Table 2 Classification performance on the withheld tweets of politicians and the Twitter accounts of politicians. The subscript “\(\mathrm{no\ attn}\)” signifies that we use the mean value of \(\{\mathbf{z}^{(p)}\}\) directly (i.e., without applying an attention mechanism). Skip-Gram (i.e., the Baseline PEM model) and GloVe use a pretrained embedding with the same MLP binary classifier as in our discriminator. (To train this classifier, we use a training set that includes 80% of the politicians’ tweets.) In each entry, we show the accuracy followed by the \(F_{1}\) score. We show the best results for each column in bold. The names of our models are also in bold

From: Detecting political biases of named entities and hashtags on Twitter