This paper presents the Georgetown University NLP (GUNLP) system developed for SemEval 2026 Task 10: Psycholinguistic Conspiracy Marker Extraction and Detection, addressing the classification of conspiratorial beliefs in Reddit posts (Subtask 2). Our approach leverages COVID-Twitter-BERT v2 (CT-BERT-v2) within a multi-task learning framework that jointly optimizes conspiracy classification and emotion label prediction through a dual-head architecture. To address data scarcity, we enrich the training set using paraphrasing-based data augmentation and GPT-5-generated chain-of-thought emotion annotations, effectively doubling the training corpus to approximately 8,600 examples. We evaluate two input configurations: text only and text concatenated with emotion labels. The emotion-aware configuration achieves the strongest performance with an F1 score of 0.87 on the official development set, outperforming the text-only baseline by five F1 points and demonstrating the value of paraphrased samples and affective auxiliary supervision for conspiracy detection in social media text.