improves the efficiency of performing maximum likelihood estimation through gradient descent techniques.

We show that a maximum likelihood approach for parameter estimation in agent-based models (ABMs) of opinion dynamics outperforms the typical simulation-based approach. Simulation-based approaches simulate the model repeatedly in search of a set of parameters that generates data similar enough to the observed one. In contrast, likelihood-based approaches derive a likelihood function that connects the unknown parameters to the observed data in a statistically principled way. We compare these two approaches on the well-known bounded-confidence model of opinion dynamics.