A Method of Analyzing the Influence of Content on Collective Sensemaking in Social Media

The formation of meaning on social media is determined by how users interact with messages such as sharing, reacting, or commenting. In this study, the dissemination of information on social media was investigated using epidemiological modeling, specifically the Susceptible-Infectious-Recovered (SIR) model. The proposed method was applied to assess message on health sur-veillance issues and could help understand how users behave in contact with mes-sages. The preliminaries results have shown the potential of the proposal to quan-tify the influence of messages on collective (users) sensemaking on social media.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-10-02
DOI
https://doi.org/10.5281/zenodo.16755398
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
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article

A Method of Analyzing the Influence of Content on Collective Sensemaking in Social Media

Zenodo (CERN European Organization for Nuclear Research)
Misinformation and Its Impacts
article

A Method of Analyzing the Influence of Content on Collective Sensemaking in Social Media

article en

Abstract

The formation of meaning on social media is determined by how users interact with messages such as sharing, reacting, or commenting. In this study, the dissemination of information on social media was investigated using epidemiological modeling, specifically the Susceptible-Infectious-Recovered (SIR) model. The proposed method was applied to assess message on health sur-veillance issues and could help understand how users behave in contact with mes-sages. The preliminaries results have shown the potential of the proposal to quan-tify the influence of messages on collective (users) sensemaking on social media.

Zenodo (CERN European Organization for Nuclear Research)
Universidade Federal do Rio de Janeiro (BR), Universidade Federal Rural do Rio de Janeiro (BR), Sulzer (United Kingdom) (GB)
Openalex Percentile: Top 40%
Misinformation and Its Impacts
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