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

A biological NN comprises numerous layers, each consisting of a set of neurons (see Fig 4 in the S1 Appendix). Each neuron consists of dendrites that receive input signal pulses, a cell body that performs a transformation of the incoming combination of pulses, and an axon. The axon carries the output pulse through its trunk (covered with isolating myelin sheaths) to its ramified terminal, which has a set of synapses that connect to the dendrites of the upstream neurons in the network. The functioning of biological NNs has been studied and formalized independently in [49, 50]. Each neuron in the network conducts a non-linear operation on the superposition of the signals received by its dendrites. These signals are weighted by the strength of the connection of its synapses of the upstream neurons. The process carried out by each neuron can therefore be modeled as a simple unit computing the weighted sum of its inputs x1, . . ., xM with weights wj,1, . . ., wj,M, adding a bias term bj and applying a non-linear transformation a(�), called an activation function on the weighted sum (see Fig 4a in the S1 Appendix). This unit, first pro- posed in [51], is referred to as an artificial neuron and constitutes the basic building block of every NN. Information travels through the NN from the input layer towards the output layer, passing through all neurons on its way. For binary classification tasks, the output layer consists of a single neuron that outputs the probability of the observation belonging to one of the classes.

Fig 6. Illustration of a deep-learning model. The classifier provides a complex non-linear function (a) that maps an input feature vector into a predicted label. The function is learned from the training set by adjusting the parameters of a set of internal units arranged in, e.g., a layered fully-connected structure (b). (a) Deep-learning model as a black box. (b) Fully-connected deep neural network. https://doi.org/10.1371/journal.pone.0290762.g006 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 12 / 26