Fine tuned sentiment stance classifier
A longitudinal study of how English-language Reddit discussed China between January 2020 and December 2025. The corpus is roughly 460,000 submissions and 4.66 million comments drawn from ten subreddits and collected through Arctic Shift, alongside a smaller YouTube component.
The subreddits are stratified into three groups so that changes in one can be read against the others: topic-centric communities where China is the subject, informational communities where it appears as news, and general-interest communities where it surfaces incidentally.
The core method is supervised fine-tuning of ModernBERT to classify both sentiment and stance. VADER and BERTopic serve as baselines rather than as the primary approach, which lets the fine-tuned model be measured against the lexicon-based and unsupervised alternatives most often used for this kind of corpus. Mann-Kendall tests identify monotonic trends and PELT changepoint detection locates structural breaks, which together separate slow drift from event-driven movement.
Three questions run through it. Did sentiment actually change over the period. Do any changes reflect a shift in who is posting rather than a shift in opinion. And are the changes that remain event-driven or long-term. The framing is distant reading supported by close reading, so quantitative results are read back against the posts themselves rather than presented alone.
Python · ModernBERT · BERTopic (all-MiniLM-L6-v2, UMAP, HDBSCAN, c-TF-IDF) · VADER · Mann-Kendall · PELT · Arctic Shift