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arXiv AI By S M Rafiuddin, Atriya Sen

C$^{3}$T: Counterfactual Causal Reasoning for Sentiment Shifts in Social-Media Conversation Trees

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The paper introduces C$^{3}$T, a Counterfactual Causal Conversation Transformer that models sentiment shifts in social‑media conversation trees. It treats discourse moves such as denial, evidence, and toxicity as interventions, predicts node sentiment and shifts, and attributes sentiment changes to specific ancestor messages. The authors also present CaSiRe, a causal sentiment reasoning layer that enriches rumor conversation datasets with sentiment, shift, intervention, and causal‑source annotations, and demonstrate that C$^{3}$T outperforms baseline models in robustness and interpretability.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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