The spread of fake news on online platforms has long been a pressing concern. Considering this, extensive efforts have been made to develop fake news detectors. However, a major drawback of these models is their relatively low performance—lagging by more than 20%—in identifying *fake* news compared to *real* news, making them less suitable for practical deployment. This gap is likely due to an imbalance in the dataset and the model’s inadequate understanding of data distribution on the targeted platform. In this work, we focus on improving the model’s effectiveness in detecting *fake* news. To achieve this, we **first** adopt an LLM to **generate** fake news in three different styles, which are later incorporated into the training set to augment the representation of fake news. **Then**, we apply Reinforcement Learning to dynamically **sample** fake news, allowing the model to learn the optimal real-to-fake news ratio for training an effective fake news detector on the targeted platform. This approach allows our model to perform effectively even with a limited amount of annotated news data and consistently improve detection accuracy across different platforms. Experimental results demonstrate that our approach achieves state-of-the-art performance on two benchmark datasets, improving *fake* news detection performance by 24.02% and 11.06% respectively.

Nowadays, memes have become quite common in day-to-day communications on social media platforms. They appear to be amusing, evoking and attractive to audiences. However, some memes containing malicious contents can be harmful to the targeted group and arouse public anger in the long run. In this paper, we study misogynous meme detection, a shared task in SemEval 2022 - Multimedia Automatic Misogyny Identification (MAMI). The challenge of misogynous meme detection is to co-represent multi-modal features. To tackle with this challenge, we propose a Multi-modal Multi-task Variational AutoEncoder (MMVAE) to learn an effective co-representation of visual and textual features in the latent space, and determine if the meme contains misogynous information and identify its fine-grained categories. Our model achieves 0.723 on sub-task A and 0.634 on sub-task B in terms of F₁ scores. We carry out comprehensive experiments on our model’s architecture and show that our approach significantly outperforms several strong uni-modal and multi-modal approaches. Our code is released on github.

Intellectual Property (IP) in the form of issued patents is a critical and very desirable element of innovation in high-tech. In this position paper, we explore the possibility of automating the legal task of Claim Construction in patent applications via Natural Language Processing (NLP) and Machine Learning (ML). To this end, we first create a large dataset known as CMUmine™and then demonstrate that, using NLP and ML techniques the Claim Construction in patent applications, a crucial legal task currently performed by IP attorneys, can be automated. To the best of our knowledge, this is the first public patent application dataset. Our results look very promising in automating the patent application process.