GASTON: Graph-Aware Social Transformer for Online Networks

Abstract

Online communities have become essential digital third places for socialization and support, yet they also possess toxicity, echo chambers, and misinformation. Mitigating these harms requires computational models that can understand the nuance of online interactions to accurately detect harmful content such as toxicity and norm violation. This is difficult because the meaning of an individual post is rarely self-contained; it is dynamically constructed through the interplay of what is written (textual content) and where it is posted (social structure). We require models that effectively fuse these two signals to generate representations for online entities such as posts, users, and communities.

Current approaches often treat these different signals in isolation: text-only models analyze content but miss the local social norms that define acceptable behavior, while structure-only models map relationships but ignore the semantic content of discussions. Recent hybrid approaches attempt to bridge this gap but some rely on simple text averaging mechanisms to represent a user and a community, and in so doing flatten the rich, norm-defining identity.

To address this limitation, this thesis proposes GASTON (Graph-Aware Social Transformer for Online Networks), a graph learning framework designed to capture the essence of online social networks. It does so by modeling connections between all online entities, such as users, communities, and text. This makes it possible to ground user and text representations in their local norms, providing the necessary context to accurately classify behaviour in downstream tasks. The heart of our solution is a contrastive initialization strategy which pre-trains community representations based on user membership patterns, effectively capturing the unique signature of a community's user base before the model processes any text. This allows GASTON to distinguish between communities (e.g., a support group vs. a hate group) based on who interacts there, even if they share similar vocabulary.

We evaluate GASTON across a diverse set of socially-aware downstream tasks, including mental health stress detection, toxicity scoring, and norm violation detection. Our experiments demonstrate that GASTON outperforms state-of-the-art baselines, particularly in tasks where social context is critical for classification, such as detecting norm violations. Furthermore, we illustrate that these learned representations provide interpretable insights, offering a path toward user-empowered transparency in online spaces.