# Predicting Opinion Dynamics via Sociologically-Informed Neural Networks

arXiv: https://arxiv.org/abs/2207.03990 · HTML: https://mlsanity.com/paper/2207.03990/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Automatic differentiation in machine learning: a survey](https://mlsanity.com/paper/1502.05767/) (2015)
- [Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems](https://mlsanity.com/paper/1809.08327/) (2018)
- [Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations](https://mlsanity.com/paper/1811.02033/) (2018)
- [Deep Learning of Vortex Induced Vibrations](https://mlsanity.com/paper/1808.08952/) (2018)
- [GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution](https://mlsanity.com/paper/1611.04051/) (2016)
- [Learning and Forecasting Opinion Dynamics in Social Networks](https://mlsanity.com/paper/1506.05474/) (2015)
- [Social Influence and the Collective Dynamics of Opinion Formation](https://mlsanity.com/paper/1311.3475/) (2013)
- [Learning Opinion Dynamics From Social Traces](https://mlsanity.com/paper/2006.01673/) (2020)
- [Emergence of polarized ideological opinions in multidimensional topic spaces](https://mlsanity.com/paper/2007.00601/) (2020)
- [An agent-based model of multi-dimensional opinion dynamics and opinion alignment](https://mlsanity.com/paper/2003.05929/) (2020)
- [The impact of random actions on opinion dynamics](https://mlsanity.com/paper/1704.01660/) (2017)

## Built on by (most cited first, 5 of 5)

- [UniGO: A Unified Graph Neural Network for Modeling Opinion Dynamics on Graphs](https://mlsanity.com/paper/2502.11519/) (2025)
- [A Survey of Physics-Informed AI for Complex Urban Systems](https://mlsanity.com/paper/2506.13777/) (2025)
- [Towards Opinion Shaping: A Deep Reinforcement Learning Approach in Bot-User Interactions](https://mlsanity.com/paper/2409.11426/) (2024)
- [Digital Persuasion: Understanding the Impact of Online Influencers on Public Opinion](https://mlsanity.com/paper/2609.16062/) (2026)
- [H-NeiFi: Non-Invasive and Consensus-Efficient Multi-Agent Opinion Guidance](https://mlsanity.com/paper/2507.13370/) (2025)

## Abstract (arXiv)

Opinion formation and propagation are crucial phenomena in social networks and have been extensively studied across several disciplines. Traditionally, theoretical models of opinion dynamics have been proposed to describe the interactions between individuals (i.e., social interaction) and their impact on the evolution of collective opinions. Although these models can incorporate sociological and psychological knowledge on the mechanisms of social interaction, they demand extensive calibration with real data to make reliable predictions, requiring much time and effort. Recently, the widespread use of social media platforms provides new paradigms to learn deep learning models from a large volume of social media data. However, these methods ignore any scientific knowledge about the mechanism of social interaction. In this work, we present the first hybrid method called Sociologically-Informed Neural Network (SINN), which integrates theoretical models and social media data by transporting the concepts of physics-informed neural networks (PINNs) from natural science (i.e., physics) into social science (i.e., sociology and social psychology). In particular, we recast theoretical models as ordinary differential equations (ODEs). Then we train a neural network that simultaneously approximates the data and conforms to the ODEs that represent the social scientific knowledge. In addition, we extend PINNs by integrating matrix factorization and a language model to incorporate rich side information (e.g., user profiles) and structural knowledge (e.g., cluster structure of the social interaction network). Moreover, we develop an end-to-end training procedure for SINN, which involves Gumbel-Softmax approximation to include stochastic mechanisms of social interaction. Extensive experiments on real-world and synthetic datasets show SINN outperforms six baseline methods in predicting opinion dynamics.