The widespread dissemination of online misinformation poses significant threats to the public interest, highlighting the urgent need for effective fake news detection. In the era of Large Language Models (LLMs), the rise of AI-generated fake news has intensified this issue, making misinformation more pervasive and harder to control. While fact-checking offers a promising solution by leveraging external knowledge, efficiently linking claims within news articles to relevant external facts remains a significant challenge. To address this, we propose a mis-information detection framework FCRV (Full-Context Retrieval and Verification) that constructs a "full-context" for news articles by integrating LLM-based claim extraction with Retrieval-Augmented Generation (RAG) for fact-checking. We implemented an LLM pipeline for human-like extraction of key claims from datasets, significantly improving extraction quality over traditional methods. Our retrieval workflow effectively detects fictitious entities prevalent in AI-generated news by identifying claims lacking a basis in reality. Experiments across multiple human-generated and AI-generated datasets demonstrate that verifying news using this "full-context" approach leads to more stable and robust fake news detection, enhancing scalability, accuracy, and the model’s ability to handle AI-generated content.

For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being. To this end, we harness the power of social media data, a rich source of public sentiment, to enhance the accuracy of stock market predictions. Diverging from conventional methods, we pioneer an approach that integrates sentiment analysis, macroeconomic indicators, search engine data, and historical prices within a multi-attention deep learning model, masterfully decoding the complex patterns inherent in the data. We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility.

Predicting stock market is vital for investors and policymakers, acting as a barometer of the economic health. We leverage social media data, a potent source of public sentiment, in tandem with macroeconomic indicators as government-compiled statistics, to refine stock market predictions. However, prior research using tweet data for stock market prediction faces three challenges. First, the quality of tweets varies widely. While many are filled with noise and irrelevant details, only a few genuinely mirror the actual market scenario. Second, solely focusing on the historical data of a particular stock without considering its sector can lead to oversight. Stocks within the same industry often exhibit correlated price behaviors. Lastly, simply forecasting the direction of price movement without assessing its magnitude is of limited value, as the extent of the rise or fall truly determines profitability. In this paper, diverging from the conventional methods, we pioneer an ECON (A Framework Leveraging Tweets, Macroeconomic Indicators, and Historical Prices to Predict Stock Movement and Volatility). The framework has following advantages: First, ECON has an adept tweets filter that efficiently extracts and decodes the vast array of tweet data. Second, ECON discerns multi-level relationships among stocks, sectors, and macroeconomic factors through a self-aware mechanism in semantic space. Third, ECON offers enhanced accuracy in predicting substantial stock price fluctuations by capitalizing on stock price movement. We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility.