ValueScope: Unveiling Implicit Norms and Values via Return Potential Model of Social Interactions

TL;DR

ValueScope introduces a theoretically grounded, scalable framework to quantify implicit social norms and values in online communities using the Return Potential Model (RPM). It operationalizes two interlinked predictors—the Normness Scale Predictor ($\Phi_d$) and the Community Preference Predictor ($\Psi_c$)—into a pipeline comprising Normness Measurement, Normness Distillation, and Community Preference Distillation. Validated on 13 Reddit communities across gender, politics, science, and finance, the approach reveals substantial diversity in norms even among related communities and demonstrates predictive power for temporal norm changes and the influence of external events. This framework enables nuanced moderation strategies and supports social-science inquiry into how norms form, crystallize, and evolve in digital spaces, with practical implications for platform design and community management. The work also introduces novel data-generation and evaluation techniques, including Community Language Simulation and synthetic-label-based NSP training, to enable large-scale analysis of implicit norms with robust validation.

Abstract

This study introduces ValueScope, a framework leveraging language models to quantify social norms and values within online communities, grounded in social science perspectives on normative structures. We employ ValueScope to dissect and analyze linguistic and stylistic expressions across 13 Reddit communities categorized under gender, politics, science, and finance. Our analysis provides a quantitative foundation showing that even closely related communities exhibit remarkably diverse norms. This diversity supports existing theories and adds a new dimension--community preference--to understanding community interactions. ValueScope not only delineates differing social norms among communities but also effectively traces their evolution and the influence of significant external events like the U.S. presidential elections and the emergence of new sub-communities. The framework thus highlights the pivotal role of social norms in shaping online interactions, presenting a substantial advance in both the theory and application of social norm studies in digital spaces.

Figures (65)

• Figure 1: The ValueScope framework. We characterize a comment along a norm dimension (e.g., formality), outputting the normness scale (e.g., a very casual comment has a formality scale of 0.1). Then, we predict the return potential, reflecting community preference (e.g., the number of upvotes). Finally, we plot the return potential against the normness scale using the Return Potential Model (RPM) to visualize community values.
• Figure 2: Data filtering pipeline, including preprocessing, lexical, fluency, and content preservation filters to ensure data quality, keeps 67% data after filtering.
• Figure 3: Estimated return potential over normness scales. Formality preferences in politics subreddits (top) and supportiveness preferences in science subreddits both corroborate prior findings about the communities.
• Figure 4: PMR of the top five subreddits for Serious--Humorous and Toxic--Supportive. The point of maximum return on an RPM curve describes the "ideal" behavior that would maximize community preference. For instance, these results show that r/askscience strongly prefers supportive comments.
• Figure 5: PRD across topical groups, reflecting the feedback strategy used by the community to regulate certain norms. All studied communities tend to use positive feedback: the gender related subreddits extensively reward behaviors aligned with their values, while the politics subreddits reward much more conservatively.