Main Data and Code

Important Notice: Ethical Use OnlyThis repository provides code and datasets for academic research on misinformation.Please note that the datasets include rumor-related texts. These materials are supplied solely for scholarly analysis and research aimed at understanding and combating misinformation.Prohibited UseDo not use this repository, including its code or data, to create or spread false information in any real-world context.Any misuse of these resources for malicious purposes is strictly forbidden.DisclaimerThe authors bear no responsibility for any unethical or unlawful use of the provided resources.By accessing or using this repository, you acknowledge and agree to comply with these ethical guidelines.Project StructureThe project is organized into three main directories, each corresponding to a major section of the paper's experiments:main_data_and_code/├── rumor_generation/├── rumor_detection/└── rumor_debunking/How to Get StartedPrerequisitesTo successfully run the code and reproduce the results, you will need to:Obtain and configure your own API key for the large language models (LLMs) used in the experiments. Please replace the placeholder API key in the code with your own.For the rumor detection experiments, download the public datasets (Twitter15, Twitter16, FakeNewsNet) from their respective sources. The pre-process scripts in the rumor detection folder must be run first to prepare the public datasets.Please note that many scripts are provided as examples using the Twitter15 dataset. To run experiments on other datasets like Twitter16 or FakeNewsNet, you will need to modify these scripts or create copies and update the corresponding file paths.Detailed Directory Breakdown1. rumor_generation/This directory contains all the code and data related to the rumor generation experiments.rumor_generation_zeroshot.py: Code for the zero-shot rumor generation experiment.rumor_generation_fewshot.py: Code for the few-shot rumor generation experiment.rumor_generation_cot.py: Code for the chain-of-thought (CoT) rumor generation experiment.token_distribution.py: Script to analyze token distribution in the generated text.label_rumors.py：Script to label LLM-generated texts based on whether they contain rumor-related content.extract_reasons.py: Script to extract reasons for rumor generation and rejection.visualization.py: Utility script for generating figures.LDA.py: Code for performing LDA topic modeling on the generated data.rumor_generation_responses.json: The complete output dataset from the rumor generation experiments.generation_reasons_extracted.json: The extracted reasons for generated rumors.rejection_reasons_extracted.json: The extracted reasons for rejected rumor generation requests.2. rumor_detection/This directory contains the code and data used for the rumor detection experiments.nonreasoning_zeroshot_twitter15.py: Code for the non-reasoning, zero-shot detection on the Twitter15 dataset. To run on Twitter16 or FakeNewsNet, update the file paths within the script. Similar experiment scripts below follow the same principle and are not described repeatedly.nonreasoning_fewshot_twitter15.py: Code for the non-reasoning, few-shot detection on the Twitter15 dataset.nonreasoning_cot_twitter15.py: Code for the non-reasoning, CoT detection on the Twitter15 dataset.reasoning_zeroshot_twitter15.py: Code for the Reasoning LLMs, zero-shot detection on the Twitter15 dataset.reasoning_fewshot_twitter15.py: Code for the Reasoning LLMs, few-shot detection on the Twitter15 dataset.reasoning_cot_twitter15.py: Code for the Reasoning LLMs, CoT detection on the Twitter15 dataset.traditional_model.py: Code for the traditional models used as baselines.preprocess_twitter15_and_twitter16.py: Script for preprocessing the Twitter15 and Twitter16 datasets.preprocess_fakenews.py: Script for preprocessing the FakeNewsNet dataset.generate_summary_table.py: Calculates all classification metrics and generates the final summary table for the rumor detection experiments.select_few_shot_example_15.py: Script to pre-select few-shot examples, using the Twitter15 dataset as an example. To generate examples for Twitter16 or FakeNewsNet, update the file paths within the script.twitter15_few_shot_examples.json: Pre-selected few-shot examples for the Twitter15 dataset.twitter16_few_shot_examples.json: Pre-selected few-shot examples for the Twitter16 dataset.fakenewsnet_few_shot_examples.json: Pre-selected few-shot examples for the FakeNewsNet dataset.twitter15_llm_results.json: LLM prediction results on the Twitter15 dataset.twitter16_llm_results.json: LLM prediction results on the Twitter16 dataset.fakenewsnet_llm_results.json: LLM prediction results on the FakeNewsNet dataset.visualization.py: Utility script for generating figures.3. rumor_debunking/This directory contains all the code and data for the rumor debunking experiments.analyze_sentiment.py: Script for analyzing the sentiment of the debunking texts.calculate_readability.py: Script for calculating the readability score of the debunking texts.plot_readability.py: Utility script for generating figures related to readability.fact_checking_with_nli.py: Code for the NLI-based fact-checking experiment.debunking_results.json: The dataset containing the debunking results for this experimental section.debunking_results_with_readability.json: The dataset containing the debunking results along with readability scores.sentiment_analysis/: This directory contains the result file from the sentiment analysis.debunking_results_with_sentiment.json: The dataset containing the debunking results along with sentiment analysis.Please contact the repository owner if you encounter any problems or have questions about the code or data.