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Two Sides of the Same Coin: How to Integrate Social Network Analysis and Topic Detection to Investigate Shared Contents and Communicative Interactions in Social Representations Cover

Two Sides of the Same Coin: How to Integrate Social Network Analysis and Topic Detection to Investigate Shared Contents and Communicative Interactions in Social Representations

Open Access
|Dec 2024

Figures & Tables

irsp-37-973-g1.png
Figure 1

Non-directed network (left) and directed network (right).

irsp-37-973-g2.png
Figure 2

Ideal example of three different group formations performing a bipartite network analysis: a) the orange group exists because its members (i.e., authors 3, 5, and 9) share the same representational contents (i.e., Topic 1); b) the green group exists because its members (Authors 7, 8, and 10) interact; c) the purple group exists because its members (Authors 1, 2, 4, and 6) both share the same representational contents (Topic 2 and 3) and interact with each other.

Note: Each colour represents a group based on modularity class. The dimension of the nodes’ labels is proportional to the in-degree.

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Figure 3

Dendrogram of the topics resulting from the Reinert method. English translation by the authors.

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Figure 4

Bipartite network analysis output of the example of application (cf. graph file in the supplementary material). The thickness of the arc is determined by their weight. The size of the node labels is proportional to the in-degree. Each community is represented by a different colour and is defined by the modularity class.

Table 1

List of profiles with the highest indegree in the network.

RANKINGIDPROFILEACTIVITYINDEGREE
1308@jairbolsonaroPresident of Brazil8.0
2314@jdoriajrGovernor of Sao Paulo7.0
3247@folhaNewspaper4.0
4311@JanainaDoBrasilSão Paulo State Representative4.0
590@BlogdoNoblatJournalist3.0
6257@GabrielaPrioliLawyer and Political Commentator3.0
7615@XicoGrazianoPolitician3.0
8260@Gavin07290309---3.0
940@AnaPaulaVoleiPolitical Commentator2.0
1061@ArthurWeintMinister of Education (Bolsonaro Government)2.0
irsp-37-973-g5.png
Figure 5

The figure shows the distribution of community sizes by modularity class, highlighting classes 0, 8, 10, 12, 15, and 22. These classes represent communities formed by nodes connected to topics 1 through 6, identified using the Reinert Method. Each of these nodes is linked to at least one of the six topics. Other modular groups, which are not related to these topics, only represent ‘user-user’ relationships.

irsp-37-973-g6.png
Figure 6

Multiple User-Topic and User-User connections.

Note: Zooms of Figure 4; labels A, B, C, D, and E – representing users – have been manually added for clarity of exposition.

DOI: https://doi.org/10.5334/irsp.973 | Journal eISSN: 2397-8570
Language: English
Submitted on: Jun 26, 2024
Accepted on: Nov 23, 2024
Published on: Dec 16, 2024
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2024 Valentina Rizzoli, Anderson da Silveira, Mirella De Falco, Mauro Sarrica, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.