Data Driven Modeling Social Media Influence using Differential Equations

TL;DR

This paper presents a data-driven approach to model opinion evolution on social media using differential equations, leveraging high-dimensional embeddings and real Twitter data to explain opinion changes influenced by social networks.

Contribution

It introduces a novel pipeline combining high-dimensional embeddings and ODEs to model personal opinion evolution driven by social influence, validated on COVID-19 Twitter data.

Findings

Model explains 99% of opinion variation based on influencers.

Social influence is the primary factor in opinion shifts.

Long-term self-evolution of opinions is limited.

Abstract

Individuals modify their opinions towards a topic based on their social interactions. Opinion evolution models conceptualize the change of opinion as a uni-dimensional continuum, and the effect of influence is built by the group size, the network structures, or the relations among opinions within the group. However, how to model the personal opinion evolution process under the effect of the online social influence as a function remains unclear. Here, we show that the uni-dimensional continuous user opinions can be represented by compressed high-dimensional word embeddings, and its evolution can be accurately modelled by an ordinary differential equation (ODE) that reflects the social network influencer interactions. Our three major contributions are: (1) introduce a data-driven pipeline representing the personal evolution of opinions with a time kernel, (2) based on previous psychology models, we model the opinion evolution process as a function of online social influence using an ordinary differential equation, and (3) applied Our opinion evolution model to the real-time Twitter data. We perform our analysis on 87 active users with corresponding influencers on the COVID-19 topic from 2020 to 2022. The regression results demonstrate that 99% of the variation in the quantified opinions can be explained by the way we model the connected opinions from their influencers. Our research on the COVID-19 topic and for the account analysed shows that social media users primarily shift their opinion based on influencers they follow (e.g., model explains for 99% variation) and self-evolution of opinion over a long time scale is limited.