MONTROSE : LLM -driven M onte C arlo Tree Search Self-Refinement for Cross-Domain Rumor Detection (2025)

Abstract: With the emergence of new topics on social media as sources of rumor dissemination, addressing the distribution shifts between source and target domains remains a crucial task in cross-domain rumor detection. Existing feature alignment methods, which aim to reduce the discrepancies between domains, are often susceptible to task interference during training. Additionally, data distribution alignment methods, which rely on existing data to synthesize new training samples, inherently introduce noise. To deal with these challenges, a new cross-domain rumor detection method, MONTROSE, is proposed. It combines LLM-driven Monte Carlo Tree Search (MCTS) data synthesis to generate high-quality synthetic data for the target domain and a domain-sharpness-aware (DSAM) self-refinement approach to train rumor detection models with these synthetic data effectively. Experiments demonstrate the superior performance of MONTROSE in cross-domain rumor detection.

## introduction with the advent of machine learning and deep learning techniques, significant progress has been made in rumor detection. these methods effectively mine semantic information from both text content @xcite @xcite and propagation structures @xcite @xcite , achieving better rumor detection performance. however, detecting rumors of emerging topics remains a challenge for existing methods @xcite . traditional methods detect rumors under in-domain conditions. nevertheless, in the case of detecting rumors about emerging topics, there is a distribution shift between the emerging topics and historical topics. as shown in figure 1 , there are differences in word usage and writing style between the two domains. if this distributional discrepancy is ignored and the model trained on the source domain is directly applied to the target domain, their performance undergoes a significant diminishment. to deal with the distribution shift issue, methods can be divided into two main categories: feature space alignment @xcite @xcite @xcite and data distribution alignment @xcite @xcite . the former methods employ techniques, such as domain adversarial learning (dal) @xcite and maximum mean discrepancy (mmd) @xcite , to align the feature space distributions across various topics. however, these methods simultaneously optimize two conflicting objectives, aligning feature space and conducting supervised training, leading to task interference and suboptimal performance @xcite . data distribution alignment aims to create training datasets that closely resemble the target data distribution, enabling model retraining and adaptation through techniques such as pseudolabeling @xcite and data selection @xcite . yet, pseudo-labeling suffers from label noise, and data selection introduces input bias, particularly problematic in complex tasks like rumor detection, where contextual understanding is critical @xcite . in this paper, we propose a new approach for cross-domain rumor detection that combines feature space alignment and data distribution alignment, termed montrose (llm-driven monte carlo tree search domain-sharpnessaware minimization self-refinement). mon-trose is a two-stage framework that first synthesizes training data with an llm-driven monte carlo tree search (mcts) module, and then retrains the model on these synthesized data via a domain-sharpness-aware minimization (dsam) self-refinement method: llm-driven mcts. although llms excel at generating fluent text, synthesizing rumors remains challenging due to their complex social and psychological underpinnings, which result in intricate contextual and propagation dynamics @xcite . to address this, we employ mcts to model the branching nature of rumor dissemination. by identifying key nodes, mcts guides the llm generation process, enabling rapid convergence on domain-relevant propagation patterns. this approach ensures that the synthesized data is not only highly readable but also authentically captures the structural characteristics of rumor propagation, thereby achieving the goal of synthesizing domain-specific data. ## problem formulation cross-domain rumor detection can be denoted as the unsupervised domain adaptation task in text classification. historic data is the source domain d s , composed of a feature space x s , a label space y s , and an associated probabil- ## experiments in this section, the dataset, baselines, and implementation details are introduced briefly at first. following that, experimental results are illustrated. ## conclusion in this paper, we introduced montrose to address the challenges of cross-domain rumor detection. llm-driven mcts data synthesis is integrated to generate high-quality synthetic data tailored to the target domain's characteristics. a dsam self-refinement method is further utilized to perturb model parameters with domain classification gradients, aligning the feature space and enhancing the training contribution of highconfidence samples. experimental results demonstrate that montrose outperforms existing methods in detecting rumors in emerging topics, making it a robust solution for real-time and cross-domain rumor detection.