# Comparing community-based interventions versus population-wide response in information diffusion on social media platforms

**Authors:** Chathura Jayalath, Xiaoxia Champon, William Rand, Jasser Jasser, Ozlem Garibay, Ivan Garibay

PMC · DOI: 10.1017/dap.2025.10053 · Data & policy · 2026-02-07

## TL;DR

This paper compares how information spreads through social media communities versus the general population, using Twitter data to analyze differences in diffusion strategies.

## Contribution

The study introduces a comparative framework for evaluating community-based versus population-wide interventions in information diffusion on social media.

## Key findings

- Community-based interventions show distinct engagement patterns compared to population-wide approaches.
- Functional data analysis reveals disparities in how communities promote specific narratives.
- Temporal metrics highlight differences in information amplification across social media groups.

## Abstract

The dynamics of information diffusion on social media platforms vary significantly between individual communities and the broader population. This study explores and compares the differences between community-based interventions and population-wide approaches in adjusting the spread of information. We first examine the temporal dynamics of social media groups, assessing their behavior through metrics such as time-dependent posts and retweets. Using functional data analysis, we investigate Twitter activities related to incidents such as the Skripal/Novichok case. We present three ways to quantify disparities between communities and uncover the strategies used by each group to promote specific narratives. We then compare the impact of targeted, community-based interventions with that of broader, population-wide responses in shaping the diffusion of information. Through this analysis, we identify key differences in how communities engage with and amplify information, revealing distinct patterns in the diffusion process. Our findings provide a comparative framework for understanding the relative consequences of different intervention strategies, offering insights into how targeted and broad approaches influence public discourse across social media platforms.

## Full-text entities

- **Diseases:** COVID19 (MESH:D000086382), poisoning (MESH:D011041)
- **Chemicals:** FDA (-)

## Full text

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## Figures

11 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12880767/full.md

## References

33 references — full list in the complete paper: https://tomesphere.com/paper/PMC12880767/full.md

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Source: https://tomesphere.com/paper/PMC12880767