Short text classification with machine learning in the social sciences: The case of climate change on Twitter

74. Bergstra J, Bardenet R, Bengio Y, Ke´ gl B. Algorithms for hyper-parameter optimization. Advances in Neural Information Processing Systems. 2011; 24. 75. Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. In: Pro- ceedings of the 13th International Conference on Artificial Intelligence and Statistics; 2010. p. 249–256. 76. Jang B, Kim I, Kim JW. Word2vec convolutional neural networks for classification of news articles and tweets. PLoS ONE. 2019; 14(8):e0220976. https://doi.org/10.1371/journal.pone.0220976 PMID: 31437181 77. Nandanwar AK, Choudhary J. Semantic features with contextual knowledge-based web page categori- zation using the GloVe model and stacked BiLSTM. Symmetry. 2021; 13(10):1772. https://doi.org/10. 3390/sym13101772 78. Google. Colaboratory; 2023. https://research.google.com/colaboratory, verified Apr. 2023. 79. He H, Garcia EA. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engi- neering. 2009; 21(9):1263–1284. https://doi.org/10.1109/TKDE.2008.239 80. Denny MJ, Spirling A. Text preprocessing for unsupervised learning: Why it matters, when it misleads, and what to do about it. Political Analysis. 2018; 26(2):168–189. https://doi.org/10.1017/pan.2017.44 81. Fawcett T. ROC graphs: Notes and practical considerations for researchers. Machine Learning. 2004; 31(1):1–38. 82. Davis J, Goadrich M. The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine learning; 2006. p. 233–240.

83. Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure. 1975; 405(2):442–451. https://doi.org/10.1016/ 0005-2795(75)90109-9 PMID: 1180967 84. Lipton ZC, Elkan C, Naryanaswamy B. Optimal thresholding of classifiers to maximize F1 measure. In: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases; 2014. p. 225–239. 85. Kaggle. Twitter Climate Change Sentiment Dataset; 2019. https://www.kaggle.com/datasets/edqian/ twitter-climate-change-sentiment-dataset, accessed Nov. 2021. 86. Hartmann J, Huppertz J, Schamp C, Heitmann M. Comparing automated text classification methods. International Journal of Research in Marketing. 2019; 36(1):20–38. https://doi.org/10.1016/j.ijresmar. 2018.09.009 87. McNemar Q. Note on the sampling error of the difference between correlated proportions or percent- ages. Psychometrika. 1947; 12(2):153–157. https://doi.org/10.1007/BF02295996 PMID: 20254758 88. Miller B, Linder F, Mebane WR. Active learning approaches for labeling text: Review and assessment of the performance of active learning approaches. Political Analysis. 2020; 28(4):532–551. https://doi.org/ 10.1017/pan.2020.4 89. Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. In: Proceedings of International Conference on Learning Representations; 2013. p. 1–12. 90. Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for lan-

guage understanding. In: Proceedings of the Conference of the North American Chapter of the Associa- tion for Computational Linguistics: Human Language Technologies; 2019. p. 4171–4186. PLOS ONE Short text classification with machine learning in the social sciences PLOS ONE | https://doi.org/10.1371/journal.pone.0290762 September 29, 2023 26 / 26