Analyzing Twitter networks using graph embeddings: an application to the British case
Tóm tắt
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.
Từ khóa
Tài liệu tham khảo
Barberá, P. (2015). Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data. Political Analysis, 23(1), 76–91.
Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological Science, 26(10), 1531–1542.
Cai, H., Zheng, V. W., & Chang, K.C.-C. (2018). A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Transactions on Knowledge and Data Engineering, 30(9), 1616–1637.
Goyal, P., & Ferrara, E. (2018). Graph embedding techniques, applications, and performance: A survey. Knowledge-Based Systems, 151, 78–94.
Grover, A. & Leskovec, J. (2016). node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM pp. 855–864.
Hamdi, T., Slimi, H., Bounhas, I., & Slimani, Y. (2020). A hybrid approach for fake news detection in twitter based on user features and graph embedding. In International Conference on Distributed Computing and Internet Technology. Springer pp. 266–280.
Harris, Z. S. (1954). Distributional structure. Word, 10(2–3), 146–162.
Hobolt, S. B., et al. (2018). Brexit and the 2017 UK general election. Journal of Common Market Studies, 56(S1), 39–50.
Laver, M. (2014). Measuring policy positions in political space. Annual Review of Political Science, 17, 207–223.
Masood, M.A., & Abbasi, R.A. (2021). Using graph embedding and machine learning to identify rebels on Twitter. Journal of Informetrics, 15(1), 101–121. https://doi.org/10.1016/j.joi.2020.101121.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. pp. 3111–3119.
Řehůřek, R., & Sojka, P. (2010). Software framework for topic modelling with large corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. Valletta, Malta: ELRA pp. 45–50. http://is.muni.cz/publication/884893/en.
Rheault, L., & Cochrane, C. (2020). Word embeddings for the analysis of ideological placement in parliamentary corpora. Political Analysis, 28(1), 112–133.
Rodman, E. (2020). A timely intervention: Tracking the changing meanings of political concepts with word vectors. Political Analysis, 28(1), 87–111.
Spirling, A., & Rodriguez, P. (forthcoming). Word embeddings: What works, what doesn’t, and how to tell the difference for applied research.” Journal of Politics forthcoming.
van der Maaten, Laurens, & Hinton, Geoffrey. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(Nov), 2579–2605.