This paper presents the English-language submissions of The Counterfactuals team for the three subtasks of Task 9 at SemEval 2026. The task aims to detect multicultural online polarization, how it is expressed, and in what contexts. The task provides a high-quality annotation dataset of posts that follows a three-level schema: polarized or not (subtask 1), polarization type classification (subtask 2), and manifestation identification (subtask 3). I construct a pointwise mutual information-based lexicon that identifies highly-correlated words with the polarized class as labeled in subtask 1. Using this lexicon, I implement a large language model data augmentation technique. I then use the preprocessed datasets to finetune a BERT model (BERTweet) for each subtask. My highest performing models placed 48th out of 60, 35th out of 36, and 17th out of 24 on subtasks 1, 2, and 3 respectively. All code is available on GitHub.