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

There are several versions of the NB classifier (e.g., Gaussian, Bernoulli, multinomial). The operation of a Gaussian NB classifier on exemplary data is demonstrated in Fig 5. Studies have reported that a multinomial mixture model shows improved performance scores on several data collections, when compared to other commonly used versions of the NB approach [46]. Because of this, for our empirical analysis, we use a multinomial NB classifier with the smooth- ing hyperparameter tuned to achieve the best performance. Deep learning Deep learning (DL) is a prominent supervised ML framework for data-driven predictive modeling. Despite its promising classification capacities, DL continues to be a much less used approach to text classification in social science, when compared to its use in such fields as com- puter science. A DL classifier can be thought of as a black box: data go in, decisions come out (see Fig 6a). Inside the black box there are a large number of parameters that are learned dur- ing the training. In this way, the DL model approximates a predictor function that describes the observed data. Because of the way its operations resemble those of the human brain, the broader class of methods to which DL belongs is often referred to as artificial intelligence (AI). A central element of DL is the concept of an artificial neural network (NN) which describes the inner-workings of the DL black box. It consists of a layered structure of units conducting mathematical operations on their inputs and passing along the results (see Fig 6b). The useful-

ness of an NN lies in its ability to infer a function from observations. In this way, the DL model can approximate the predictor function that describes the observed data which is essen- tial for both classification and regression tasks. The idea of an NN was first proposed by Hebb [48] as an abstraction of a real biological network of neurons in the mammalian brain. Fig 5. Illustration of a Gaussian naïve Bayes classifier. The classifier fits two Gaussian distributions (depicted with contour plots) to the categorized labels in the training set. The decision boundary is determined by the point where the probability densities for the two categories take the same value. https://doi.org/10.1371/journal.pone.0290762.g005 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 11 / 26