Uncovering Like-minded Political Communities on Twitter

O. Fraisier; G. Cabanac; Y. Pitarch; R. Besançon; M. Boughanem

Uncovering Like-minded Political Communities on Twitter

O. Fraisier ^{1, 2} G. Cabanac ² Y. Pitarch ² R. Besançon ³ M. Boughanem ²

1 CEA Tech Languedoc-Roussillon Midi-Pyrénées

2 IRIT - Institut de recherche en informatique de Toulouse

3 DIASI - Département Intelligence Ambiante et Systèmes Interactifs

Abstract : Stance detection systems often integrate social clues in their algorithms. While the influence of social groups on stance is known, there is no evaluation of how well state-of-the-art community detection algorithms perform in terms of detecting like-minded communities, i.e. communities that share the same stance on a given subject. We used Twitter's social interactions to compare the results of community detection algorithms on datasets on the Scottish Independence Referendum and US Midterm Elections. Our results show that algorithms relying on information diffusion perform better for this task and confirm previous observations about retweets being better vectors of stance than mentions.

Keywords : Benchmarking Information retrieval Population dynamics Signal detection Community detection algorithms Detection system Information diffusion Social groups Social interactions Social media State of the art Social networking (online)

Document type :

Conference papers

Domain :

Engineering Sciences [physics]

Complete list of metadatas

https://hal-cea.archives-ouvertes.fr/cea-01831838
Contributor : Léna Le Roy <>
Submitted on : Friday, July 6, 2018 - 11:52:19 AM
Last modification on : Thursday, June 27, 2019 - 4:27:53 PM

Uncovering Like-minded Political Communities on Twitter

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Uncovering Like-minded Political Communities on Twitter

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