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Quoting is not Citing: Disentangling Affiliation and Interaction on Twitter

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Part of the Studies in Computational Intelligence book series (SCI,volume 1015)

Abstract

Interaction networks are generally much less homophilic than affiliation networks, accommodating for many more cross-cutting links. By statistically assigning a political valence to users from their follower ties, and by further contrasting interaction and affiliation on Twitter (quotes and retweets) within specific discursive events, namely quote trees, we describe a variety of cross-cutting patterns which significantly nuance the traditional “echo chamber” narrative.

Keywords

  • Discussion trees
  • Cross-cutting interaction
  • Twitter

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Acknowledgments

We are grateful to Telmo Menezes and Katharina Tittel for contributing to define the user perimeter and subsequently collect Twitter data. This work was supported by the “Socsemics” Consolidator grant from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 772743)

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Correspondence to Camille Roth .

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Roth, C., St-Onge, J., Herms, K. (2022). Quoting is not Citing: Disentangling Affiliation and Interaction on Twitter. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1015. Springer, Cham. https://doi.org/10.1007/978-3-030-93409-5_58

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  • DOI: https://doi.org/10.1007/978-3-030-93409-5_58

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