More concretely, our probing method produces a score s for a language model LM, a ﬁll-in-the-blank prompt, and target
word w. We write this as s = LM(prompt,w). For the BERT baseline (no adaption to media content), this score is simply the
probability of w in the blank position. For instance, for the prompt “Requiring most businesses other than grocery stores and
pharmacies to close is [BLANK] in order to address the coronavirus outbreak”, the probability of ‘necessary’ is s = 0.188. For
a media diet model, we divide its score by the baseline, non-adapted model score. This is: s = MediaDietBERT (prompt,w)
. This
normalization means the score reﬂects new information contained in the media diet dataset, but relative to existing knowledge
and information in the base model. Finally, we introduce a synonym-grouping scoring method, in which probabilities are

BERT (prompt,w)

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Language models trained on media diets can predict public opinion