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Cited by 88Social Media and PoliticsMisinformation and Its ImpactsComplex Network Analysis Techniques

Reconstruction of the socio-semantic dynamics of political activist Twitter networks—Method and application to the 2017 French presidential election

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Gaumont, Noé, Panahi, Maziyar, Chavalarias, David·2018·PLOS ONE
DOI 10.1371/journal.pone.0201879PMC PMC6145593
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Abstract

BACKGROUND: Digital spaces, and in particular social networking sites, are becoming increasingly present and influential in the functioning of our democracies. In this paper, we propose an integrated methodology for the data collection, the reconstruction, the analysis and the visualization of the development of a country's political landscape from Twitter data. METHOD: The proposed method relies solely on the interactions between Twitter accounts and is independent of the characteristics of the shared contents such as the language of the tweets. We validate our methodology on a case study on the 2017 French presidential election (60 million Twitter exchanges between more than 2.4 million users) via two independent methods: the comparison between our automated political categorization and a human categorization based on the evaluation of a sample of 5000 profiles descriptions; the correspondence between the reconfigurations detected in the reconstructed political landscape and key political events reported in the media. This latter validation demonstrated the ability of our approach to accurately reflect the reconfigurations at play in the off-line political scene. RESULTS: We built on this reconstruction to give insights into the opinion dynamics and the reconfigurations of political communities at play during a presidential election. First, we propose a quantitative description and analysis of the political engagement of members of political communities. Second, we analyze the impact of political communities on information diffusion and in particular on their role in the fake news phenomena. We measure a differential echo chamber effect on the different types of political news (fake news, debunks, standard news) caused by the community structure and emphasize the importance of addressing the meso-structures of political networks in understanding the fake news phenomena. CONCLUSIONS: Giving access to an intermediate level, between sociological surveys in the field and large statistical studies (such as those conducted by national or international organizations) we demonstrate that social networks data make it possible to qualify and quantify the activity of political communities in a multi-polar political environment; as well as their temporal evolution and reconfiguration, their structure, their alliance strategies and their semantic particularities during a presidential campaign through the analysis of their digital traces. We conclude this paper with a comment on the political and ethical implications of the use of social networks data in politics. We stress the importance of developing social macroscopes that will enable citizens to better understand how they collectively make society and propose as example the "Politoscope", a macroscope that delivers some of our results in an interactive way.

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  • Machine learning-based bot detection offers a way to mitigate this issue.

    “Machine learning-based bot detection, as proposed by Rusche [110], and implemented by Choi et al. [100] and Gaumont et al. [120], offers a way to mitigate this issue.”

    From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [120]

    The Abstract (Method) describes automated categorization of Twitter accounts by political affiliation, not bot detection. The Results (Propagation of political information) discuss strategies for mitigating fake news, but do not propose or implement machine-learning-based bot detection. Searches for bot detection, automated accounts, and bots found no passage supporting the attributed claim. (LLM, conf=0.76)

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Cited by

Sorted by citations

Authors
Gaumont, N., Panahi, M., & Chavalarias, D.
Affiliations
Centre National de la Recherche Scientifique (FR)
Corresponding author
Chavalarias, David
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2018-09-19
Volume
13
Issue
9
Pages
e0201879
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0201879
PMC
PMC6145593
OpenAlex
W2747327853
Cited by
88
References
6
Citations by year
2026: 4 · 2025: 12 · 2024: 10 · 2023: 7 · 2022: 10 · 2021: 16 · 2020: 13 · 2019: 14 · 2018: 2
FWCI
22.95
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, cc-by-nc)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Science en Poche (Programme Emergence(s))
  • Agence Nationale de la Recherche (ANR-16-CE38-0002-03)
Topics
  • Social Sciences › Communication › Social Media and Politics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords
2017 French presidential election, political participation, fake news, echo chambers, information dissemination, opinion dynamics, alliance strategy
MeSH terms
Political Activism, Communications Media, Data Collection, France, Government, Humans, Leadership, Politics, Semantics, Social Networking, Social Media

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