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. 2024 Jun 21;19(6):e0304889. doi: 10.1371/journal.pone.0304889

Fig 4. Schematic diagram of the infection model training process.

Fig 4

Diagram describing the training process for the infection model, which predicts whether User A will retweet User B’s post. The core model is a gradient boosted classifier with three sets of input features (i) transformer embeddings of User B’s post (i) transformer embeddings extracted from both historical tweets User B has authored and historical tweets User A has retweeted from others (iii) user metadata—such as number of followers, number of followees, etc.–from both User A and User B. Once the infection model is trained, it can be deployed to estimate the likelihood of infection spread.