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

Miguel Won () and Jorge M. Fernandes ()
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Miguel Won: INESC-RD
Jorge M. Fernandes: Institute of Social Sciences, University of Lisbon

Journal of Computational Social Science, 2022, vol. 5, issue 1, No 11, 253-263

Abstract: 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.

Keywords: Graph embeddings; Social networks; Social media; Twitter; Political map; UK (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s42001-021-00128-6

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