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Analyzing Twitter networks using graph embeddings: an application to the British case

Author

Listed:
  • Miguel Won

    (INESC-RD)

  • Jorge M. Fernandes

    (Institute of Social Sciences, University of Lisbon)

Abstract

Embeddings have gained traction in the social sciences in recent years. Existing work focuses on text-as-data to estimate word embeddings. In this paper, we turn to graph embeddings as a tool whose use has been overlooked in the analysis of social networks. Graph embeddings have two primary uses. First, to encode users and their interactions onto a single vector. Second, graph embeddings can be used as inputs for machine-learning classifiers. In this paper, we use the British political Twitter to illustrate both uses of graph embeddings. We encode users’ partisanship. Furthermore, we use an SVM and a NN to estimate the partisan proximity of Twitter users. Results suggest that graph embeddings yield high precision predictions.

Suggested Citation

  • Miguel Won & Jorge M. Fernandes, 2022. "Analyzing Twitter networks using graph embeddings: an application to the British case," Journal of Computational Social Science, Springer, vol. 5(1), pages 253-263, May.
    • Handle: RePEc:spr:jcsosc:v:5:y:2022:i:1:d:10.1007_s42001-021-00128-6
    DOI: 10.1007/s42001-021-00128-6
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    References listed on IDEAS

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    1. Sara B. Hobolt, 2018. "Brexit and the 2017 UK General Election," Journal of Common Market Studies, Wiley Blackwell, vol. 56(S1), pages 39-50, September.
    2. Masood, Muhammad Ali & Abbasi, Rabeeh Ayaz, 2021. "Using graph embedding and machine learning to identify rebels on twitter," Journal of Informetrics, Elsevier, vol. 15(1).
    3. Rheault, Ludovic & Cochrane, Christopher, 2020. "Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora," Political Analysis, Cambridge University Press, vol. 28(1), pages 112-133, January.
    4. Rodman, Emma, 2020. "A Timely Intervention: Tracking the Changing Meanings of Political Concepts with Word Vectors," Political Analysis, Cambridge University Press, vol. 28(1), pages 87-111, January.
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